system

Abstract

The system according to the embodiment aims to enable users to easily create moral education animations at home. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a configuration unit, and a provision unit. The reception unit receives input of a scenario. The generation unit analyzes the scenario received by the reception unit and generates an animation scene. The configuration unit configures the animation generated by the generation unit. The provision unit provides the animation generated by the configuration unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to easily create moral education animations at home.

[0005] The system according to the embodiment aims to enable users to easily create moral education animations at home. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a configuration unit, and a provision unit. The reception unit receives input of a scenario. The generation unit analyzes the scenario received by the reception unit and generates an animation scene. The configuration unit configures the animation generated by the generation unit. The provision unit provides the animation generated by the configuration unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create moral education animations at home. [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 moral education animation creation system according to an embodiment of the present invention allows users to easily create moral education animations at home. In this system, a user inputs an animation scenario, and a generation AI analyzes the scenario and automatically generates animation scenes. The generated animation is constructed based on characters and backgrounds selected by the user. This system allows users to easily create moral education animations without specialized knowledge. For example, if a user inputs a scenario based on the theme of "the importance of helping friends," the generation AI analyzes the scenario and programs the character movements, facial expressions, background changes, and so on. The generated animation is constructed based on a background selected by the user, such as a school classroom or park. This allows teachers to create animations for use in classes and parents to create animations to teach morality to their children. This allows users to easily create moral education animations without specialized knowledge.

[0029] A moral education animation creation system according to an embodiment includes a reception unit, a generation unit, a composition unit, and a provision unit. The reception unit receives an animation scenario input from a user. For example, the user can input a scenario based on the theme of "the importance of helping friends." The generation unit uses a generation AI to analyze the scenario received by the reception unit and generate animation scenes. For example, the generation AI programs character movements, facial expressions, background changes, and the like based on the content of the scenario. The composition unit composes the animation generated by the generation unit. For example, the animation is composed based on elements selected from a library of characters and backgrounds selected by the user. The provision unit provides the animation generated by the composition unit. For example, the animation created by the user can be viewed. This allows the moral education animation creation system according to an embodiment to easily create moral education animations even if the user does not have specialized knowledge.

[0030] The reception unit can accept text input, voice input, and image input. For example, the reception unit allows a user to input a scenario in text format using a keyboard. The reception unit can also input a scenario in voice format using a microphone. The reception unit can also input a scenario in image format using a camera. For example, a user can take a picture of a handwritten scenario with a camera and input the image to the reception unit. This allows a user to input a scenario in a variety of ways. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can convert a voice-input scenario into text format using voice recognition technology.

[0031] The generation unit can generate character movements, facial expressions, and background changes based on the content of the scenario. The generation unit generates character movements based on the content of the scenario, for example, using a generation AI. For example, in a scene where a character helps a friend, the generation unit generates an action in which the character reaches out to the friend in need. The generation unit can also generate character facial expressions using the generation AI. For example, in a scene where a character smiles, the generation unit generates a smiling expression on the character's face. The generation unit can also generate background changes using the generation AI. For example, when a scene changes from a school classroom to a park, the background is switched from the school classroom to the park. This allows the generation unit to generate detailed animation based on the scenario. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate animation using an AI model that takes the content of the scenario as input and outputs character movements, facial expressions, and background changes.

[0032] The composition unit can compose an animation based on elements selected from a library of characters and backgrounds selected by the user. For example, the composition unit places a character selected by the user in the animation. For example, if a user selects a character to be used in a school classroom scene, the composition unit places the character in the classroom background. The composition unit can also apply the background selected by the user to the animation. For example, if a user selects a park scene, the composition unit applies the park background to the animation. Furthermore, the composition unit can add effects and sounds selected by the user to the animation. For example, if a user adds sound effects to match the character's movements, the composition unit incorporates the sound effects into the animation. This allows the composition unit to compose an animation based on elements selected by the user. Some or all of the above-described processing in the composition unit may be performed using, for example, AI, or may be performed without using AI. For example, the composition unit can compose an animation using an AI model that inputs data of characters and backgrounds selected by the user and outputs animation.

[0033] The providing unit can provide animations created by users so that they can be viewed. The providing unit, for example, streams the animations created by users. For example, the providing unit can enable the animations to be viewed through a web application or a mobile application. The providing unit can also provide the animations created by users for download. For example, the providing unit can provide the animations in a file format so that the user can download and view them. The providing unit can also provide a function for sharing the animations created by users on social media. For example, the providing unit can directly upload the animations to a social media platform so that they can be shared with other users. In this way, the providing unit can provide the animations created by users so that they can be viewed. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the animations using an AI model that uses animation data as input and performs streaming or download distribution.

[0034] The reception unit can analyze the user's past scenario input history and select an appropriate input method. For example, the reception unit prioritizes and suggests input methods (text, voice, image) that the user has frequently used in the past. For example, if the user has frequently used text input in the past, the reception unit prioritizes and suggests text input. The reception unit can also automatically suggest scenarios related to a specific theme based on the user's past input history. For example, the reception unit can analyze the theme of scenarios the user has previously input and suggest related scenarios. Furthermore, the reception unit can analyze the user's past input history and provide a customized input interface to improve input efficiency. For example, the reception unit can learn patterns of scenarios the user has previously input and suggest an efficient input method. This allows the reception unit to provide an optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can support scenario input using an AI model that selects an optimal input method using the user's past input data as input.

[0035] The reception unit can filter scenarios based on the user's current themes of interest when the scenario is input. For example, the reception unit can suggest scenario templates related to the user's current themes of interest. For example, if the user is interested in "environmental protection," the reception unit can suggest scenario templates related to environmental protection. The reception unit can also automatically complete related keywords based on the user's themes of interest. For example, if the user is interested in "the importance of helping friends," the reception unit can automatically complete related keywords and support the input of the scenario. Furthermore, the reception unit can filter the input content of the scenario in real time based on the user's themes of interest and provide appropriate advice. For example, the reception unit can analyze the content of the scenario input by the user and provide appropriate advice based on the themes of interest. This allows the reception unit to filter the input of the scenario based on the user's themes of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can support the input of the scenario using an AI model that filters the input content of the scenario using the user's themes of interest as input.

[0036] When inputting a scenario, the reception unit can prioritize inputting a highly relevant scenario based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting a scenario related to that area. For example, when the user is in an urban area, the reception unit suggests a scenario related to the urban area. Furthermore, when the user is traveling, the reception unit can prioritize inputting a scenario related to the travel destination. For example, when the user is in a tourist destination, the reception unit suggests a scenario related to the tourist destination. Furthermore, when the user is at home, the reception unit can prioritize inputting a scenario related to the home. For example, the reception unit suggests a scenario related to an event that occurs at home. This allows the reception unit to prioritize inputting a highly relevant scenario based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can support input of a scenario using an AI model that suggests highly relevant scenarios using the user's geographical location information as input.

[0037] When inputting a scenario, the reception unit can analyze the user's social media activity and input a related scenario. The reception unit can, for example, suggest related scenarios based on content shared by the user on social media. For example, if the user posts on social media about "environmental protection," the reception unit can suggest scenarios related to environmental protection. The reception unit can also analyze the content of the user's social media posts and input scenarios related to the user's interests. For example, if the user posts about "the importance of helping friends," the reception unit can suggest related scenarios. The reception unit can also input related scenarios based on the activities of the user's social media followers and friends. For example, if the user's followers are interested in "environmental protection," the reception unit can suggest related scenarios. This allows the reception unit to input related scenarios based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can support the input of scenarios using an AI model that uses the user's social media activity data as input and suggests related scenarios.

[0038] The generation unit can adjust the level of detail of the animation based on the importance of the scenario during generation. For example, for an important scenario, the generation unit generates detailed backgrounds and character movements. For example, the generation unit depicts the background in detail and programs the character movements in detail in important scenes of the scenario. The generation unit can also generate simplified backgrounds and character movements in less important scenarios. For example, the generation unit simplifies the background and simplifies the character movements in scenes of less importance of the scenario. Furthermore, the generation unit can adjust the level of detail of animation effects and audio according to the importance of the scenario. For example, the generation unit sets detailed effects and audio in important scenarios to generate visually and aurally appealing animation. This allows the generation unit to adjust the level of detail of the animation according to the importance of the scenario. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate animation using an AI model that adjusts the level of detail of the animation using scenario importance data as input.

[0039] The generation unit can apply different generation algorithms depending on the scenario category during generation. For example, in the case of an educational scenario, the generation unit applies a generation algorithm that emphasizes educational elements. For example, in an educational scenario, the generation unit generates character movements and background settings that match the educational content. Furthermore, in the case of an entertainment scenario, the generation unit can apply a visually appealing generation algorithm. For example, in an entertainment scenario, the generation unit generates character movements and background settings that are visually appealing. Furthermore, in the case of an emotional scenario, the generation unit can apply a generation algorithm that enhances emotions. For example, in an emotional scenario, the generation unit generates character movements and background settings that enhance emotions. This allows the generation unit to apply an optimal generation algorithm depending on the scenario category. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate animation using an AI model that applies different generation algorithms using scenario category data as input.

[0040] The generation unit can determine the priority of animations based on the submission dates of the scenarios at the time of generation. For example, for a scenario with an approaching deadline, the generation unit generates animations with priority. For example, if the submission deadline of a scenario is approaching, the generation unit processes that scenario with priority and generates animations. The generation unit can also postpone the generation of animations for a scenario with a distant submission date. For example, if the submission deadline of a scenario is still in the future, the generation unit postpones processing of that scenario. Furthermore, the generation unit can adjust the order of animation generation according to the submission dates. For example, the generation unit generates animations by determining the priority order taking into account the submission dates of multiple scenarios. This allows the generation unit to determine the priority of animations based on the submission dates of the scenarios. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate animations using an AI model that determines the priority of animations using scenario submission date data as input.

[0041] The generation unit can adjust the order of animations based on the relevance of the scenarios during generation. For example, the generation unit prioritizes animating highly relevant scenarios. For example, if the content of a scenario is highly relevant to other scenarios, the generation unit prioritizes processing the scenario and generates animation. The generation unit can also postpone animating less relevant scenarios. For example, if the content of a scenario is less relevant to other scenarios, the generation unit postpones processing the scenario. Furthermore, the generation unit can adjust the generation order of animations according to the relevance of the scenarios. For example, the generation unit generates animations by determining priorities based on the relevance of multiple scenarios. This allows the generation unit to adjust the order of animations based on the relevance of the scenarios. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate animations using an AI model that adjusts the order of animations using scenario relevance data as input.

[0042] During composition, the composition unit can analyze the user's past selection history to select the optimal composition method. For example, the composition unit prioritizes suggesting characters and backgrounds previously selected by the user. For example, the composition unit analyzes data on characters and backgrounds previously selected by the user and suggests similar characters and backgrounds. The composition unit can also automatically suggest components related to a specific theme based on the user's past selection history. For example, the composition unit suggests components related to a theme previously selected by the user. Furthermore, the composition unit can analyze the user's past selection history and provide a customized composition interface to improve composition efficiency. For example, the composition unit learns composition patterns previously selected by the user and suggests an efficient composition method. This allows the composition unit to provide the optimal composition method based on the user's past selection history. Some or all of the above-described processing in the composition unit may be performed, for example, using AI, or may be performed without AI. For example, the composition unit can compose an animation using an AI model that selects the optimal composition method using the user's past selection data as input.

[0043] The composition unit can customize components based on the user's current themes of interest during composition. For example, the composition unit can suggest characters and backgrounds related to the user's current themes of interest. For example, if the user is interested in "environmental protection," the composition unit can suggest characters and backgrounds related to environmental protection. The composition unit can also automatically complete related effects and sounds based on the user's themes of interest. For example, if the user is interested in "the importance of helping friends," the composition unit can automatically complete related effects and sounds and customize animations. Furthermore, the composition unit can customize components in real time based on the user's themes of interest and provide appropriate advice. For example, the composition unit can analyze the content of a scenario entered by the user and provide appropriate advice based on the themes of interest. This allows the composition unit to customize components based on the user's themes of interest. Some or all of the above-described processing in the composition unit may be performed using, for example, AI, or may be performed without AI. For example, the composition unit can use an AI model that customizes components using the user's themes of interest as input to compose an animation.

[0044] During composition, the composition unit can select appropriate components based on the user's geographical location information. For example, if the user is in a specific area, the composition unit prioritizes selecting components related to that area. For example, if the user is in an urban area, the composition unit suggests components related to the urban area. Furthermore, if the user is traveling, the composition unit can prioritize selecting components related to the travel destination. For example, if the user is in a tourist destination, the composition unit suggests components related to the tourist destination. Furthermore, if the user is at home, the composition unit can prioritize selecting components related to the home. For example, the composition unit suggests components related to events that the user is experiencing at home. This allows the composition unit to select optimal components based on the user's geographical location information. Some or all of the above-described processing in the composition unit may be performed using, for example, AI, or may be performed without AI. For example, the composition unit can compose an animation using an AI model that selects appropriate components using the user's geographical location information as input.

[0045] During composition, the composition unit can analyze the user's social media activity to suggest components. For example, the composition unit can suggest related components based on content shared by the user on social media. For example, if the user posts on social media about "environmental protection," the composition unit can suggest components related to environmental protection. The composition unit can also analyze the content of the user's social media posts to suggest components related to topics of interest. For example, if the user posts about "the importance of helping friends," the composition unit can suggest related components. Furthermore, the composition unit can suggest related components based on the activities of the user's social media followers and friends. For example, if the user's followers are interested in "environmental protection," the composition unit can suggest related components. In this way, the composition unit can suggest related components based on the user's social media activity. Some or all of the above-described processing in the composition unit may be performed using, for example, AI, or may be performed without AI. For example, the composition unit can compose an animation using an AI model that suggests related components using the user's social media activity data as input.

[0046] The providing unit can select the optimal providing method by referring to the user's past viewing history when providing the animation. For example, the providing unit can provide the animation in a similar style based on the style of animation the user has previously viewed. For example, the providing unit can analyze the style of animation the user has previously viewed and suggest animations of a similar style. The providing unit can also analyze the user's preferred genre from the user's past viewing history and provide the optimal animation. For example, the providing unit can analyze the genre the user has previously viewed and suggest animations of a similar genre. Furthermore, the providing unit can select a providing method suited to the viewing time period by referring to the user's past viewing history. For example, the providing unit can analyze the time period the user has previously viewed and provide animations at similar time periods. This allows the providing unit to select the optimal providing method based on the user's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the animation using an AI model that selects the optimal providing method using the user's past viewing data as input.

[0047] The providing unit can customize the provided content based on the user's current themes of interest at the time of providing. For example, the providing unit provides animations related to the user's current themes of interest. For example, if the user is interested in "environmental protection," the providing unit suggests animations related to environmental protection. The providing unit can also customize related effects and sounds based on the user's themes of interest. For example, if the user is interested in "the importance of helping friends," the providing unit customizes related effects and sounds and provides animations. Furthermore, the providing unit can customize the provided content in real time based on the user's themes of interest and provide appropriate advice. For example, the providing unit analyzes the content of the animation the user is watching and provides appropriate advice based on the themes of interest. This allows the providing unit to customize the provided content based on the user's themes of interest. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can provide animations using an AI model that customizes the provided content using the user's themes of interest as input.

[0048] The providing unit can select an appropriate delivery method based on the user's geographical location information when providing the animation. For example, if the user is in a specific area, the providing unit can prioritize providing animations related to that area. For example, if the user is in an urban area, the providing unit can suggest animations related to the urban area. Furthermore, if the user is traveling, the providing unit can prioritize providing animations related to the travel destination. For example, if the user is in a tourist destination, the providing unit can suggest animations related to the tourist destination. Furthermore, if the user is at home, the providing unit can prioritize providing animations related to home life. For example, the providing unit can suggest animations related to events that occur at home. This allows the providing unit to select an optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide animations using an AI model that selects an appropriate delivery method using the user's geographical location information as input.

[0049] The providing unit can analyze the user's social media activity and suggest content to be provided. For example, the providing unit can suggest related animations based on content shared by the user on social media. For example, if the user posts on social media about "environmental protection," the providing unit can suggest animations related to environmental protection. The providing unit can also analyze the user's social media posts and provide animations related to themes of interest. For example, if the user posts about "the importance of helping friends," the providing unit can suggest related animations. Furthermore, the providing unit can provide related animations based on the activities of the user's social media followers and friends. For example, if the user's followers are interested in "environmental protection," the providing unit can suggest related animations. This allows the providing unit to suggest related animations based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can provide animations using an AI model that uses the user's social media activity data as input and suggests related animations.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's input content in real time and provide appropriate feedback. For example, while the user is inputting a scenario, the reception unit analyzes the content and points out grammatical errors and unnatural expressions. The reception unit can also provide related reference materials and advice based on the content of the scenario input by the user. For example, if the user is inputting a scenario on the theme of "the importance of helping friends," the reception unit can suggest related moral anecdotes and quotes. Furthermore, the reception unit can analyze the user's input speed and patterns and provide a customized interface to improve input efficiency. For example, the reception unit can predict frequently used phrases and words and provide an auto-completion function. This allows the reception unit to analyze the user's input content in real time and provide appropriate feedback.

[0052] The generation unit can generate a character's voice based on the content of the scenario. For example, the generation unit generates a character's voice based on the lines of the scenario and incorporates it into the animation. The generation unit can also generate a voice expression according to the character's emotion. For example, in a scene where a character is angry, the generation unit generates a voice that expresses anger. Furthermore, the generation unit can generate background sounds and sound effects based on the content of the scenario. For example, if a scene takes place in a park, the generation unit generates sound effects such as the ambient sounds of the park and birds chirping. This allows the generation unit to generate a character's voice and background sounds based on the content of the scenario.

[0053] The composition unit can compose animations using original materials uploaded by the user in addition to a library of characters and backgrounds selected by the user. For example, the user can upload characters and backgrounds they have drawn themselves and incorporate them into the animation. The composition unit can also add audio and music uploaded by the user to the animation. For example, the composition unit can incorporate narration and background music recorded by the user into the animation. Furthermore, the composition unit can provide editing functions such as color correction and effect application for materials uploaded by the user. For example, the composition unit can adjust the color tone of images uploaded by the user or add specific effects. This allows the composition unit to compose animations using original materials uploaded by the user.

[0054] The providing unit can simultaneously distribute animations created by users on multiple platforms. For example, the providing unit can upload the animations to video sharing sites such as YouTube (registered trademark) and Vimeo (registered trademark) and simultaneously share them on social media. The providing unit can also distribute animations created by users on the internal networks of educational institutions and companies. For example, the providing unit can distribute animations to a school's online class platform or a company's intranet. Furthermore, the providing unit can provide a function for exporting animations created by users to physical media such as DVDs and Blu-rays. For example, the providing unit can export animations in high quality so that users can view them at home. This allows the providing unit to simultaneously distribute animations created by users on multiple platforms.

[0055] The providing unit can collect and analyze viewer feedback on animations created by users. For example, the providing unit provides a function that allows viewers to leave ratings and comments after watching an animation. The providing unit can also analyze viewer feedback and provide improvements and advice to users. For example, the providing unit can analyze viewer ratings and comments and inform users which parts of the animation were well received and which parts need improvement. Furthermore, the providing unit can provide hints and suggestions that will be useful for creating the next animation based on viewer feedback. For example, the providing unit can suggest ideas for the next scenario or character settings based on viewer feedback. In this way, the providing unit can collect and analyze viewer feedback on animations created by users.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user inputs an anime scenario into the reception unit. For example, the user can input a scenario with the theme "The importance of helping friends." Step 2: The generator uses the generation AI to analyze the scenario received by the reception unit and generate animation scenes. For example, the generation AI programs the character's movements, facial expressions, background changes, etc. based on the content of the scenario. Step 3: The composition unit composes the animation generated by the generation unit, for example, based on elements selected from a library of characters and backgrounds selected by the user. Step 4: The providing unit provides the animation generated by the composing unit, for example, providing the animation created by the user so that the user can view it.

[0058] (Example 2) A moral education animation creation system according to an embodiment of the present invention allows users to easily create moral education animations at home. In this system, a user inputs an animation scenario, and a generation AI analyzes the scenario and automatically generates animation scenes. The generated animation is constructed based on characters and backgrounds selected by the user. This system allows users to easily create moral education animations without specialized knowledge. For example, if a user inputs a scenario based on the theme of "the importance of helping friends," the generation AI analyzes the scenario and programs the character movements, facial expressions, background changes, and so on. The generated animation is constructed based on a background selected by the user, such as a school classroom or park. This allows teachers to create animations for use in classes and parents to create animations to teach morality to their children. This allows users to easily create moral education animations without specialized knowledge.

[0059] A moral education animation creation system according to an embodiment includes a reception unit, a generation unit, a composition unit, and a provision unit. The reception unit receives an animation scenario input from a user. For example, the user can input a scenario based on the theme of "the importance of helping friends." The generation unit uses a generation AI to analyze the scenario received by the reception unit and generate animation scenes. For example, the generation AI programs character movements, facial expressions, background changes, and the like based on the content of the scenario. The composition unit composes the animation generated by the generation unit. For example, the animation is composed based on elements selected from a library of characters and backgrounds selected by the user. The provision unit provides the animation generated by the composition unit. For example, the animation created by the user can be viewed. This allows the moral education animation creation system according to an embodiment to easily create moral education animations even if the user does not have specialized knowledge.

[0060] The reception unit can accept text input, voice input, and image input. For example, the reception unit allows a user to input a scenario in text format using a keyboard. The reception unit can also input a scenario in voice format using a microphone. The reception unit can also input a scenario in image format using a camera. For example, a user can take a picture of a handwritten scenario with a camera and input the image to the reception unit. This allows a user to input a scenario in a variety of ways. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can convert a voice-input scenario into text format using voice recognition technology.

[0061] The generation unit can generate character movements, facial expressions, and background changes based on the content of the scenario. The generation unit generates character movements based on the content of the scenario, for example, using a generation AI. For example, in a scene where a character helps a friend, the generation unit generates an action in which the character reaches out to the friend in need. The generation unit can also generate character facial expressions using the generation AI. For example, in a scene where a character smiles, the generation unit generates a smiling expression on the character's face. The generation unit can also generate background changes using the generation AI. For example, when a scene changes from a school classroom to a park, the background is switched from the school classroom to the park. This allows the generation unit to generate detailed animation based on the scenario. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate animation using an AI model that takes the content of the scenario as input and outputs character movements, facial expressions, and background changes.

[0062] The composition unit can compose an animation based on elements selected from a library of characters and backgrounds selected by the user. For example, the composition unit places a character selected by the user in the animation. For example, if a user selects a character to be used in a school classroom scene, the composition unit places the character in the classroom background. The composition unit can also apply the background selected by the user to the animation. For example, if a user selects a park scene, the composition unit applies the park background to the animation. Furthermore, the composition unit can add effects and sounds selected by the user to the animation. For example, if a user adds sound effects to match the character's movements, the composition unit incorporates the sound effects into the animation. This allows the composition unit to compose an animation based on elements selected by the user. Some or all of the above-described processing in the composition unit may be performed using, for example, AI, or may be performed without using AI. For example, the composition unit can compose an animation using an AI model that inputs data of characters and backgrounds selected by the user and outputs animation.

[0063] The providing unit can provide animations created by users so that they can be viewed. The providing unit, for example, streams the animations created by users. For example, the providing unit can enable the animations to be viewed through a web application or a mobile application. The providing unit can also provide the animations created by users for download. For example, the providing unit can provide the animations in a file format so that the user can download and view them. The providing unit can also provide a function for sharing the animations created by users on social media. For example, the providing unit can directly upload the animations to a social media platform so that they can be shared with other users. In this way, the providing unit can provide the animations created by users so that they can be viewed. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the animations using an AI model that uses animation data as input and performs streaming or download distribution.

[0064] The reception unit can estimate the user's emotions and adjust the timing of scenario input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit simplifies the scenario input to allow the user to input it in a short time. For example, the reception unit reduces the number of scenario items to be input by the user and prompts the user to input them in the form of simple questions. Furthermore, when the user is relaxed, the reception unit prompts the user to input a detailed scenario, allowing the user to input more information. For example, the reception unit presents the user with questions requesting a detailed explanation of the scenario. Furthermore, when the user is concentrating, the reception unit allows the user to input the scenario continuously, minimizing interruptions to the work. For example, the reception unit automatically displays the next input item when the user inputs a scenario, supporting smooth input. This allows the reception unit to adjust the timing of scenario input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can support scenario input using an AI model that uses the user's facial expression data as input, estimates emotions, and adjusts the timing of scenario input.

[0065] The reception unit can analyze the user's past scenario input history and select an appropriate input method. For example, the reception unit prioritizes and suggests input methods (text, voice, image) that the user has frequently used in the past. For example, if the user has frequently used text input in the past, the reception unit prioritizes and suggests text input. The reception unit can also automatically suggest scenarios related to a specific theme based on the user's past input history. For example, the reception unit can analyze the theme of scenarios the user has previously input and suggest related scenarios. Furthermore, the reception unit can analyze the user's past input history and provide a customized input interface to improve input efficiency. For example, the reception unit can learn patterns of scenarios the user has previously input and suggest an efficient input method. This allows the reception unit to provide an optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can support scenario input using an AI model that selects an optimal input method using the user's past input data as input.

[0066] The reception unit can filter scenarios based on the user's current themes of interest when the scenario is input. For example, the reception unit can suggest scenario templates related to the user's current themes of interest. For example, if the user is interested in "environmental protection," the reception unit can suggest scenario templates related to environmental protection. The reception unit can also automatically complete related keywords based on the user's themes of interest. For example, if the user is interested in "the importance of helping friends," the reception unit can automatically complete related keywords and support the input of the scenario. Furthermore, the reception unit can filter the input content of the scenario in real time based on the user's themes of interest and provide appropriate advice. For example, the reception unit can analyze the content of the scenario input by the user and provide appropriate advice based on the themes of interest. This allows the reception unit to filter the input of the scenario based on the user's themes of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can support the input of the scenario using an AI model that filters the input content of the scenario using the user's themes of interest as input.

[0067] The reception unit can estimate the user's emotions and determine the priority of scenarios to be input based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes input of simple and short scenarios. For example, the reception unit reduces the number of scenario items to be input by the user and prompts the user to input using simple questions. Furthermore, when the user is relaxed, the reception unit prioritizes input of detailed and complex scenarios. For example, the reception unit presents the user with questions that request detailed explanations of the scenario. Furthermore, when the user is concentrating, the reception unit prioritizes input of important scenarios. For example, when the user inputs a scenario, the reception unit automatically displays the next input item to support smooth input. This allows the reception unit to determine the priority of scenarios according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can support the input of scenarios using an AI model that uses the user's facial expression data as input, estimates emotions, and determines the priority of scenarios.

[0068] When inputting a scenario, the reception unit can prioritize inputting a highly relevant scenario based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting a scenario related to that area. For example, when the user is in an urban area, the reception unit suggests a scenario related to the urban area. Furthermore, when the user is traveling, the reception unit can prioritize inputting a scenario related to the travel destination. For example, when the user is in a tourist destination, the reception unit suggests a scenario related to the tourist destination. Furthermore, when the user is at home, the reception unit can prioritize inputting a scenario related to the home. For example, the reception unit suggests a scenario related to an event that occurs at home. This allows the reception unit to prioritize inputting a highly relevant scenario based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can support input of a scenario using an AI model that suggests highly relevant scenarios using the user's geographical location information as input.

[0069] When inputting a scenario, the reception unit can analyze the user's social media activity and input a related scenario. The reception unit can, for example, suggest related scenarios based on content shared by the user on social media. For example, if the user posts on social media about "environmental protection," the reception unit can suggest scenarios related to environmental protection. The reception unit can also analyze the content of the user's social media posts and input scenarios related to the user's interests. For example, if the user posts about "the importance of helping friends," the reception unit can suggest related scenarios. The reception unit can also input related scenarios based on the activities of the user's social media followers and friends. For example, if the user's followers are interested in "environmental protection," the reception unit can suggest related scenarios. This allows the reception unit to input related scenarios based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can support the input of scenarios using an AI model that uses the user's social media activity data as input and suggests related scenarios.

[0070] The generation unit can estimate the user's emotions and adjust the animation expression method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates animation with calm colors and slow movements. For example, the generation unit slows the character's movements and sets the background color to a calm tone. Furthermore, if the user is excited, the generation unit can generate animation with vivid colors and fast movements. For example, the generation unit speeds up the character's movements and sets the background color to a bright tone. Furthermore, if the user is sad, the generation unit can generate animation with calm colors and slow movements. For example, the generation unit slows the character's movements and sets the background color to a calm tone. This allows the generation unit to adjust the animation expression method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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 such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can generate animations using an AI model that takes a user's facial expression data as input, estimates emotions, and adjusts the way the animation is expressed.

[0071] The generation unit can adjust the level of detail of the animation based on the importance of the scenario during generation. For example, for an important scenario, the generation unit generates detailed backgrounds and character movements. For example, the generation unit depicts the background in detail and programs the character movements in detail in important scenes of the scenario. The generation unit can also generate simplified backgrounds and character movements in less important scenarios. For example, the generation unit simplifies the background and simplifies the character movements in scenes of less importance of the scenario. Furthermore, the generation unit can adjust the level of detail of animation effects and audio according to the importance of the scenario. For example, the generation unit sets detailed effects and audio in important scenarios to generate visually and aurally appealing animation. This allows the generation unit to adjust the level of detail of the animation according to the importance of the scenario. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate animation using an AI model that adjusts the level of detail of the animation using scenario importance data as input.

[0072] The generation unit can apply different generation algorithms depending on the scenario category during generation. For example, in the case of an educational scenario, the generation unit applies a generation algorithm that emphasizes educational elements. For example, in an educational scenario, the generation unit generates character movements and background settings that match the educational content. Furthermore, in the case of an entertainment scenario, the generation unit can apply a visually appealing generation algorithm. For example, in an entertainment scenario, the generation unit generates character movements and background settings that are visually appealing. Furthermore, in the case of an emotional scenario, the generation unit can apply a generation algorithm that enhances emotions. For example, in an emotional scenario, the generation unit generates character movements and background settings that enhance emotions. This allows the generation unit to apply an optimal generation algorithm depending on the scenario category. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate animation using an AI model that applies different generation algorithms using scenario category data as input.

[0073] The generation unit can estimate the user's emotions and adjust the length of the animation based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point animation. For example, the generation unit generates an animation that quickly conveys important points of a scenario. Furthermore, if the user is relaxed, the generation unit can generate a longer animation that includes detailed explanations. For example, the generation unit generates an animation that includes a detailed explanation of the scenario, allowing the user to watch it in a relaxed state. Furthermore, if the user is excited, the generation unit can generate an animation that adds visually stimulating effects. For example, the generation unit adds visually appealing effects to important scenes in the scenario to generate animation that enhances the user's excitement. This allows the generation unit to adjust the length of the animation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can generate animations using an AI model that takes a user's facial expression data as input, estimates emotions, and adjusts the length of the animation.

[0074] The generation unit can determine the priority of animations based on the submission dates of the scenarios at the time of generation. For example, for a scenario with an approaching deadline, the generation unit generates animations with priority. For example, if the submission deadline of a scenario is approaching, the generation unit processes that scenario with priority and generates animations. The generation unit can also postpone the generation of animations for a scenario with a distant submission date. For example, if the submission deadline of a scenario is still in the future, the generation unit postpones processing of that scenario. Furthermore, the generation unit can adjust the order of animation generation according to the submission dates. For example, the generation unit generates animations by determining the priority order taking into account the submission dates of multiple scenarios. This allows the generation unit to determine the priority of animations based on the submission dates of the scenarios. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate animations using an AI model that determines the priority of animations using scenario submission date data as input.

[0075] The generation unit can adjust the order of animations based on the relevance of the scenarios during generation. For example, the generation unit prioritizes animating highly relevant scenarios. For example, if the content of a scenario is highly relevant to other scenarios, the generation unit prioritizes processing the scenario and generates animation. The generation unit can also postpone animating less relevant scenarios. For example, if the content of a scenario is less relevant to other scenarios, the generation unit postpones processing the scenario. Furthermore, the generation unit can adjust the generation order of animations according to the relevance of the scenarios. For example, the generation unit generates animations by determining priorities based on the relevance of multiple scenarios. This allows the generation unit to adjust the order of animations based on the relevance of the scenarios. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate animations using an AI model that adjusts the order of animations using scenario relevance data as input.

[0076] The composition unit can estimate the user's emotions and adjust the animation composition method based on the estimated user's emotions. For example, if the user is relaxed, the composition unit composes animations with calm colors and slow movements. For example, the composition unit slows the character's movements and sets the background color to a calm tone. Furthermore, if the user is excited, the composition unit can compose animations with vivid colors and fast movements. For example, the composition unit speeds up the character's movements and sets the background color to a vivid tone. Furthermore, if the user is sad, the composition unit can compose animations with calm colors and slow movements. For example, the composition unit slows the character's movements and sets the background color to a calm tone. This allows the composition unit to adjust the animation composition method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the composition unit may be performed using, for example, an AI, or without an AI. For example, the composition unit can compose animations using an AI model that takes a user's facial expression data as input, estimates emotions, and adjusts the animation composition method.

[0077] During composition, the composition unit can analyze the user's past selection history to select the optimal composition method. For example, the composition unit prioritizes suggesting characters and backgrounds previously selected by the user. For example, the composition unit analyzes data on characters and backgrounds previously selected by the user and suggests similar characters and backgrounds. The composition unit can also automatically suggest components related to a specific theme based on the user's past selection history. For example, the composition unit suggests components related to a theme previously selected by the user. Furthermore, the composition unit can analyze the user's past selection history and provide a customized composition interface to improve composition efficiency. For example, the composition unit learns composition patterns previously selected by the user and suggests an efficient composition method. This allows the composition unit to provide the optimal composition method based on the user's past selection history. Some or all of the above-described processing in the composition unit may be performed, for example, using AI, or may be performed without AI. For example, the composition unit can compose an animation using an AI model that selects the optimal composition method using the user's past selection data as input.

[0078] The composition unit can customize components based on the user's current themes of interest during composition. For example, the composition unit can suggest characters and backgrounds related to the user's current themes of interest. For example, if the user is interested in "environmental protection," the composition unit can suggest characters and backgrounds related to environmental protection. The composition unit can also automatically complete related effects and sounds based on the user's themes of interest. For example, if the user is interested in "the importance of helping friends," the composition unit can automatically complete related effects and sounds and customize animations. Furthermore, the composition unit can customize components in real time based on the user's themes of interest and provide appropriate advice. For example, the composition unit can analyze the content of a scenario entered by the user and provide appropriate advice based on the themes of interest. This allows the composition unit to customize components based on the user's themes of interest. Some or all of the above-described processing in the composition unit may be performed using, for example, AI, or may be performed without AI. For example, the composition unit can use an AI model that customizes components using the user's themes of interest as input to compose an animation.

[0079] The composition unit can estimate the user's emotions and prioritize animation components based on the estimated user emotions. For example, when the user is stressed, the composition unit prioritizes simple and short components. For example, the composition unit reduces the number of items in the scenario to be input by the user and prompts the user to input using simple questions. The composition unit can also prioritize detailed and complex components when the user is relaxed. For example, the composition unit presents the user with a question requesting a detailed explanation of the scenario. Furthermore, when the user is concentrating, the composition unit can prioritize important components. For example, when the user inputs a scenario, the composition unit automatically displays the next input item to support smooth input. This allows the composition unit to prioritize components according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the composition unit can be performed using, for example, AI, or without AI. For example, the composition unit can use an AI model that takes a user's facial expression data as input, estimates emotions, and determines the priority of components to compose an animation.

[0080] During composition, the composition unit can select appropriate components based on the user's geographical location information. For example, if the user is in a specific area, the composition unit prioritizes selecting components related to that area. For example, if the user is in an urban area, the composition unit suggests components related to the urban area. Furthermore, if the user is traveling, the composition unit can prioritize selecting components related to the travel destination. For example, if the user is in a tourist destination, the composition unit suggests components related to the tourist destination. Furthermore, if the user is at home, the composition unit can prioritize selecting components related to the home. For example, the composition unit suggests components related to events that the user is experiencing at home. This allows the composition unit to select optimal components based on the user's geographical location information. Some or all of the above-described processing in the composition unit may be performed using, for example, AI, or may be performed without AI. For example, the composition unit can compose an animation using an AI model that selects appropriate components using the user's geographical location information as input.

[0081] During composition, the composition unit can analyze the user's social media activity to suggest components. For example, the composition unit can suggest related components based on content shared by the user on social media. For example, if the user posts on social media about "environmental protection," the composition unit can suggest components related to environmental protection. The composition unit can also analyze the content of the user's social media posts to suggest components related to topics of interest. For example, if the user posts about "the importance of helping friends," the composition unit can suggest related components. Furthermore, the composition unit can suggest related components based on the activities of the user's social media followers and friends. For example, if the user's followers are interested in "environmental protection," the composition unit can suggest related components. In this way, the composition unit can suggest related components based on the user's social media activity. Some or all of the above-described processing in the composition unit may be performed using, for example, AI, or may be performed without AI. For example, the composition unit can compose an animation using an AI model that suggests related components using the user's social media activity data as input.

[0082] The providing unit can estimate the user's emotion and adjust the animation presentation method based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can provide the animation along with calm music. For example, the providing unit can play calm music in the background while the animation is being played. Furthermore, if the user is excited, the providing unit can also provide the animation along with energetic music. For example, the providing unit can play energetic music in the background while the animation is being played. Furthermore, if the user is sad, the providing unit can also provide the animation along with calm music. For example, the providing unit can play calm music in the background while the animation is being played. This allows the providing unit to adjust the animation presentation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, 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 such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can provide animation using an AI model that uses a user's facial expression data as input, estimates emotions, and adjusts the method of providing the animation.

[0083] The providing unit can select the optimal providing method by referring to the user's past viewing history when providing the animation. For example, the providing unit can provide the animation in a similar style based on the style of animation the user has previously viewed. For example, the providing unit can analyze the style of animation the user has previously viewed and suggest animations of a similar style. The providing unit can also analyze the user's preferred genre from the user's past viewing history and provide the optimal animation. For example, the providing unit can analyze the genre the user has previously viewed and suggest animations of a similar genre. Furthermore, the providing unit can select a providing method suited to the viewing time period by referring to the user's past viewing history. For example, the providing unit can analyze the time period the user has previously viewed and provide animations at similar time periods. This allows the providing unit to select the optimal providing method based on the user's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the animation using an AI model that selects the optimal providing method using the user's past viewing data as input.

[0084] The providing unit can customize the provided content based on the user's current themes of interest at the time of providing. For example, the providing unit provides animations related to the user's current themes of interest. For example, if the user is interested in "environmental protection," the providing unit suggests animations related to environmental protection. The providing unit can also customize related effects and sounds based on the user's themes of interest. For example, if the user is interested in "the importance of helping friends," the providing unit customizes related effects and sounds and provides animations. Furthermore, the providing unit can customize the provided content in real time based on the user's themes of interest and provide appropriate advice. For example, the providing unit analyzes the content of the animation the user is watching and provides appropriate advice based on the themes of interest. This allows the providing unit to customize the provided content based on the user's themes of interest. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can provide animations using an AI model that customizes the provided content using the user's themes of interest as input.

[0085] The providing unit can estimate the user's emotions and determine the order in which animations are provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing relaxing animations. For example, the providing unit can adjust the order of animations viewed by the user to provide animations with relaxing content first. The providing unit can also prioritize providing detailed and complex animations when the user is relaxed. For example, the providing unit can adjust the order of animations viewed by the user to provide animations with detailed and complex content. Furthermore, the providing unit can prioritize providing important animations when the user is concentrating. For example, the providing unit can adjust the order of animations viewed by the user to provide animations with important content. This allows the providing unit to determine the order in which animations are provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can provide animations using an AI model that uses a user's facial expression data as input, estimates emotions, and determines the order in which animations are provided.

[0086] The providing unit can select an appropriate delivery method based on the user's geographical location information when providing the animation. For example, if the user is in a specific area, the providing unit can prioritize providing animations related to that area. For example, if the user is in an urban area, the providing unit can suggest animations related to the urban area. Furthermore, if the user is traveling, the providing unit can prioritize providing animations related to the travel destination. For example, if the user is in a tourist destination, the providing unit can suggest animations related to the tourist destination. Furthermore, if the user is at home, the providing unit can prioritize providing animations related to home life. For example, the providing unit can suggest animations related to events that occur at home. This allows the providing unit to select an optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide animations using an AI model that selects an appropriate delivery method using the user's geographical location information as input.

[0087] The providing unit can analyze the user's social media activity and suggest content to be provided. For example, the providing unit can suggest related animations based on content shared by the user on social media. For example, if the user posts on social media about "environmental protection," the providing unit can suggest animations related to environmental protection. The providing unit can also analyze the user's social media posts and provide animations related to themes of interest. For example, if the user posts about "the importance of helping friends," the providing unit can suggest related animations. Furthermore, the providing unit can provide related animations based on the activities of the user's social media followers and friends. For example, if the user's followers are interested in "environmental protection," the providing unit can suggest related animations. This allows the providing unit to suggest related animations based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can provide animations using an AI model that uses the user's social media activity data as input and suggests related animations. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, configuration 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 control unit 46A of the smart device 14, and allows a user to input an animation scenario. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the scenario using a generation AI to generate animation scenes. The configuration unit is realized, for example, by the control unit 46A of the smart device 14, and configures the generated animation. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the animation created by the user so that it can be viewed. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, configuration 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 control unit 46A of the smart glasses 214, and allows a user to input an animation scenario. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the scenario using a generation AI to generate animation scenes. The configuration unit is realized, for example, by the control unit 46A of the smart glasses 214, and configures the generated animation. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the animation created by the user so that it can be viewed. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, configuration 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 control unit 46A of the headset type terminal 314, and allows the user to input an animation scenario. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the scenario using a generation AI to generate animation scenes. The configuration unit is realized, for example, by the control unit 46A of the headset type terminal 314, and configures the generated animation. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the animation created by the user so that it can be viewed. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, configuration 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 control unit 46A of the robot 414, and allows a user to input an animation scenario. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the scenario using a generation AI to generate animation scenes. The configuration unit is realized, for example, by the control unit 46A of the robot 414, and configures the generated animation. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the animation created by the user so that it can be viewed.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reception unit can analyze the user's input content in real time and provide appropriate feedback. For example, while the user is inputting a scenario, the reception unit analyzes the content and points out grammatical errors and unnatural expressions. The reception unit can also provide related reference materials and advice based on the content of the scenario input by the user. For example, if the user is inputting a scenario on the theme of "the importance of helping friends," the reception unit can suggest related moral anecdotes and quotes. Furthermore, the reception unit can analyze the user's input speed and patterns and provide a customized interface to improve input efficiency. For example, the reception unit can predict frequently used phrases and words and provide an auto-completion function. This allows the reception unit to analyze the user's input content in real time and provide appropriate feedback.

[0090] The generation unit can generate a character's voice based on the content of the scenario. For example, the generation unit generates a character's voice based on the lines of the scenario and incorporates it into the animation. The generation unit can also generate a voice expression according to the character's emotion. For example, in a scene where a character is angry, the generation unit generates a voice that expresses anger. Furthermore, the generation unit can generate background sounds and sound effects based on the content of the scenario. For example, if a scene takes place in a park, the generation unit generates sound effects such as the ambient sounds of the park and birds chirping. This allows the generation unit to generate a character's voice and background sounds based on the content of the scenario.

[0091] The composition unit can compose animations using original materials uploaded by the user in addition to a library of characters and backgrounds selected by the user. For example, the user can upload characters and backgrounds they have drawn themselves and incorporate them into the animation. The composition unit can also add audio and music uploaded by the user to the animation. For example, the composition unit can incorporate narration and background music recorded by the user into the animation. Furthermore, the composition unit can provide editing functions such as color correction and effect application for materials uploaded by the user. For example, the composition unit can adjust the color tone of images uploaded by the user or add specific effects. This allows the composition unit to compose animations using original materials uploaded by the user.

[0092] The providing unit can simultaneously distribute animations created by users across multiple platforms. For example, the providing unit can upload the animations to video sharing sites such as YouTube and Vimeo and simultaneously share them on social media. The providing unit can also distribute animations created by users across internal networks of educational institutions and companies. For example, the providing unit can distribute animations to a school's online class platform or a company's intranet. The providing unit can also provide a function for exporting animations created by users to physical media such as DVDs and Blu-rays. For example, the providing unit can export animations in high quality so that users can view them at home. This allows the providing unit to simultaneously distribute animations created by users across multiple platforms.

[0093] The providing unit can collect and analyze viewer feedback on animations created by users. For example, the providing unit provides a function that allows viewers to leave ratings and comments after watching an animation. The providing unit can also analyze viewer feedback and provide improvements and advice to users. For example, the providing unit can analyze viewer ratings and comments and inform users which parts of the animation were well received and which parts need improvement. Furthermore, the providing unit can provide hints and suggestions that will be useful for creating the next animation based on viewer feedback. For example, the providing unit can suggest ideas for the next scenario or character settings based on viewer feedback. In this way, the providing unit can collect and analyze viewer feedback on animations created by users.

[0094] The reception unit can estimate the user's emotions and provide scenario input support based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit simplifies the scenario input, allowing the user to input the scenario in a short amount of time. For example, the reception unit reduces the number of scenario items to be input by the user and prompts the user to input them in the form of simple questions. Furthermore, when the user is relaxed, the reception unit prompts the user to input a detailed scenario, allowing the user to input more information. For example, the reception unit presents the user with questions that require a detailed explanation of the scenario. Furthermore, when the user is concentrating, the reception unit allows the user to input the scenario continuously, minimizing interruptions to the work. For example, the reception unit automatically displays the next input item when the user is inputting a scenario, supporting smooth input. In this way, the reception unit can provide scenario input support according to the user's emotions.

[0095] The generation unit can estimate the user's emotions and adjust the animation expression method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates animation with calm colors and slow movements. For example, the generation unit makes the character's movements slow and the background colors calm. Furthermore, if the user is excited, the generation unit can generate animation with vivid colors and fast movements. For example, the generation unit makes the character's movements fast and the background colors calm. Furthermore, if the user is sad, the generation unit can generate animation with muted colors and slow movements. For example, the generation unit makes the character's movements slow and the background colors calm. In this way, the generation unit can adjust the animation expression method according to the user's emotions.

[0096] The composition unit can estimate the user's emotions and adjust the animation composition method based on the estimated user's emotions. For example, if the user is relaxed, the composition unit can compose animation with calm colors and slow movements. For example, the composition unit can make the character's movements slow and the background colors calm. If the user is excited, the composition unit can also compose animation with vivid colors and fast movements. For example, the composition unit can make the character's movements fast and the background colors calm. If the user is sad, the composition unit can also compose animation with muted colors and slow movements. For example, the composition unit can make the character's movements slow and the background colors calm. This allows the composition unit to adjust the animation composition method according to the user's emotions.

[0097] The providing unit can estimate the user's emotion and adjust the way in which the animation is presented based on the estimated user's emotion. For example, if the user is relaxed, the animation is presented along with calm music. For example, the providing unit plays calm music in the background while the animation is being played. Furthermore, if the user is excited, the providing unit can also present the animation along with energetic music. For example, the providing unit plays energetic music in the background while the animation is being played. Furthermore, if the user is sad, the providing unit can also present the animation along with calm music. For example, the providing unit plays calm music in the background while the animation is being played. In this way, the providing unit can adjust the way in which the animation is presented according to the user's emotion.

[0098] The providing unit can estimate the user's emotions and determine the order in which animations are provided based on the estimated user's emotions. For example, if the user is feeling stressed, relaxing animations can be provided preferentially. For example, the providing unit can adjust the order of animations viewed by the user to provide animations with relaxing content first. Furthermore, if the user is relaxed, the providing unit can also provide detailed and complex animations preferentially. For example, the providing unit can adjust the order of animations viewed by the user to provide animations with detailed and complex content. Furthermore, if the user is concentrating, the providing unit can also provide important animations preferentially. For example, the providing unit can adjust the order of animations viewed by the user to provide animations with important content. In this way, the providing unit can determine the order in which animations are provided according to the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The user inputs an anime scenario into the reception unit. For example, the user can input a scenario with the theme "The importance of helping friends." Step 2: The generator uses the generation AI to analyze the scenario received by the reception unit and generate animation scenes. For example, the generation AI programs the character's movements, facial expressions, background changes, etc. based on the content of the scenario. Step 3: The composition unit composes the animation generated by the generation unit, for example, based on elements selected from a library of characters and backgrounds selected by the user. Step 4: The providing unit provides the animation generated by the composing unit, for example, providing the animation created by the user so that the user can view it.

[0101] 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.

[0102] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[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] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a scenario; a generation unit that analyzes the scenario received by the reception unit and generates animation scenes; a configuration unit that configures the animation generated by the generation unit; a providing unit that provides the animation generated by the configuration unit. A system characterized by:

2. The reception unit Accepts text, voice, and image input 2. The system of claim 1.

3. The generation unit Generate character movements, facial expressions, and background changes based on the content of the scenario 2. The system of claim 1.

4. The component comprises: Compose animations based on elements selected from a user-selected library of characters and backgrounds 2. The system of claim 1.

5. The providing unit Providing user-created animations for viewing 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the input timing of the scenario based on the estimated user emotions.

2. The system of claim 1.

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

8. The reception unit Filtering scenarios based on the user's current themes of interest as they are entered 2. The system of claim 1.

9. The reception unit Estimate the user's emotions and prioritize the input scenarios based on the estimated user emotions.

2. The system of claim 1.

10. The reception unit When entering scenarios, prioritize the most relevant scenarios based on the user's geographic location.

2. The system of claim 1.

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