system

Abstract

The system according to the embodiment aims to efficiently generate animations based on images expressed in natural language by a user. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives an image in natural language from a user. The generating unit analyzes the image received by the receiving unit and generates variations of animation. The providing unit provides the animation generated by the generating 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] With conventional technology, it was difficult to generate animation based on the user's natural language image, making it difficult to produce animation efficiently.

[0005] The system according to the embodiment aims to efficiently generate animations based on images expressed in natural language by a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives an image in natural language from a user. The generating unit analyzes the image received by the receiving unit and generates variations of animation. The providing unit provides the animation generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate animations based on images expressed in natural language by a user. [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 business animation production system according to an embodiment of the present invention uses a generative AI to generate business animations. This system allows a user to input an image of an animation in natural language, analyzes the image, generates multiple variations of animation, and provides the generated animation to the user. For example, if a user inputs, "I want to create a business meeting scene," the generative AI automatically generates the background of the meeting room and the movements of the characters. The generative AI analyzes the user's input and generates an animation scenario and character movements. The generated animation has high resolution and a high frame rate, and the generation process is fast and efficient. This allows the business animation production system to quickly and efficiently generate and provide a variety of animations based on the user's natural language input.

[0029] A business animation production system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an image in natural language from a user. For example, if a user inputs, "I want to create a business meeting scene," the reception unit can receive the image. The generation unit analyzes the image received by the reception unit and generates variations of animation. The generation unit can generate an animation scenario based on the user's input, for example, using a generation AI. The generation unit can also generate character movements based on the user's input. The generation unit can generate animation with high resolution and a high frame rate using the generation AI. Furthermore, the generation unit can generate animation quickly and efficiently. The provision unit provides the animation generated by the generation unit. The provision unit can, for example, visually display the generated animation. This allows the business animation production system to generate and provide animation based on the user's natural language input.

[0030] The generation unit may include a scenario generation unit that generates an animation scenario based on user input. The scenario generation unit, for example, analyzes natural language images input by the user and generates an animation scenario. For example, if the user inputs, "I want to create a business meeting scene," the scenario generation unit can generate a scenario including the progress of the meeting and the dialogue between the characters. The scenario generation unit can also adjust the details of the scenario based on the user input using a generation AI. For example, the scenario generation unit can adjust the length and content of the scenario based on the user input. This makes it possible to generate an animation scenario based on the user input.

[0031] The generation unit may include a character generation unit that generates character movements based on user input. The character generation unit, for example, analyzes natural language images input by the user and generates character movements. For example, if a user inputs, "I want to create a business meeting scene," the character generation unit can generate movements of the characters participating in the meeting. The character generation unit can also adjust the details of the character movements based on the user input using a generation AI. For example, the character generation unit can adjust the speed and facial expressions of the character movements based on the user input. This makes it possible to generate character movements based on the user input.

[0032] The providing unit may include a display unit that visually displays the generated animation. The display unit may visually display the generated animation, for example, on a display, a projector, a VR headset, or the like. For example, the display unit may display the generated animation in high resolution to provide visual feedback to the user. The display unit may also display the generated animation in real time. This allows the generated animation to be visually displayed.

[0033] The generation unit can generate animations with high resolution and high frame rates. The generation unit can generate animations with high resolution, such as 4K or 8K. The generation unit can also generate animations with high frame rates, such as 30 fps or 60 fps. For example, the generation unit can generate animations with high resolution and high frame rates using a generation AI. This makes it possible to generate animations with high resolution and high frame rates.

[0034] The generation unit can generate animations quickly and efficiently. The generation unit can generate animations quickly and efficiently, for example, by using a generation AI. For example, the generation unit can generate animations in a short time using the generation AI, thereby optimizing resource usage. The generation unit can also optimize processing using the generation AI to efficiently generate animations. This allows for fast and efficient generation of animations.

[0035] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit can, for example, analyze the user's past input history and select the optimal reception method. For example, the reception unit can preferentially suggest an input method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and select the most efficient reception method. This makes it possible to select the optimal reception method based on the user's past input history.

[0036] The reception unit can filter images based on the user's current project or area of ​​interest when receiving the images. The reception unit can filter images based on the user's current project or area of ​​interest, for example. For example, the reception unit can preferentially receive images related to a project currently in progress by the user. The reception unit can also filter and receive highly relevant images based on the user's area of ​​interest. Furthermore, the reception unit can select and receive appropriate images according to the progress of the user's project. This makes it possible to filter images based on the user's current project or area of ​​interest.

[0037] When receiving images, the reception unit can preferentially receive highly relevant images taking into account the user's geographical location information. For example, the reception unit can preferentially receive highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can preferentially receive images related to that area. The reception unit can also filter and receive highly relevant images based on the user's current location. Furthermore, the reception unit can select and receive optimal images taking into account the user's geographical location information. This makes it possible to preferentially receive highly relevant images based on the user's geographical location information.

[0038] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. The reception unit can, for example, analyze the user's social media activity and receive related images. For example, the reception unit can preferentially receive related images based on content shared by the user on social media. The reception unit can also analyze the user's social media activity and filter and receive images that match the user's interests. Furthermore, the reception unit can select and receive optimal images in consideration of the user's social media activity history. This makes it possible to receive related images based on the user's social media activity.

[0039] The generation unit may adjust the level of detail of the generation based on the importance of the animation during generation. The generation unit may adjust the level of detail of the generation based on, for example, the importance of the animation. For example, the generation unit may depict important animations in detail. The generation unit may also generate animations with low importance in a simplified manner. Furthermore, the generation unit may adjust the level of detail of backgrounds and characters according to the importance. In this way, the level of detail of the generation may be adjusted based on the importance of the animation.

[0040] The generation unit can apply different generation algorithms depending on the category of the animation during generation. For example, the generation unit can apply different generation algorithms depending on the category of the animation. For example, the generation unit can apply a formal style generation algorithm to animation for business presentations. The generation unit can also apply a visually easy-to-understand style generation algorithm to animation for educational purposes. Furthermore, the generation unit can apply a dynamic and fun style generation algorithm to animation for entertainment purposes. In this way, different generation algorithms can be applied depending on the category of the animation.

[0041] The generation unit can determine the generation priority based on the submission time of the animation at the time of generation. The generation unit can determine the generation priority based on, for example, the submission time of the animation. For example, the generation unit can prioritize the generation of animations with an upcoming submission deadline. The generation unit can also postpone the generation of animations with a distant submission deadline. Furthermore, the generation unit can adjust the order of generation according to the submission time. In this way, the generation priority can be determined based on the submission time of the animation.

[0042] The generation unit can adjust the order of generation based on the relevance of the animations at the time of generation. The generation unit can adjust the order of generation based on, for example, the relevance of the animations. For example, the generation unit can preferentially generate animations that are highly relevant to the user's project. The generation unit can also postpone animations that are less relevant. Furthermore, the generation unit can adjust the order of generation according to the relevance. This makes it possible to adjust the order of generation based on the relevance of the animations.

[0043] The providing unit can select the optimal display method by referring to the user's past viewing history when providing the display. The providing unit can select the optimal display method by referring to the user's past viewing history, for example. For example, the providing unit can select the optimal display method based on the style of animation that the user has previously viewed. The providing unit can also suggest a preferred display method based on the user's past viewing history. Furthermore, the providing unit can analyze the user's viewing history and select the display method with the highest visibility. This makes it possible to select the optimal display method based on the user's past viewing history.

[0044] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display information. The providing unit can select the optimal display method by taking into consideration the user's device information, for example. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information.

[0045] When providing animations, the providing unit can prioritize providing highly relevant animations by taking into account the user's geographical location information. The providing unit can, for example, prioritize providing highly relevant animations by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing animations related to that area. The providing unit can also filter and provide highly relevant animations based on the user's current location. Furthermore, the providing unit can select and provide optimal animations by taking into account the user's geographical location information. This makes it possible to prioritize providing highly relevant animations based on the user's geographical location information.

[0046] The providing unit can analyze the user's social media activity and provide related animations at the time of providing. The providing unit can, for example, analyze the user's social media activity and provide related animations. For example, the providing unit can preferentially provide related animations based on content shared by the user on social media. The providing unit can also analyze the user's social media activity and filter and provide animations that match the user's interests. Furthermore, the providing unit can select and provide the most appropriate animations in consideration of the user's social media activity history. This makes it possible to provide related animations based on the user's social media activity.

[0047] The scenario generation unit can adjust the level of detail of the scenario based on the importance of the animation when generating a scenario. The scenario generation unit can adjust the level of detail of the scenario based on, for example, the importance of the animation. For example, the scenario generation unit can generate a detailed scenario for an important animation. Also, the scenario generation unit can generate a simplified scenario for an animation with low importance. Furthermore, the scenario generation unit can adjust the details of the scenario according to the importance. In this way, the level of detail of the scenario can be adjusted based on the importance of the animation.

[0048] The scenario generation unit can apply different scenario generation algorithms depending on the category of animation when generating a scenario. The scenario generation unit can apply different scenario generation algorithms depending on, for example, the category of animation. For example, the scenario generation unit can apply a formal style generation algorithm to a scenario for a business presentation. The scenario generation unit can also apply a visually easy-to-understand style generation algorithm to an educational scenario. Furthermore, the scenario generation unit can apply a dynamic and fun style generation algorithm to an entertainment scenario. In this way, different scenario generation algorithms can be applied depending on the category of animation.

[0049] When generating a scenario, the scenario generation unit can determine the priority of the scenario based on the submission time of the animation. The scenario generation unit can determine the priority of the scenario based on, for example, the submission time of the animation. For example, the scenario generation unit can generate a scenario with priority for an animation whose submission deadline is approaching. The scenario generation unit can also postpone a scenario for an animation whose submission deadline is far away. Furthermore, the scenario generation unit can adjust the order in which scenarios are generated depending on the submission time. This makes it possible to determine the priority of the scenario based on the submission time of the animation.

[0050] The scenario generation unit can adjust the order of scenarios based on the relevance of animations when generating scenarios. The scenario generation unit can adjust the order of scenarios based on, for example, the relevance of animations. For example, the scenario generation unit can generate scenarios that are highly relevant to the user's project with priority. The scenario generation unit can also postpone scenarios that are less relevant. Furthermore, the scenario generation unit can adjust the order of scenario generation according to the relevance. This makes it possible to adjust the order of scenarios based on the relevance of animations.

[0051] The character generation unit can adjust the level of detail of the character's movements based on the importance of the animation when generating the character. The character generation unit can adjust the level of detail of the character's movements based on, for example, the importance of the animation. For example, the character generation unit can generate a character that is depicted in great detail for an important animation. The character generation unit can also generate a simplified character for an animation that is less important. Furthermore, the character generation unit can adjust the level of detail of the character's movements based on the importance. This allows the level of detail of the character's movements to be adjusted based on the importance of the animation.

[0052] The character generation unit can apply different character generation algorithms depending on the animation category when generating a character. For example, the character generation unit can apply different character generation algorithms depending on the animation category. For example, the character generation unit can apply a formal style generation algorithm to a character for a business presentation. The character generation unit can also apply a visually easy-to-understand style generation algorithm to an educational character. Furthermore, the character generation unit can apply a dynamic and fun style generation algorithm to an entertainment character. In this way, different character generation algorithms can be applied depending on the animation category.

[0053] When generating a character, the character generation unit can determine the priority of character movements based on the time of submission of the animation. The character generation unit can determine the priority of character movements based on, for example, the time of submission of the animation. For example, the character generation unit can prioritize the generation of characters of animations whose submission deadlines are approaching. The character generation unit can also postpone the generation of characters of animations whose submission deadlines are further away. Furthermore, the character generation unit can adjust the order in which character movements are generated depending on the time of submission. This makes it possible to determine the priority of character movements based on the time of submission of the animation.

[0054] The character generation unit can adjust the order of character movements based on the relevance of the animations when generating a character. The character generation unit can adjust the order of character movements based on, for example, the relevance of the animations. For example, the character generation unit can prioritize the generation of movements of characters that are highly relevant to the user's project. The character generation unit can also postpone the generation of movements of characters that are less relevant. Furthermore, the character generation unit can adjust the generation order of character movements based on the relevance. This makes it possible to adjust the order of character movements based on the relevance of the animations.

[0055] The display unit can select the optimal display method by referring to the user's past viewing history when displaying. The display unit can select the optimal display method by referring to the user's past viewing history, for example. For example, the display unit can select the optimal display method based on the style of animation that the user has viewed in the past. The display unit can also suggest a preferred display method based on the user's past viewing history. Furthermore, the display unit can analyze the user's viewing history and select the display method with the highest visibility. This makes it possible to select the optimal display method based on the user's past viewing history.

[0056] The display unit can select the optimal display method when displaying, taking into consideration the user's device information. The display unit can select the optimal display method, for example, taking into consideration the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This makes it possible to select the optimal display method based on the user's device information.

[0057] The display unit can prioritize displaying highly relevant animations in consideration of the user's geographical location information when displaying the animations. For example, the display unit can prioritize displaying highly relevant animations in consideration of the user's geographical location information. For example, if the user is in a specific area, the display unit can prioritize displaying animations related to that area. The display unit can also filter and display highly relevant animations based on the user's current location. Furthermore, the display unit can select and display optimal animations in consideration of the user's geographical location information. This makes it possible to prioritize displaying highly relevant animations based on the user's geographical location information.

[0058] The display unit can analyze the user's social media activity and display related animations when displaying the animations. For example, the display unit can analyze the user's social media activity and display related animations. For example, the display unit can prioritize displaying related animations based on content shared by the user on social media. The display unit can also analyze the user's social media activity and filter and display animations that match the user's interests. Furthermore, the display unit can select and display the most appropriate animations in consideration of the user's social media activity history. This makes it possible to display related animations based on the user's social media activity.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's voice input and convert natural language images into text. For example, if the user voice-inputs, "I want to create a scene of a business meeting," the reception unit can convert the voice into text and send it to the generation unit. The reception unit can also use voice recognition technology to analyze the user's speaking rate and intonation to more accurately understand the user's intentions. Furthermore, the reception unit supports multiple languages ​​and can automatically translate and convert voice input in different languages ​​into text. This allows the user's voice input to be analyzed and natural language images to be converted into text.

[0061] The generation unit may include a background generation unit that generates an animated background based on user input. For example, when a user inputs, "I want to create a business meeting scene," the background generation unit can automatically generate a conference room background. The background generation unit can also adjust details of the background based on the user input. For example, the background generation unit can adjust the layout and decoration of the conference room based on the user input. Furthermore, the background generation unit can generate a background in real time using generative AI and provide visual feedback to the user. This allows the animated background to be generated based on the user input.

[0062] The providing unit can optimize the generated animation for the user's device and display it. For example, the providing unit can display the animation at an optimal resolution and frame rate for different devices, such as a smartphone, a tablet, and a desktop. The providing unit can also automatically adjust the quality of the animation according to the performance of the user's device. Furthermore, the providing unit can adjust the layout of the animation to match the screen size of the user's device to improve visibility. This allows the generated animation to be optimized for the user's device and displayed.

[0063] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can preferentially suggest input methods that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and select the most efficient input method. This makes it possible to suggest the optimal input method based on the user's past input history.

[0064] The providing unit may have a function for directly sharing the generated animation to the user's social media account. For example, the providing unit may directly post the generated animation to social media platforms such as Facebook (registered trademark), X (formerly Twitter (registered trademark)), and Instagram (registered trademark). The providing unit may also share the animation with specific groups or followers selected by the user. Furthermore, the providing unit may collect user reactions to social media posts and reflect them in the next generation of animation. This allows the generated animation to be directly shared to the user's social media account.

[0065] The provider can integrate the generated animation with the user's project management tool. For example, the provider can directly add the generated animation to a project management tool such as Trello, Asana, or Jira. The provider can also automatically update the animation according to the progress of the project. Furthermore, the provider can share the animation with project team members and collect feedback. This allows the generated animation to be integrated with the user's project management tool.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives an image in natural language from the user. For example, if the user inputs "I want to create a scene of a business meeting," the reception unit can receive the image. Step 2: The generation unit analyzes the image received by the reception unit and generates variations of the animation. The generation unit uses generation AI to generate animation scenarios and character movements based on user input, enabling high-resolution, high-frame-rate animation to be generated quickly and efficiently. Step 3: The providing unit provides the animation generated by the generating unit, and the providing unit can visually display the generated animation.

[0068] (Example 2) A business animation production system according to an embodiment of the present invention uses a generative AI to generate business animations. This system allows a user to input an image of an animation in natural language, analyzes the image, generates multiple variations of animation, and provides the generated animation to the user. For example, if a user inputs, "I want to create a business meeting scene," the generative AI automatically generates the background of the meeting room and the movements of the characters. The generative AI analyzes the user's input and generates an animation scenario and character movements. The generated animation has high resolution and a high frame rate, and the generation process is fast and efficient. This allows the business animation production system to quickly and efficiently generate and provide a variety of animations based on the user's natural language input.

[0069] A business animation production system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an image in natural language from a user. For example, if a user inputs, "I want to create a business meeting scene," the reception unit can receive the image. The generation unit analyzes the image received by the reception unit and generates variations of animation. The generation unit can generate an animation scenario based on the user's input, for example, using a generation AI. The generation unit can also generate character movements based on the user's input. The generation unit can generate animation with high resolution and a high frame rate using the generation AI. Furthermore, the generation unit can generate animation quickly and efficiently. The provision unit provides the animation generated by the generation unit. The provision unit can, for example, visually display the generated animation. This allows the business animation production system to generate and provide animation based on the user's natural language input.

[0070] The generation unit may include a scenario generation unit that generates an animation scenario based on user input. The scenario generation unit, for example, analyzes natural language images input by the user and generates an animation scenario. For example, if the user inputs, "I want to create a business meeting scene," the scenario generation unit can generate a scenario including the progress of the meeting and the dialogue between the characters. The scenario generation unit can also adjust the details of the scenario based on the user input using a generation AI. For example, the scenario generation unit can adjust the length and content of the scenario based on the user input. This makes it possible to generate an animation scenario based on the user input.

[0071] The generation unit may include a character generation unit that generates character movements based on user input. The character generation unit, for example, analyzes natural language images input by the user and generates character movements. For example, if a user inputs, "I want to create a business meeting scene," the character generation unit can generate movements of the characters participating in the meeting. The character generation unit can also adjust the details of the character movements based on the user input using a generation AI. For example, the character generation unit can adjust the speed and facial expressions of the character movements based on the user input. This makes it possible to generate character movements based on the user input.

[0072] The providing unit may include a display unit that visually displays the generated animation. The display unit may visually display the generated animation, for example, on a display, a projector, a VR headset, or the like. For example, the display unit may display the generated animation in high resolution to provide visual feedback to the user. The display unit may also display the generated animation in real time. This allows the generated animation to be visually displayed.

[0073] The generation unit can generate animations with high resolution and high frame rates. The generation unit can generate animations with high resolution, such as 4K or 8K. The generation unit can also generate animations with high frame rates, such as 30 fps or 60 fps. For example, the generation unit can generate animations with high resolution and high frame rates using a generation AI. This makes it possible to generate animations with high resolution and high frame rates.

[0074] The generation unit can generate animations quickly and efficiently. The generation unit can generate animations quickly and efficiently, for example, by using a generation AI. For example, the generation unit can generate animations in a short time using the generation AI, thereby optimizing resource usage. The generation unit can also optimize processing using the generation AI to efficiently generate animations. This allows for fast and efficient generation of animations.

[0075] The reception unit can estimate the user's emotions and adjust the timing of receiving images based on the estimated user emotions. The reception unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is feeling stressed, the reception unit can delay the timing of receiving images to relax the user. Furthermore, if the user is relaxed, the reception unit can immediately receive images and start processing quickly. Furthermore, if the user is in a hurry, the reception unit can advance the timing of receiving images to quickly receive images. This makes it possible to adjust the timing of receiving images based on the user's emotions.

[0076] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit can, for example, analyze the user's past input history and select the optimal reception method. For example, the reception unit can preferentially suggest an input method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and select the most efficient reception method. This makes it possible to select the optimal reception method based on the user's past input history.

[0077] The reception unit can filter images based on the user's current project or area of ​​interest when receiving the images. The reception unit can filter images based on the user's current project or area of ​​interest, for example. For example, the reception unit can preferentially receive images related to a project currently in progress by the user. The reception unit can also filter and receive highly relevant images based on the user's area of ​​interest. Furthermore, the reception unit can select and receive appropriate images according to the progress of the user's project. This makes it possible to filter images based on the user's current project or area of ​​interest.

[0078] The reception unit can estimate the user's emotion and determine the priority of images to be received based on the estimated user's emotion. The reception unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is feeling stressed, the reception unit can prioritize receiving relaxing images. Furthermore, if the user is relaxed, the reception unit can prioritize receiving important images. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving images that require quick processing. In this way, the priority of images to be received can be determined based on the user's emotion.

[0079] When receiving images, the reception unit can preferentially receive highly relevant images taking into account the user's geographical location information. For example, the reception unit can preferentially receive highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can preferentially receive images related to that area. The reception unit can also filter and receive highly relevant images based on the user's current location. Furthermore, the reception unit can select and receive optimal images taking into account the user's geographical location information. This makes it possible to preferentially receive highly relevant images based on the user's geographical location information.

[0080] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. The reception unit can, for example, analyze the user's social media activity and receive related images. For example, the reception unit can preferentially receive related images based on content shared by the user on social media. The reception unit can also analyze the user's social media activity and filter and receive images that match the user's interests. Furthermore, the reception unit can select and receive optimal images in consideration of the user's social media activity history. This makes it possible to receive related images based on the user's social media activity.

[0081] The generation unit can estimate the user's emotion and adjust the style of the generated animation based on the estimated user's emotion. The generation unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can generate animation with soft colors and slow movements when the user is relaxed. The generation unit can also generate animation with bright colors and fast movements when the user is in a hurry. Furthermore, the generation unit can generate animation with vivid colors and dynamic movements when the user is excited. This makes it possible to adjust the style of the animation based on the user's emotion.

[0082] The generation unit may adjust the level of detail of the generation based on the importance of the animation during generation. The generation unit may adjust the level of detail of the generation based on, for example, the importance of the animation. For example, the generation unit may depict important animations in detail. The generation unit may also generate animations with low importance in a simplified manner. Furthermore, the generation unit may adjust the level of detail of backgrounds and characters according to the importance. In this way, the level of detail of the generation may be adjusted based on the importance of the animation.

[0083] The generation unit can apply different generation algorithms depending on the category of the animation during generation. For example, the generation unit can apply different generation algorithms depending on the category of the animation. For example, the generation unit can apply a formal style generation algorithm to animation for business presentations. The generation unit can also apply a visually easy-to-understand style generation algorithm to animation for educational purposes. Furthermore, the generation unit can apply a dynamic and fun style generation algorithm to animation for entertainment purposes. In this way, different generation algorithms can be applied depending on the category of the animation.

[0084] The generation unit can estimate the user's emotion and adjust the length of the generated animation based on the estimated user's emotion. The generation unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point animation. If the user is relaxed, the generation unit can also generate a longer animation including detailed explanations. Furthermore, if the user is excited, the generation unit can generate an animation with a visually stimulating effect. This allows the length of the animation to be adjusted based on the user's emotion.

[0085] The generation unit can determine the generation priority based on the submission time of the animation at the time of generation. The generation unit can determine the generation priority based on, for example, the submission time of the animation. For example, the generation unit can prioritize the generation of animations with an upcoming submission deadline. The generation unit can also postpone the generation of animations with a distant submission deadline. Furthermore, the generation unit can adjust the order of generation according to the submission time. In this way, the generation priority can be determined based on the submission time of the animation.

[0086] The generation unit can adjust the order of generation based on the relevance of the animations at the time of generation. The generation unit can adjust the order of generation based on, for example, the relevance of the animations. For example, the generation unit can preferentially generate animations that are highly relevant to the user's project. The generation unit can also postpone animations that are less relevant. Furthermore, the generation unit can adjust the order of generation according to the relevance. This makes it possible to adjust the order of generation based on the relevance of the animations.

[0087] The providing unit can estimate the user's emotion and adjust the display method of the animation to be provided based on the estimated user's emotion. The providing unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the animation based on the user's emotion.

[0088] The providing unit can select the optimal display method by referring to the user's past viewing history when providing the display. The providing unit can select the optimal display method by referring to the user's past viewing history, for example. For example, the providing unit can select the optimal display method based on the style of animation that the user has previously viewed. The providing unit can also suggest a preferred display method based on the user's past viewing history. Furthermore, the providing unit can analyze the user's viewing history and select the display method with the highest visibility. This makes it possible to select the optimal display method based on the user's past viewing history.

[0089] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display information. The providing unit can select the optimal display method by taking into consideration the user's device information, for example. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information.

[0090] The providing unit can estimate the user's emotion and determine the priority of animations to be provided based on the estimated user's emotion. The providing unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is feeling stressed, the providing unit can prioritize providing relaxing animations. Furthermore, if the user is relaxed, the providing unit can prioritize providing important animations. Furthermore, if the user is in a hurry, the providing unit can prioritize providing animations that require quick processing. In this way, the priority of animations can be determined based on the user's emotion.

[0091] When providing animations, the providing unit can prioritize providing highly relevant animations by taking into account the user's geographical location information. The providing unit can, for example, prioritize providing highly relevant animations by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing animations related to that area. The providing unit can also filter and provide highly relevant animations based on the user's current location. Furthermore, the providing unit can select and provide optimal animations by taking into account the user's geographical location information. This makes it possible to prioritize providing highly relevant animations based on the user's geographical location information.

[0092] The providing unit can analyze the user's social media activity and provide related animations at the time of providing. The providing unit can, for example, analyze the user's social media activity and provide related animations. For example, the providing unit can preferentially provide related animations based on content shared by the user on social media. The providing unit can also analyze the user's social media activity and filter and provide animations that match the user's interests. Furthermore, the providing unit can select and provide the most appropriate animations in consideration of the user's social media activity history. This makes it possible to provide related animations based on the user's social media activity.

[0093] The scenario generation unit can estimate the user's emotions and adjust the way a scenario is expressed based on the estimated user's emotions. The scenario generation unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the scenario generation unit can generate a scenario using a gentle expression method when the user is relaxed. Furthermore, the scenario generation unit can generate a concise and to-the-point scenario when the user is in a hurry. Furthermore, the scenario generation unit can generate a scenario using a visually stimulating expression method when the user is excited. This makes it possible to adjust the way a scenario is expressed based on the user's emotions.

[0094] The scenario generation unit can adjust the level of detail of the scenario based on the importance of the animation when generating a scenario. The scenario generation unit can adjust the level of detail of the scenario based on, for example, the importance of the animation. For example, the scenario generation unit can generate a detailed scenario for an important animation. Also, the scenario generation unit can generate a simplified scenario for an animation with low importance. Furthermore, the scenario generation unit can adjust the details of the scenario according to the importance. In this way, the level of detail of the scenario can be adjusted based on the importance of the animation.

[0095] The scenario generation unit can apply different scenario generation algorithms depending on the category of animation when generating a scenario. The scenario generation unit can apply different scenario generation algorithms depending on, for example, the category of animation. For example, the scenario generation unit can apply a formal style generation algorithm to a scenario for a business presentation. The scenario generation unit can also apply a visually easy-to-understand style generation algorithm to an educational scenario. Furthermore, the scenario generation unit can apply a dynamic and fun style generation algorithm to an entertainment scenario. In this way, different scenario generation algorithms can be applied depending on the category of animation.

[0096] The scenario generation unit can estimate the user's emotions and adjust the length of the scenario based on the estimated user emotions. The scenario generation unit can, for example, capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is in a hurry, the scenario generation unit can generate a short scenario that is concise. Also, if the user is relaxed, the scenario generation unit can generate a longer scenario that includes detailed explanations. Furthermore, if the user is excited, the scenario generation unit can generate a scenario that adds visually stimulating effects. This makes it possible to adjust the length of the scenario based on the user's emotions.

[0097] When generating a scenario, the scenario generation unit can determine the priority of the scenario based on the submission time of the animation. The scenario generation unit can determine the priority of the scenario based on, for example, the submission time of the animation. For example, the scenario generation unit can generate a scenario with priority for an animation whose submission deadline is approaching. The scenario generation unit can also postpone a scenario for an animation whose submission deadline is far away. Furthermore, the scenario generation unit can adjust the order in which scenarios are generated depending on the submission time. This makes it possible to determine the priority of the scenario based on the submission time of the animation.

[0098] The scenario generation unit can adjust the order of scenarios based on the relevance of animations when generating scenarios. The scenario generation unit can adjust the order of scenarios based on, for example, the relevance of animations. For example, the scenario generation unit can generate scenarios that are highly relevant to the user's project with priority. The scenario generation unit can also postpone scenarios that are less relevant. Furthermore, the scenario generation unit can adjust the order of scenario generation according to the relevance. This makes it possible to adjust the order of scenarios based on the relevance of animations.

[0099] The character generation unit can estimate the user's emotion and adjust the way the character's movement is expressed based on the estimated user's emotion. The character generation unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the character generation unit can generate a character with gentle movements when the user is relaxed. Furthermore, the character generation unit can generate a character with fast movements when the user is in a hurry. Furthermore, the character generation unit can generate a character with dynamic movements when the user is excited. This makes it possible to adjust the way the character's movement is expressed based on the user's emotion.

[0100] The character generation unit can adjust the level of detail of the character's movements based on the importance of the animation when generating the character. The character generation unit can adjust the level of detail of the character's movements based on, for example, the importance of the animation. For example, the character generation unit can generate a character that is depicted in great detail for an important animation. The character generation unit can also generate a simplified character for an animation that is less important. Furthermore, the character generation unit can adjust the level of detail of the character's movements based on the importance. This allows the level of detail of the character's movements to be adjusted based on the importance of the animation.

[0101] The character generation unit can apply different character generation algorithms depending on the animation category when generating a character. For example, the character generation unit can apply different character generation algorithms depending on the animation category. For example, the character generation unit can apply a formal style generation algorithm to a character for a business presentation. The character generation unit can also apply a visually easy-to-understand style generation algorithm to an educational character. Furthermore, the character generation unit can apply a dynamic and fun style generation algorithm to an entertainment character. In this way, different character generation algorithms can be applied depending on the animation category.

[0102] The character generation unit can estimate the user's emotion and adjust the length of the character's movement based on the estimated user's emotion. The character generation unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is in a hurry, the character generation unit can generate a character that makes short, to-the-point movements. If the user is relaxed, the character generation unit can also generate a character that makes longer, detailed movements. Furthermore, if the user is excited, the character generation unit can also generate a character that makes movements with visually stimulating effects. This makes it possible to adjust the length of the character's movement based on the user's emotion.

[0103] When generating a character, the character generation unit can determine the priority of character movements based on the time of submission of the animation. The character generation unit can determine the priority of character movements based on, for example, the time of submission of the animation. For example, the character generation unit can prioritize the generation of characters of animations whose submission deadlines are approaching. The character generation unit can also postpone the generation of characters of animations whose submission deadlines are further away. Furthermore, the character generation unit can adjust the order in which character movements are generated depending on the time of submission. This makes it possible to determine the priority of character movements based on the time of submission of the animation.

[0104] The character generation unit can adjust the order of character movements based on the relevance of the animations when generating a character. The character generation unit can adjust the order of character movements based on, for example, the relevance of the animations. For example, the character generation unit can prioritize the generation of movements of characters that are highly relevant to the user's project. The character generation unit can also postpone the generation of movements of characters that are less relevant. Furthermore, the character generation unit can adjust the generation order of character movements based on the relevance. This makes it possible to adjust the order of character movements based on the relevance of the animations.

[0105] The display unit can estimate the user's emotion and adjust the animation display method based on the estimated user's emotion. The display unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the display unit can provide a simple, highly visible display method when the user is nervous. Furthermore, the display unit can provide a display method including detailed information when the user is relaxed. Furthermore, the display unit can provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to adjust the animation display method based on the user's emotion.

[0106] The display unit can select the optimal display method by referring to the user's past viewing history when displaying. The display unit can select the optimal display method by referring to the user's past viewing history, for example. For example, the display unit can select the optimal display method based on the style of animation that the user has viewed in the past. The display unit can also suggest a preferred display method based on the user's past viewing history. Furthermore, the display unit can analyze the user's viewing history and select the display method with the highest visibility. This makes it possible to select the optimal display method based on the user's past viewing history.

[0107] The display unit can select the optimal display method when displaying, taking into consideration the user's device information. The display unit can select the optimal display method, for example, taking into consideration the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This makes it possible to select the optimal display method based on the user's device information.

[0108] The display unit can estimate the user's emotion and adjust the display order of animations based on the estimated user's emotion. The display unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is feeling stressed, the display unit can prioritize displaying relaxing animations. Furthermore, if the user is relaxed, the display unit can prioritize displaying important animations. Furthermore, if the user is in a hurry, the display unit can prioritize displaying animations that require quick processing. In this way, the display order of animations can be adjusted based on the user's emotion.

[0109] The display unit can prioritize displaying highly relevant animations in consideration of the user's geographical location information when displaying the animations. For example, the display unit can prioritize displaying highly relevant animations in consideration of the user's geographical location information. For example, if the user is in a specific area, the display unit can prioritize displaying animations related to that area. The display unit can also filter and display highly relevant animations based on the user's current location. Furthermore, the display unit can select and display optimal animations in consideration of the user's geographical location information. This makes it possible to prioritize displaying highly relevant animations based on the user's geographical location information.

[0110] The display unit can analyze the user's social media activity and display related animations when displaying the animations. For example, the display unit can analyze the user's social media activity and display related animations. For example, the display unit can prioritize displaying related animations based on content shared by the user on social media. The display unit can also analyze the user's social media activity and filter and display animations that match the user's interests. Furthermore, the display unit can select and display the most appropriate animations in consideration of the user's social media activity history. This makes it possible to display related animations based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives natural language input from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an animation scenario and character movements using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14 and visually displays the generated animation. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives natural language input from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an animation scenario and character movements using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and visually displays the generated animation. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives natural language input from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an animation scenario and character movements using a generation AI. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and visually displays the generated animation. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives natural language input from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an animation scenario and character movements using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414 and visually displays the generated animation.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The reception unit can analyze the user's voice input and convert natural language images into text. For example, if the user voice-inputs, "I want to create a scene of a business meeting," the reception unit can convert the voice into text and send it to the generation unit. The reception unit can also use voice recognition technology to analyze the user's speaking rate and intonation to more accurately understand the user's intentions. Furthermore, the reception unit supports multiple languages ​​and can automatically translate and convert voice input in different languages ​​into text. This allows the user's voice input to be analyzed and natural language images to be converted into text.

[0113] The generation unit may include a background generation unit that generates an animated background based on user input. For example, when a user inputs, "I want to create a business meeting scene," the background generation unit can automatically generate a conference room background. The background generation unit can also adjust details of the background based on the user input. For example, the background generation unit can adjust the layout and decoration of the conference room based on the user input. Furthermore, the background generation unit can generate a background in real time using generative AI and provide visual feedback to the user. This allows the animated background to be generated based on the user input.

[0114] The generation unit can estimate the user's emotion and adjust the character's facial expression based on the estimated user's emotion. For example, the generation unit can soften the character's facial expression when the user is relaxed. Also, the generation unit can make the character's facial expression serious when the user is nervous. Furthermore, the generation unit can make the character's facial expression lively when the user is excited. In this way, the character's facial expression can be adjusted based on the user's emotion.

[0115] The providing unit can optimize the generated animation for the user's device and display it. For example, the providing unit can display the animation at an optimal resolution and frame rate for different devices, such as a smartphone, a tablet, and a desktop. The providing unit can also automatically adjust the quality of the animation according to the performance of the user's device. Furthermore, the providing unit can adjust the layout of the animation to match the screen size of the user's device to improve visibility. This allows the generated animation to be optimized for the user's device and displayed.

[0116] The generation unit can estimate the user's emotion and adjust the color tone of the animation based on the estimated user's emotion. For example, the generation unit can generate animation with soft color tones when the user is relaxed. Furthermore, the generation unit can generate animation with subdued color tones when the user is nervous. Furthermore, the generation unit can generate animation with vivid color tones when the user is excited. In this way, the color tone of the animation can be adjusted based on the user's emotion.

[0117] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can preferentially suggest input methods that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and select the most efficient input method. This makes it possible to suggest the optimal input method based on the user's past input history.

[0118] The generation unit can estimate the user's emotion and adjust the tempo of the animation based on the estimated user's emotion. For example, the generation unit can generate animation with a slow tempo when the user is relaxed. The generation unit can also generate animation with a fast tempo when the user is in a hurry. Furthermore, the generation unit can generate animation with a dynamic tempo when the user is excited. In this way, the tempo of the animation can be adjusted based on the user's emotion.

[0119] The providing unit may have a function for directly sharing the generated animation to the user's social media account. For example, the providing unit may directly post the generated animation to social media platforms such as Facebook, X (formerly Twitter), and Instagram. The providing unit may also share the animation with specific groups or followers selected by the user. Furthermore, the providing unit may collect user reactions to social media posts and reflect them in the next generation of animation. This allows the generated animation to be directly shared to the user's social media account.

[0120] The generation unit can estimate the user's emotion and adjust the sound of the animation based on the estimated user's emotion. For example, the generation unit can use a calm voice when the user is relaxed. Furthermore, the generation unit can use a calm voice when the user is nervous. Furthermore, the generation unit can use a lively voice when the user is excited. In this way, the sound of the animation can be adjusted based on the user's emotion.

[0121] The provider can integrate the generated animation with the user's project management tool. For example, the provider can directly add the generated animation to a project management tool such as Trello, Asana, or Jira. The provider can also automatically update the animation according to the progress of the project. Furthermore, the provider can share the animation with project team members and collect feedback. This allows the generated animation to be integrated with the user's project management tool.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The reception unit receives an image in natural language from the user. For example, if the user inputs "I want to create a scene of a business meeting," the reception unit can receive the image. Step 2: The generation unit analyzes the image received by the reception unit and generates variations of the animation. The generation unit uses generation AI to generate animation scenarios and character movements based on user input, enabling high-resolution, high-frame-rate animation to be generated quickly and efficiently. Step 3: The providing unit provides the animation generated by the generating unit, and the providing unit can visually display the generated animation.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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, in order to avoid confusion and to 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 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 an image in natural language from a user; a generation unit that analyzes the image received by the reception unit and generates variations of the animation; a providing unit that provides the animation generated by the generating unit. A system characterized by:

2. The generation unit A scenario generation unit is provided that generates an animation scenario based on user input.

2. The system of claim 1.

3. The generation unit A character generation unit generates character movements based on user input.

2. The system of claim 1.

4. The providing unit A display unit is provided to visually display the generated animation.

2. The system of claim 1.

5. The generation unit Generate high resolution, high frame rate animations 2. The system of claim 1.

6. The generation unit Generate animations quickly and efficiently 2. The system of claim 1.

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

2. The system of claim 1.

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

9. The reception unit Filtering images as they arrive based on the user's current project or area of ​​interest 2. The system of claim 1.

Images