System

Abstract

An object of the system according to the embodiment is to generate a high-quality video without a specialized skill.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a video image of a user. The analysis unit analyzes the video image received by the reception unit. The generation unit generates a video based on the video image analyzed by the analysis unit. The provision unit provides the video generated by the 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] With conventional technology, it was difficult to produce high-quality video without specialized skills, and it was time-consuming and costly.

[0005] The system according to the embodiment aims to generate high-quality video without requiring specialized skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a video image of a user. The analysis unit analyzes the video image received by the reception unit. The generation unit generates a video based on the video image analyzed by the analysis unit. The provision unit provides the video generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate high-quality images without requiring specialized skills. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A video generation system according to an embodiment of the present invention generates high-quality videos based on a video image input by a user. This video generation system uses AI to generate high-quality videos based on the user's input image. This significantly reduces the time, cost, and effort required for video production, allowing users to focus on their ideas and plans. An application is also available that provides an interface specialized for AI-powered video production. This allows the video generation system to easily generate high-quality videos without requiring specialized skills. For example, in the advertising industry, high-quality video production is required in a short period of time. This system allows users to produce videos quickly and efficiently. Furthermore, even individual creators or small production teams can produce professional-quality videos at low cost. This significantly simplifies the video production process, providing an environment in which users can focus on their ideas and plans.

[0029] The video generation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's video image. The user's video image may include, but is not limited to, a still image, a video, or a 3D model. The reception unit may receive detailed information, such as a specific scene, character, or background, input by the user. The analysis unit analyzes the video image received by the reception unit. The analysis may be performed using, for example, but not limited to, image recognition technology or pattern recognition technology. The analysis unit identifies the scene configuration, character movement, background depiction, and the like. The generation unit generates a video based on the information identified by the analysis unit. The generation may be performed using, for example, but not limited to, rendering technology or animation generation technology. The generation unit generates a video based on the user's image. The provision unit provides the video generated by the generation unit to the user. The provision may be performed by, for example, but not limited to, streaming distribution or download provision. The provision unit allows the user to provide instructions for correction or addition as needed. As a result, the image generation system according to the embodiment can efficiently accept, analyze, generate, and provide the user's image.

[0030] The reception unit can receive detailed information about a specific scene, character, or background input by the user. Examples of specific scenes include, but are not limited to, a location, a time, and a situation. For example, the user can input an image such as "a scene of children playing on the beach at sunset" or "a night view of a futuristic city." Examples of characters include, but are not limited to, appearance, personality, and role. For example, the reception unit can allow the user to input detailed information about the character. Examples of backgrounds include, but are not limited to, scenery, buildings, and environments. For example, the reception unit can allow the user to input detailed information about the background. As a result, a more specific video image can be received by inputting detailed information by the user.

[0031] The analysis unit analyzes the information received by the reception unit and can identify the scene configuration, character movements, and background depiction. Scene configuration includes, for example, the order of scenes, important events, etc., but is not limited to these examples. The analysis unit can, for example, analyze the order of scenes and identify important events. Character movements include, for example, the type of movement, movement patterns, etc., but are not limited to these examples. The analysis unit can, for example, analyze the type of character movement and identify movement patterns. Background depiction includes, for example, the level of detail of the depiction, color, texture, etc., but is not limited to these examples. The analysis unit can, for example, analyze the level of detail of the background depiction and identify color and texture. Thus, by analyzing the information received by the reception unit, the scene configuration, character movements, background depiction, etc. can be identified.

[0032] The generation unit can generate video based on the information identified by the analysis unit. Video generation includes, but is not limited to, rendering technology and animation generation technology, for example. The generation unit can realistically reproduce the scene configuration, character movements, and background depiction using rendering technology, for example. The generation unit can smoothly express character movements using animation generation technology, for example. The generation unit can depict the color of the scene and the texture of the background in detail, for example. As a result, by generating video based on the information identified by the analysis unit, it is possible to generate video based on the user's image.

[0033] The providing unit provides the generated video to the user, allowing the user to give corrections or additional instructions as needed. The providing unit can provide the generated video to the user, for example, by streaming or downloading. The providing unit can provide an interface that allows the user to change the color of a scene or adjust the movement of a character, for example. The providing unit can provide a function that allows the user to input additional scenes or characters, for example. This allows the generated video to be provided to the user, allowing the user to give corrections or additional instructions as needed.

[0034] The reception unit can analyze the user's past video image input history and select an appropriate reception method. For example, the reception unit can automatically display video image patterns that the user has frequently input in the past as candidates. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest video image patterns to be used in a specific time period from the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past video image input history.

[0035] The reception unit can filter the video images based on the user's current project or field of interest when receiving the video images. For example, the reception unit can preferentially receive video images related to a project currently in progress by the user. For example, the reception unit can filter and receive related video images based on the user's field of interest. For example, the reception unit can suggest related video images by referring to the user's past project history. In this way, by filtering the video images based on the user's current project or field of interest, it is possible to preferentially receive highly relevant video images.

[0036] When receiving a video image, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs a video image by voice, the reception unit can use voice recognition technology to receive the video image. For example, when the user inputs a video image by text, the reception unit can use text analysis technology to receive the video image. For example, when the user inputs a video image by image, the reception unit can use image analysis technology to receive the video image. This allows the video image to be received efficiently by selecting the optimal reception means depending on the user's input method.

[0037] When receiving video images, the reception unit can preferentially receive highly relevant images based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can preferentially receive video images related to that area. For example, when the user is traveling, the reception unit can preferentially receive video images related to the travel destination. For example, when the user is participating in a specific event, the reception unit can preferentially receive video images related to the event. In this way, by taking the user's geographical location information into consideration, highly relevant video images can be preferentially received.

[0038] The reception unit can analyze the user's social media activity and receive related images when receiving a video image. For example, the reception unit can preferentially receive video images shared by the user on social media. For example, the reception unit can analyze the content of the user's posts on social media and receive related video images. For example, the reception unit can receive related video images by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related video images.

[0039] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving the video image. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can, for example, preferentially suggest a specific reception method based on the user's past feedback. The reception unit can, for example, analyze the user's past feedback and customize the reception method. In this way, the optimal reception method can be customized by reflecting the user's past feedback.

[0040] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the video image. For example, the analysis unit can perform a detailed analysis on a video image with a high level of importance. For example, the analysis unit can perform a concise analysis on a video image with a low level of importance. For example, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the video image. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the video image.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video image. For example, the analysis unit can apply a specific analysis algorithm to a video image of a natural landscape. For example, the analysis unit can apply a different analysis algorithm to a video image of an urban landscape. For example, the analysis unit can apply yet another analysis algorithm to a video image featuring a character. In this way, by applying different analysis algorithms depending on the category of the video image, more appropriate analysis results can be provided.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the user's past analysis results. The analysis unit can extract specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can analyze the user's past analysis results and optimize the analysis algorithm, for example. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the video image. For example, the analysis unit can prioritize analysis of the most recently submitted video image. For example, the analysis unit can analyze the oldest submitted video image later. For example, the analysis unit can gradually adjust the priority of analysis depending on the time of submission. In this way, by determining the priority of analysis based on the time of submission of the video image, analysis can be performed efficiently.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the video images. For example, the analysis unit can prioritize analysis of highly relevant video images. For example, the analysis unit can postpone analysis of less relevant video images. For example, the analysis unit can gradually adjust the order of analysis according to the relevance of the video images. In this way, by adjusting the order of analysis based on the relevance of the video images, analysis can be performed efficiently.

[0045] During analysis, the analysis unit can optimize the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. For example, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. For example, the analysis unit can gradually adjust the use of technical terms in the analysis results according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0046] The generation unit can adjust the accuracy of generation based on the importance of the video image during generation. For example, the generation unit can perform detailed generation for a video image with high importance. For example, the generation unit can perform simple generation for a video image with low importance. For example, the generation unit can adjust the level of detail of generation in stages according to the importance of the video image. In this way, by adjusting the level of detail of generation based on the importance of the video image, it is possible to efficiently generate a video.

[0047] The generation unit can apply different generation algorithms depending on the category of the video image during generation. For example, the generation unit can apply a specific generation algorithm to a video image of a natural landscape. For example, the generation unit can apply a different generation algorithm to a video image of an urban landscape. For example, the generation unit can apply yet another generation algorithm to a video image featuring a character. In this way, by applying different generation algorithms depending on the category of the video image, more appropriate video can be generated.

[0048] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit can improve the accuracy of current generation, for example, based on the user's past generation results. The generation unit can, for example, extract a specific pattern from the user's past generation results and reflect it in the current generation. The generation unit can, for example, analyze the user's past generation results and optimize the generation algorithm. This allows the accuracy of generation to be improved by referring to the user's past generation results.

[0049] At the time of generation, the generation unit can determine a generation priority based on the submission time of the video image. For example, the generation unit can generate the most recently submitted video image with priority. For example, the generation unit can generate the oldest submitted video image later. For example, the generation unit can gradually adjust the generation priority according to the submission time. In this way, by determining the generation priority based on the submission time of the video image, videos can be generated efficiently.

[0050] The generation unit can adjust the order of generation based on the relevance of the video images during generation. For example, the generation unit can generate video images with high relevance preferentially. For example, the generation unit can generate video images with low relevance later. For example, the generation unit can adjust the order of generation in stages according to the relevance of the video images. In this way, by adjusting the order of generation based on the relevance of the video images, videos can be generated efficiently.

[0051] The generation unit may optimize the use of technical terms in the generation according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit may generate a video that uses a lot of technical terms. For example, if the user does not have technical expertise, the generation unit may generate a concise and easy-to-understand video. For example, the generation unit may gradually adjust the use of technical terms in the generation according to the user's level of expertise. In this way, a more appropriate video may be generated by adjusting the use of technical terms in the generation according to the user's level of expertise.

[0052] The providing unit can adjust the accuracy of the provision based on the importance of the video image when providing the video. For example, the providing unit can provide detailed information for a video image with a high importance. For example, the providing unit can provide concise information for a video image with a low importance. For example, the providing unit can gradually adjust the level of detail of the provision according to the importance of the video image. In this way, by adjusting the level of detail of the provision based on the importance of the video image, the video can be provided efficiently.

[0053] The providing unit can apply different providing algorithms depending on the category of the video image when providing the video. For example, the providing unit can apply a specific providing algorithm to a video image of a natural landscape. For example, the providing unit can apply a different providing algorithm to a video image of an urban landscape. For example, the providing unit can apply yet another providing algorithm to a video image in which a character appears. In this way, by applying different providing algorithms depending on the category of the video image, more appropriate video can be provided.

[0054] The providing unit can improve the accuracy of the provision based on the user's past provision results at the time of provision. The providing unit can improve the accuracy of the current provision, for example, based on the user's past provision results. The providing unit can extract a specific pattern from the user's past provision results and reflect it in the current provision. The providing unit can analyze the user's past provision results and optimize the provision algorithm, for example. This can improve the accuracy of the provision by referring to the user's past provision results.

[0055] The providing unit can determine the priority of provision based on the time of submission of the video images at the time of provision. For example, the providing unit can provide the most recently submitted video images preferentially. For example, the providing unit can provide the oldest submitted video images later. For example, the providing unit can gradually adjust the priority of provision depending on the time of submission. In this way, by determining the priority of provision based on the time of submission of the video images, videos can be provided efficiently.

[0056] The providing unit can adjust the order of providing the video images based on the relevance of the video images when providing them. For example, the providing unit can provide highly relevant video images preferentially. For example, the providing unit can provide less relevant video images later. For example, the providing unit can adjust the order of providing the video images in stages according to the relevance of the video images. In this way, by adjusting the order of providing the video images based on the relevance of the video images, the video images can be provided efficiently.

[0057] The providing unit may optimize the use of technical terms provided in accordance with the user's level of expertise during provision. For example, if the user has technical expertise, the providing unit may provide video that uses a lot of technical terms. For example, if the user does not have technical expertise, the providing unit may provide concise and easy-to-understand video. For example, the providing unit may gradually adjust the use of technical terms provided in accordance with the user's level of expertise. This allows for the provision of more appropriate video by adjusting the use of technical terms provided in accordance with the user's level of expertise.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the user's past video image input history and select an appropriate reception method. For example, it can automatically display video image patterns that the user has frequently input in the past as candidates. Furthermore, it can prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest video image patterns to be used in specific time periods from the user's past input history. In this way, it is possible to select the optimal reception method by analyzing the user's past video image input history.

[0060] The generation unit can apply different generation algorithms depending on the category of the video image during generation. For example, a specific generation algorithm can be applied to a video image of a natural landscape. A different generation algorithm can be applied to a video image of an urban landscape. Furthermore, a still different generation algorithm can be applied to a video image featuring a character. In this way, by applying different generation algorithms depending on the category of the video image, more appropriate video can be generated.

[0061] The reception unit can filter video images based on the user's current project or field of interest when receiving the video images. For example, video images related to a project currently underway by the user can be preferentially received. Related video images can be filtered and received based on the user's field of interest. Also, related video images can be suggested with reference to the user's past project history. In this way, by filtering video images based on the user's current project or field of interest, highly relevant video images can be preferentially received.

[0062] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the accuracy of the current analysis can be improved based on the user's past analysis results. Specific patterns can be extracted from the user's past analysis results and reflected in the current analysis. In addition, the user's past analysis results can be analyzed and the analysis algorithm can be optimized. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results.

[0063] The providing unit can determine the priority of provision based on the time of submission of the video images at the time of provision. For example, the most recently submitted video images can be provided preferentially. The older submitted video images can be provided later. Furthermore, the priority of provision can be adjusted in stages depending on the time of submission. In this way, by determining the priority of provision based on the time of submission of the video images, videos can be provided efficiently.

[0064] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the video image. For example, the most recently submitted video image can be analyzed first. Older submitted video images can be analyzed later. The analysis priority can also be adjusted in stages depending on the time of submission. This allows for efficient analysis by determining the priority of analysis based on the time of submission of the video image.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives the user's visual image. The user's visual image can include still images, videos, 3D models, etc. In addition, the reception unit can also receive detailed information entered by the user, such as specific scenes, characters, and backgrounds. Step 2: The analysis unit analyzes the video image received by the reception unit. The analysis is performed using image recognition technology and pattern recognition technology to identify the scene composition, character movements, background depictions, etc. Step 3: The generation unit generates an image based on the information identified by the analysis unit. The generation is performed using rendering technology and animation generation technology, and an image based on the user's image is generated. Step 4: The providing unit provides the video generated by the generating unit to the user. The video is provided by streaming distribution, download provision, or the like, and the user can make corrections or additional instructions as needed.

[0067] (Example 2) A video generation system according to an embodiment of the present invention generates high-quality videos based on a video image input by a user. This video generation system uses AI to generate high-quality videos based on the user's input image. This significantly reduces the time, cost, and effort required for video production, allowing users to focus on their ideas and plans. An application is also available that provides an interface specialized for AI-powered video production. This allows the video generation system to easily generate high-quality videos without requiring specialized skills. For example, in the advertising industry, high-quality video production is required in a short period of time. This system allows users to produce videos quickly and efficiently. Furthermore, even individual creators or small production teams can produce professional-quality videos at low cost. This significantly simplifies the video production process, providing an environment in which users can focus on their ideas and plans.

[0068] The video generation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's video image. The user's video image may include, but is not limited to, a still image, a video, or a 3D model. The reception unit may receive detailed information, such as a specific scene, character, or background, input by the user. The analysis unit analyzes the video image received by the reception unit. The analysis may be performed using, for example, but not limited to, image recognition technology or pattern recognition technology. The analysis unit identifies the scene configuration, character movement, background depiction, and the like. The generation unit generates a video based on the information identified by the analysis unit. The generation may be performed using, for example, but not limited to, rendering technology or animation generation technology. The generation unit generates a video based on the user's image. The provision unit provides the video generated by the generation unit to the user. The provision may be performed by, for example, but not limited to, streaming distribution or download provision. The provision unit allows the user to provide instructions for correction or addition as needed. As a result, the image generation system according to the embodiment can efficiently accept, analyze, generate, and provide the user's image.

[0069] The reception unit can receive detailed information about a specific scene, character, or background input by the user. Examples of specific scenes include, but are not limited to, a location, a time, and a situation. For example, the user can input an image such as "a scene of children playing on the beach at sunset" or "a night view of a futuristic city." Examples of characters include, but are not limited to, appearance, personality, and role. For example, the reception unit can allow the user to input detailed information about the character. Examples of backgrounds include, but are not limited to, scenery, buildings, and environments. For example, the reception unit can allow the user to input detailed information about the background. As a result, a more specific video image can be received by inputting detailed information by the user.

[0070] The analysis unit analyzes the information received by the reception unit and can identify the scene configuration, character movements, and background depiction. Scene configuration includes, for example, the order of scenes, important events, etc., but is not limited to these examples. The analysis unit can, for example, analyze the order of scenes and identify important events. Character movements include, for example, the type of movement, movement patterns, etc., but are not limited to these examples. The analysis unit can, for example, analyze the type of character movement and identify movement patterns. Background depiction includes, for example, the level of detail of the depiction, color, texture, etc., but is not limited to these examples. The analysis unit can, for example, analyze the level of detail of the background depiction and identify color and texture. Thus, by analyzing the information received by the reception unit, the scene configuration, character movements, background depiction, etc. can be identified.

[0071] The generation unit can generate video based on the information identified by the analysis unit. Video generation includes, but is not limited to, rendering technology and animation generation technology, for example. The generation unit can realistically reproduce the scene configuration, character movements, and background depiction using rendering technology, for example. The generation unit can smoothly express character movements using animation generation technology, for example. The generation unit can depict the color of the scene and the texture of the background in detail, for example. As a result, by generating video based on the information identified by the analysis unit, it is possible to generate video based on the user's image.

[0072] The providing unit provides the generated video to the user, allowing the user to give corrections or additional instructions as needed. The providing unit can provide the generated video to the user, for example, by streaming or downloading. The providing unit can provide an interface that allows the user to change the color of a scene or adjust the movement of a character, for example. The providing unit can provide a function that allows the user to input additional scenes or characters, for example. This allows the generated video to be provided to the user, allowing the user to give corrections or additional instructions as needed.

[0073] The reception unit can estimate the user's emotion and adjust the timing of receiving the video image based on the estimated user emotion. The reception unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expression and adjust the timing of receiving the video image. The reception unit can, for example, record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the timing of receiving the video image. The reception unit can, for example, collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and adjust the timing of receiving the video image. This allows the timing of receiving the video image to be adjusted according to the user's emotion, thereby allowing the video image to be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0074] The reception unit can analyze the user's past video image input history and select an appropriate reception method. For example, the reception unit can automatically display video image patterns that the user has frequently input in the past as candidates. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest video image patterns to be used in a specific time period from the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past video image input history.

[0075] The reception unit can filter the video images based on the user's current project or field of interest when receiving the video images. For example, the reception unit can preferentially receive video images related to a project currently in progress by the user. For example, the reception unit can filter and receive related video images based on the user's field of interest. For example, the reception unit can suggest related video images by referring to the user's past project history. In this way, by filtering the video images based on the user's current project or field of interest, it is possible to preferentially receive highly relevant video images.

[0076] When receiving a video image, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs a video image by voice, the reception unit can use voice recognition technology to receive the video image. For example, when the user inputs a video image by text, the reception unit can use text analysis technology to receive the video image. For example, when the user inputs a video image by image, the reception unit can use image analysis technology to receive the video image. This allows the video image to be received efficiently by selecting the optimal reception means depending on the user's input method.

[0077] The reception unit can estimate the user's emotion and determine the priority of the video images to be received based on the estimated user's emotion. For example, when the user is excited, the reception unit can receive video images with priority and start processing them quickly. For example, when the user is relaxed, the reception unit can receive video images with normal priority. For example, when the user is stressed, the reception unit can receive video images with top priority and start processing them quickly. By determining the priority of the video images based on the user's emotion, the video images can be received in a more appropriate order. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] When receiving video images, the reception unit can preferentially receive highly relevant images based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can preferentially receive video images related to that area. For example, when the user is traveling, the reception unit can preferentially receive video images related to the travel destination. For example, when the user is participating in a specific event, the reception unit can preferentially receive video images related to the event. In this way, by taking the user's geographical location information into consideration, highly relevant video images can be preferentially received.

[0079] The reception unit can analyze the user's social media activity and receive related images when receiving a video image. For example, the reception unit can preferentially receive video images shared by the user on social media. For example, the reception unit can analyze the content of the user's posts on social media and receive related video images. For example, the reception unit can receive related video images by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related video images.

[0080] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving the video image. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can, for example, preferentially suggest a specific reception method based on the user's past feedback. The reception unit can, for example, analyze the user's past feedback and customize the reception method. In this way, the optimal reception method can be customized by reflecting the user's past feedback.

[0081] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in detail. For example, if the user is in a hurry, the analysis unit can display the analysis results in a concise manner. For example, if the user is excited, the analysis unit can display the analysis results with a visually stimulating effect. This allows for adjusting the way the analysis is presented based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the video image. For example, the analysis unit can perform a detailed analysis on a video image with a high level of importance. For example, the analysis unit can perform a concise analysis on a video image with a low level of importance. For example, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the video image. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the video image.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video image. For example, the analysis unit can apply a specific analysis algorithm to a video image of a natural landscape. For example, the analysis unit can apply a different analysis algorithm to a video image of an urban landscape. For example, the analysis unit can apply yet another analysis algorithm to a video image featuring a character. In this way, by applying different analysis algorithms depending on the category of the video image, more appropriate analysis results can be provided.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the user's past analysis results. The analysis unit can extract specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can analyze the user's past analysis results and optimize the analysis algorithm, for example. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can shorten the length of the analysis and provide results that focus on the main points. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the video image. For example, the analysis unit can prioritize analysis of the most recently submitted video image. For example, the analysis unit can analyze the oldest submitted video image later. For example, the analysis unit can gradually adjust the priority of analysis depending on the time of submission. In this way, by determining the priority of analysis based on the time of submission of the video image, analysis can be performed efficiently.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the video images. For example, the analysis unit can prioritize analysis of highly relevant video images. For example, the analysis unit can postpone analysis of less relevant video images. For example, the analysis unit can gradually adjust the order of analysis according to the relevance of the video images. In this way, by adjusting the order of analysis based on the relevance of the video images, analysis can be performed efficiently.

[0088] During analysis, the analysis unit can optimize the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. For example, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. For example, the analysis unit can gradually adjust the use of technical terms in the analysis results according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0089] The generation unit can estimate the user's emotions and adjust the expression method of the generated video based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an image that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate an image that emphasizes the shortest route. For example, if the user is excited, the generation unit can generate an image that adds a visually stimulating effect. In this way, by adjusting the expression method of the generated video based on the user's emotions, more appropriate videos can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The generation unit can adjust the accuracy of generation based on the importance of the video image during generation. For example, the generation unit can perform detailed generation for a video image with high importance. For example, the generation unit can perform simple generation for a video image with low importance. For example, the generation unit can adjust the level of detail of generation in stages according to the importance of the video image. In this way, by adjusting the level of detail of generation based on the importance of the video image, it is possible to efficiently generate a video.

[0091] The generation unit can apply different generation algorithms depending on the category of the video image during generation. For example, the generation unit can apply a specific generation algorithm to a video image of a natural landscape. For example, the generation unit can apply a different generation algorithm to a video image of an urban landscape. For example, the generation unit can apply yet another generation algorithm to a video image featuring a character. In this way, by applying different generation algorithms depending on the category of the video image, more appropriate video can be generated.

[0092] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit can improve the accuracy of current generation, for example, based on the user's past generation results. The generation unit can, for example, extract a specific pattern from the user's past generation results and reflect it in the current generation. The generation unit can, for example, analyze the user's past generation results and optimize the generation algorithm. This allows the accuracy of generation to be improved by referring to the user's past generation results.

[0093] The generation unit can estimate the user's emotions and adjust the length of the video to be generated based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point video. For example, if the user is relaxed, the generation unit can generate a longer video including detailed explanations. For example, if the user is excited, the generation unit can generate a video with visually stimulating effects. In this way, by adjusting the length of the video to be generated based on the user's emotions, more appropriate videos can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] At the time of generation, the generation unit can determine a generation priority based on the submission time of the video image. For example, the generation unit can generate the most recently submitted video image with priority. For example, the generation unit can generate the oldest submitted video image later. For example, the generation unit can gradually adjust the generation priority according to the submission time. In this way, by determining the generation priority based on the submission time of the video image, videos can be generated efficiently.

[0095] The generation unit can adjust the order of generation based on the relevance of the video images during generation. For example, the generation unit can generate video images with high relevance preferentially. For example, the generation unit can generate video images with low relevance later. For example, the generation unit can adjust the order of generation in stages according to the relevance of the video images. In this way, by adjusting the order of generation based on the relevance of the video images, videos can be generated efficiently.

[0096] The generation unit may optimize the use of technical terms in the generation according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit may generate a video that uses a lot of technical terms. For example, if the user does not have technical expertise, the generation unit may generate a concise and easy-to-understand video. For example, the generation unit may gradually adjust the use of technical terms in the generation according to the user's level of expertise. In this way, a more appropriate video may be generated by adjusting the use of technical terms in the generation according to the user's level of expertise.

[0097] The providing unit can estimate the user's emotions and adjust the display method of the video to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. For example, if the user is relaxed, the providing unit can provide a display method including detailed information. For example, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This makes it possible to provide more appropriate videos by adjusting the display method of the video to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] The providing unit can adjust the accuracy of the provision based on the importance of the video image when providing the video. For example, the providing unit can provide detailed information for a video image with a high importance. For example, the providing unit can provide concise information for a video image with a low importance. For example, the providing unit can gradually adjust the level of detail of the provision according to the importance of the video image. In this way, by adjusting the level of detail of the provision based on the importance of the video image, the video can be provided efficiently.

[0099] The providing unit can apply different providing algorithms depending on the category of the video image when providing the video. For example, the providing unit can apply a specific providing algorithm to a video image of a natural landscape. For example, the providing unit can apply a different providing algorithm to a video image of an urban landscape. For example, the providing unit can apply yet another providing algorithm to a video image in which a character appears. In this way, by applying different providing algorithms depending on the category of the video image, more appropriate video can be provided.

[0100] The providing unit can improve the accuracy of the provision based on the user's past provision results at the time of provision. The providing unit can improve the accuracy of the current provision, for example, based on the user's past provision results. The providing unit can extract a specific pattern from the user's past provision results and reflect it in the current provision. The providing unit can analyze the user's past provision results and optimize the provision algorithm, for example. This can improve the accuracy of the provision by referring to the user's past provision results.

[0101] The providing unit can estimate the user's emotions and adjust the length of the video to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide a short, to-the-point video. For example, if the user is relaxed, the providing unit can provide a longer video with detailed explanations. For example, if the user is excited, the providing unit can provide a video with visually stimulating effects. In this way, by adjusting the length of the video to be provided based on the user's emotions, more appropriate videos can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The providing unit can determine the priority of provision based on the time of submission of the video images at the time of provision. For example, the providing unit can provide the most recently submitted video images preferentially. For example, the providing unit can provide the oldest submitted video images later. For example, the providing unit can gradually adjust the priority of provision depending on the time of submission. In this way, by determining the priority of provision based on the time of submission of the video images, videos can be provided efficiently.

[0103] The providing unit can adjust the order of providing the video images based on the relevance of the video images when providing them. For example, the providing unit can provide highly relevant video images preferentially. For example, the providing unit can provide less relevant video images later. For example, the providing unit can adjust the order of providing the video images in stages according to the relevance of the video images. In this way, by adjusting the order of providing the video images based on the relevance of the video images, the video images can be provided efficiently.

[0104] The providing unit may optimize the use of technical terms provided in accordance with the user's level of expertise during provision. For example, if the user has technical expertise, the providing unit may provide video that uses a lot of technical terms. For example, if the user does not have technical expertise, the providing unit may provide concise and easy-to-understand video. For example, the providing unit may gradually adjust the use of technical terms provided in accordance with the user's level of expertise. This allows for the provision of more appropriate video by adjusting the use of technical terms provided in accordance with the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a video image input by a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the video image received by the reception unit. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates a video based on information identified by the analysis unit. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated video to a user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a video image input by a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the video image received by the reception unit. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates a video based on information identified by the analysis unit. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated video to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives a video image input by a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the video image received by the reception unit. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates a video based on information identified by the analysis unit. The provision unit is realized, for example, by the display 343 of the headset type terminal 314 and provides the generated video to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a video image input by a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the video image received by the reception unit. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates a video based on information identified by the analysis unit. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated video to the user.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can analyze the user's past video image input history and select an appropriate reception method. For example, it can automatically display video image patterns that the user has frequently input in the past as candidates. Furthermore, it can prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest video image patterns to be used in specific time periods from the user's past input history. In this way, it is possible to select the optimal reception method by analyzing the user's past video image input history.

[0107] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis results can be displayed in detail. If the user is in a hurry, the analysis results can be displayed briefly. If the user is excited, the analysis results can be displayed with visually stimulating effects. In this way, by adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided.

[0108] The generation unit can apply different generation algorithms depending on the category of the video image during generation. For example, a specific generation algorithm can be applied to a video image of a natural landscape. A different generation algorithm can be applied to a video image of an urban landscape. Furthermore, a still different generation algorithm can be applied to a video image featuring a character. In this way, by applying different generation algorithms depending on the category of the video image, more appropriate video can be generated.

[0109] The providing unit can estimate the user's emotions and adjust the display method of the video to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the video to be provided based on the user's emotions, more appropriate video can be provided.

[0110] The reception unit can filter video images based on the user's current project or field of interest when receiving the video images. For example, video images related to a project currently underway by the user can be preferentially received. Related video images can be filtered and received based on the user's field of interest. Also, related video images can be suggested with reference to the user's past project history. In this way, by filtering video images based on the user's current project or field of interest, highly relevant video images can be preferentially received.

[0111] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the accuracy of the current analysis can be improved based on the user's past analysis results. Specific patterns can be extracted from the user's past analysis results and reflected in the current analysis. In addition, the user's past analysis results can be analyzed and the analysis algorithm can be optimized. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results.

[0112] The generation unit can estimate the user's emotions and adjust the length of the video to be generated based on the estimated user emotions. For example, if the user is in a hurry, a short video that is to the point can be generated. If the user is relaxed, a longer video that includes detailed explanations can be generated. Also, if the user is excited, a video with visually stimulating effects can be generated. In this way, by adjusting the length of the video to be generated based on the user's emotions, more appropriate videos can be generated.

[0113] The providing unit can determine the priority of provision based on the time of submission of the video images at the time of provision. For example, the most recently submitted video images can be provided preferentially. The older submitted video images can be provided later. Furthermore, the priority of provision can be adjusted in stages depending on the time of submission. In this way, by determining the priority of provision based on the time of submission of the video images, videos can be provided efficiently.

[0114] The reception unit can estimate the user's emotions and determine the priority of the video images to be received based on the estimated user's emotions. For example, if the user is excited, video images can be received with priority and processing can be started quickly. If the user is relaxed, video images can be received with normal priority. Furthermore, if the user is stressed, video images can be received with top priority and processing can be started quickly. In this way, by determining the priority of the video images based on the user's emotions, video images can be received in a more appropriate order.

[0115] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the video image. For example, the most recently submitted video image can be analyzed first. Older submitted video images can be analyzed later. The analysis priority can also be adjusted in stages depending on the time of submission. This allows for efficient analysis by determining the priority of analysis based on the time of submission of the video image.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit receives the user's visual image. The user's visual image can include still images, videos, 3D models, etc. In addition, the reception unit can also receive detailed information entered by the user, such as specific scenes, characters, and backgrounds. Step 2: The analysis unit analyzes the video image received by the reception unit. The analysis is performed using image recognition technology and pattern recognition technology to identify the scene composition, character movements, background depictions, etc. Step 3: The generation unit generates an image based on the information identified by the analysis unit. The generation is performed using rendering technology and animation generation technology, and an image based on the user's image is generated. Step 4: The providing unit provides the video generated by the generating unit to the user. The video is provided by streaming distribution, download provision, or the like, and the user can make corrections or additional instructions as needed.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

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

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

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0150] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0152] 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.

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

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a video image from a user; an analysis unit that analyzes the video image received by the reception unit; a generation unit that generates a video based on the video image analyzed by the analysis unit; a providing unit that provides the video generated by the generating unit. A system characterized by:

2. The reception unit Accepts detailed information about specific scenes, characters, and backgrounds entered by the user 2. The system of claim 1.

3. The analysis unit The information received by the reception unit is analyzed, and a scene configuration, character movement, and background depiction are identified.

2. The system of claim 1.

4. The generation unit Generate an image based on the information identified by the analysis unit 2. The system of claim 1.

5. The providing unit The generated video is provided to the user, who can then provide corrections or additional instructions as needed.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of receiving video images based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past video image input history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit As video images are received, they are filtered based on the user's current projects and interests.

2. The system of claim 1.

Images