system

Abstract

The system according to this embodiment aims to efficiently generate high-quality drawings that are faithful to the original artwork. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a supply unit. The reception unit receives input of conditions and advice. The generation unit analyzes the information input by the reception unit and generates a drawing that is faithful to the original drawing and of high quality. The supply unit provides the drawing generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently generate high-quality drawings that are faithful to the original drawings in anime production, and there are problems in improving productivity.

[0005] The system according to the embodiment aims to efficiently generate high-quality drawings that are faithful to the original drawings.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs conditions and advice. The generation unit analyzes the information input by the reception unit and generates high-quality drawings that are faithful to the original drawings. The provision unit provides the drawings generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently generate high-quality drawings that are faithful to the original artwork. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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). <JPH08-000097>

[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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An animation production support system according to an embodiment of the present invention is a system that uses AI to improve the productivity of animation production. In this animation production support system, the user inputs various conditions and advice to the AI, which analyzes the input information and generates high-quality drawings that are faithful to the original drawings. The generated drawings are provided to animation production companies and animators, improving the productivity of animation production. This lowers the barrier to production and allows supply to keep up with demand. For example, this system is particularly effective in markets where there is high demand for animation based on Japanese manga. By providing an animation production solution using AI, we aim to create a new revenue stream and expand high-quality Japanese manga culture and animation to the world. In terms of implementation, currently animators spend a lot of effort focusing on the production of one work at a time, but by realizing production support with generation AI, animators will be able to give instructions to the AI ​​and proceed with the production of multiple works simultaneously. This reduces the burden on animators and allows them to produce high-quality animation efficiently. In this way, the animation production support system can improve the productivity of animation production.

[0029] The animation production support system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives conditions and advice entered by the user. For example, the user can input conditions and advice such as color specifications, character designs, and scene details into the reception unit. The generation unit analyzes the information entered by the reception unit and generates high-quality drawings that are faithful to the original drawings. For example, the generation unit uses AI to analyze the entered information and generates drawings based on criteria such as resolution, color reproduction, and detail reproduction. The provision unit provides the drawings generated by the generation unit to animation production companies and animators. For example, the provision unit provides the generated drawings in digital format so that animation production companies and animators can easily use them. As a result, the animation production support system can generate high-quality drawings that are faithful to the original drawings simply by inputting conditions and advice, thereby improving the productivity of animation production.

[0030] The reception desk accepts conditions and suggestions entered by users. For example, users can input conditions and suggestions such as color specifications, character designs, and scene details. Specifically, users can specify details such as color palettes, character appearance, clothing, expressions, poses, and background settings through a dedicated interface. Furthermore, they can finely set scene details such as time of day, weather, location, character movements, and dialogue. The reception desk efficiently collects this information and stores it in a database. Conditions and suggestions entered by users can be accepted in various formats, not only text, but also images, sketches, and voice memos. This allows users to communicate their ideas more concretely, and the system can prepare to generate high-quality drawings based on that information. In addition, the reception desk has a function that records and allows users to reuse conditions and suggestions they have entered in the past. This saves users the trouble of re-entering conditions they have set once, allowing them to work more efficiently. The reception desk can also analyze user input in real time and provide feedback as needed. For example, if there are inconsistencies in the input or if better settings can be considered, the system will automatically provide advice and support the user in setting the optimal conditions. This allows the reception unit to accurately understand the user's intentions and provides a foundation for the generation unit to produce high-quality drawings.

[0031] The generation unit analyzes the information input by the reception unit and generates high-quality drawings that are faithful to the original artwork. For example, the generation unit uses AI to analyze the input information and generates drawings based on criteria such as resolution, color reproduction, and detail reproduction. Specifically, the generation unit utilizes deep learning technology to train a model for generating optimal drawings based on the conditions and advice input by the user. This model is designed to learn from a large amount of animation data and to handle various styles and techniques. The generation unit first preprocesses the input information and extracts the necessary features. Next, the AI ​​model generates drawings based on these features. The generated drawings are evaluated based on criteria such as resolution, color reproduction, and detail reproduction, and corrections are made as needed. For example, if the character's expression or pose differs from the user's intention, the AI ​​automatically corrects it and provides the optimal result. The generation unit can also simulate multiple scenarios and select the most appropriate drawing. This allows the generation unit to quickly generate high-quality drawings that accurately reflect the user's intentions. Furthermore, the generation unit has built a feedback loop to continuously improve the generated drawings. Based on user feedback, the AI ​​model is retrained and the changes are reflected in the next generation of drawings. This allows the generation unit to consistently provide high-quality drawings that incorporate the latest technology and meet user needs.

[0032] The provisioning department provides the drawings generated by the generation department to animation production companies and animators. For example, the provisioning department provides the generated drawings in digital format, making them easily usable by animation production companies and animators. Specifically, the provisioning department saves the generated drawings as high-resolution image files or vector data and uploads them to cloud storage or a dedicated database. Animation production companies and animators can access, download, and use this data. The provisioning department can appropriately convert and provide the drawing data according to the format and resolution specified by the user. For example, it can accommodate various needs, such as file formats compatible with specific animation software or high-resolution data for printing. Furthermore, the provisioning department can also manage versions of the drawing data and set access permissions. This allows multiple animators to work efficiently while maintaining data integrity, even when working simultaneously. In addition, the provisioning department can update the generated drawing data in real time, always providing the latest information. For example, if a user inputs new conditions or suggestions, the generation department will generate the drawings again, and the provisioning department will immediately provide the updated data. This allows animation production companies and animators to always work based on the latest drawing data. Furthermore, the provisioning department collects user feedback and provides it to the generation and reception departments, contributing to the improvement of the entire system. In this way, the provisioning department can not only improve the productivity of animation production but also increase user satisfaction.

[0033] The animation production support system includes an evaluation unit that assesses the quality of the generated animation. The evaluation unit evaluates the quality of the generated animation based on criteria such as visual aesthetics, technical accuracy, and user satisfaction. The evaluation unit can, for example, use AI to evaluate the quality of the animation. This allows for the provision of high-quality animation by evaluating the quality of the generated animation.

[0034] The animation production support system includes a progress management unit for managing multiple projects simultaneously. This unit manages the simultaneous progress of multiple projects. For example, it manages schedules and allocates resources for multiple projects such as animation series, films, and short films. The progress management unit can, for example, use AI for progress management. This allows for improved animation production efficiency by managing multiple projects simultaneously.

[0035] The reception desk analyzes past input history and presents input suggestions to make it easier for the user to input. For example, the reception desk automatically displays conditions and advice that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest conditions and advice to be used at specific times based on the user's past input history. This allows the user to input conditions and advice efficiently by analyzing past input history. Input history includes, but is not limited to, data, frequency, and patterns of conditions and advice entered in the past. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI.

[0036] The reception desk analyzes the entered conditions and suggestions in real time and provides appropriate feedback. For example, the reception desk provides real-time appropriate feedback on the conditions and suggestions entered by the user. The reception desk can also suggest additional suggestions or conditions based on the user's input. Furthermore, if there are errors in the user's input, the reception desk can suggest corrections in real time. This allows users to enter appropriate conditions and suggestions by providing real-time feedback. The definition of real-time includes, but is not limited to, the number of seconds within which feedback is provided after input. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI.

[0037] The reception desk prioritizes receiving region-specific conditions and advice based on the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize receiving region-specific conditions and advice for that region. Furthermore, if the user is traveling, the reception desk can also prioritize receiving region-specific conditions and advice for the destination region. Additionally, if the user has moved, the reception desk can prioritize receiving region-specific conditions and advice for the new region. This allows for the input of conditions and advice that are appropriate for the user by prioritizing region-specific information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI.

[0038] The reception desk analyzes the user's social media activity and automatically suggests relevant conditions and advice. For example, the reception desk can automatically suggest relevant conditions and advice based on the user's social media activity. The reception desk can also suggest appropriate conditions and advice based on information shared by the user on social media. Furthermore, the reception desk can analyze the user's social media activity and suggest conditions and advice based on trends. In this way, by analyzing social media activity, it is possible to suggest conditions and advice that are suitable for the user. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI.

[0039] The generation unit improves accuracy by referring to past drawing data when analyzing the input information. For example, the generation unit improves the accuracy of analyzing the input information based on past drawing data. The generation unit can also refer to past drawing data and generate drawings based on similar conditions and advice. Furthermore, the generation unit can analyze past drawing data and propose the optimal drawing style. In this way, the accuracy of analyzing the input information can be improved by referring to past drawing data. Past drawing data includes, but is not limited to, past project files and data of completed works. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI.

[0040] The generation unit allows the user to select different animation styles when generating drawings. For example, the generation unit generates drawings based on an animation style selected by the user. The generation unit can also suggest different animation styles and allow the user to select one. Furthermore, if the user selects multiple animation styles, the generation unit can generate drawings based on each style. This allows the user to meet diverse needs by allowing them to select different animation styles. Different animation styles include, but are not limited to, 2D animation, 3D animation, and hand-drawn styles. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0041] The generation unit customizes the style when generating artwork by referring to the user's past artwork history. For example, the generation unit customizes the drawing style based on the user's past artwork history. The generation unit can also refer to the style of artwork previously created by the user and generate artwork in a similar style. Furthermore, the generation unit can analyze the user's past artwork history and suggest the optimal drawing style. This allows the generation of a drawing style that suits the user's preferences by referring to past artwork history. Past artwork history includes, but is not limited to, data on previously created artwork and user ratings. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI.

[0042] The generation unit reflects region-specific styles based on the user's geographical location information when generating drawings. For example, if the user is in a specific region, the generation unit generates drawings that reflect the region-specific style of that region. Furthermore, if the user is traveling, the generation unit can generate drawings that reflect the region-specific style of the destination. In addition, if the user moves, the generation unit can generate drawings that reflect the new region-specific style. This allows for the generation of drawings appropriate to the user's region by reflecting region-specific styles. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI.

[0043] The delivery unit selects the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the delivery unit selects the optimal delivery method based on the user's past feedback. The delivery unit can also prioritize selecting delivery methods that the user has preferred in the past. Furthermore, the delivery unit can analyze the user's past feedback and propose the optimal delivery method. This allows the delivery unit to select a delivery method that is suitable for the user by referring to past feedback. Past feedback includes, but is not limited to, user evaluation comments and evaluation scores. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or not using AI.

[0044] The provider will allow users to select different delivery formats depending on the intended use of the artwork at the time of delivery. For example, the provider will select different delivery formats depending on the intended use of the artwork. The provider can also suggest the most suitable delivery format based on the intended use selected by the user. Furthermore, the provider can suggest multiple delivery formats depending on the intended use of the artwork. This allows users to meet their needs by selecting a delivery format that suits their intended use. Intended uses include, but are not limited to, commercial use, personal use, and educational use. Some or all of the processing described above in the provider may be performed using AI, for example, or without AI.

[0045] The service provider selects the optimal service delivery method based on the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider selects a service delivery method specific to that region. Furthermore, if the user is traveling, the service provider can select a service delivery method specific to the destination region. In addition, if the user moves, the service provider can select a service delivery method specific to the new region. This allows the service provider to deliver artwork in a way that is appropriate for the user by selecting the optimal service delivery method based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above-described processing in the service provider may be performed using, for example, AI, or without AI.

[0046] The service provider analyzes the user's social media activity and suggests relevant artwork at the time of delivery. For example, the service provider can automatically suggest relevant artwork based on the user's social media activity. The service provider can also suggest appropriate artwork based on information shared by the user on social media. Furthermore, the service provider can analyze the user's social media activity and suggest artwork based on trends. In this way, by analyzing social media activity, it is possible to suggest artwork that is suitable for the user. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI.

[0047] The evaluation unit optimizes the evaluation algorithm by referring to past evaluation data during the evaluation process. For example, the evaluation unit optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also refer to past evaluation data and perform evaluations based on similar conditions and advice. Furthermore, the evaluation unit can analyze past evaluation data and propose optimal evaluation criteria. This allows for the optimization of the evaluation algorithm and the performance of highly accurate evaluations by referring to past evaluation data. Past evaluation data includes, but is not limited to, past evaluation results and evaluation criteria. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or without using AI.

[0048] The evaluation unit adjusts the evaluation criteria based on the user's geographical location information during the evaluation process. For example, if the user is in a specific region, the evaluation unit applies evaluation criteria specific to that region. Furthermore, if the user is traveling, the evaluation unit can apply evaluation criteria specific to the destination region. Additionally, if the user moves, the evaluation unit can apply evaluation criteria specific to the new region. This allows for evaluations tailored to the user by adjusting the evaluation criteria based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the evaluation unit may be performed using, for example, AI, or without AI.

[0049] The project management department selects the optimal project management method by referring to past project management data during project management. For example, the project management department selects the optimal project management method based on past project management data. The project management department can also refer to past project management data and perform project management based on similar conditions and advice. Furthermore, the project management department can analyze past project management data and propose the optimal project management method. This allows for the selection of the optimal project management method and efficient project management by referring to past project management data. Past project management data includes, but is not limited to, past project progress status and project management methods. Some or all of the above processes in the project management department may be performed using, for example, AI, or not using AI.

[0050] The progress management unit selects the optimal progress management method based on the user's geographical location information during progress management. For example, if the user is in a specific region, the progress management unit selects a region-specific progress management method. Furthermore, if the user is traveling, the progress management unit can select a region-specific progress management method. Additionally, if the user moves, the progress management unit can select a new region-specific progress management method. This allows for user-friendly progress management by selecting the optimal method based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above-described processes in the progress management unit may be performed using, for example, AI, or without AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The animation production support system can analyze the user's past drawing style and suggest the most suitable style. For example, if the user previously preferred a hand-drawn style, it will suggest a hand-drawn style. Similarly, if the user previously preferred 3D animation, it can suggest a 3D animation style. Furthermore, if the user previously preferred a specific color scheme, it can suggest a drawing style that reflects that color scheme. In this way, by analyzing the user's past drawing style, it can suggest a drawing style that matches the user's preferences.

[0053] The animation production support system can generate animation that reflects the unique culture and scenery of a region based on the user's geographical location. For example, if the user is in Japan, it can generate animation that reflects traditional Japanese scenery and culture. If the user is in France, it can generate animation that reflects French scenery and culture. Furthermore, if the user is traveling, it can generate animation that reflects the unique scenery and culture of the region they are visiting. In this way, by reflecting the unique culture and scenery of a region, it can generate animation that is suitable for the user.

[0054] The animation production support system can analyze users' social media activity and suggest drawing styles based on trends. For example, it can suggest drawing styles that match current trends based on information shared by users on social media. It can also suggest drawing styles that reflect the styles of artists and influencers that users follow. Furthermore, it can analyze popular themes and styles from users' social media activity and suggest drawing styles based on that. In this way, by analyzing social media activity, it can suggest drawing styles that are suitable for the user.

[0055] The animation production support system can suggest the optimal drawing style based on the user's past feedback. For example, it can prioritize suggesting drawing styles that the user has previously given high ratings to. It can also avoid drawing styles that the user has previously given low ratings to. Furthermore, it can analyze the user's past feedback and suggest the optimal drawing style. In this way, it can suggest drawing styles that match the user's preferences based on past feedback.

[0056] The animation production support system can generate character designs that reflect the region specific to the user's location, based on the user's geographical location information. For example, if the user is in Japan, it can generate character designs that reflect traditional Japanese character designs. If the user is in the United States, it can generate character designs that reflect American culture. Furthermore, if the user is traveling, it can generate character designs that reflect the region specific to their travel destination. In this way, by reflecting region-specific character designs, it can generate character designs that are suitable for the user.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives the conditions and suggestions entered by the user. For example, the user can enter conditions and suggestions such as color specifications, character designs, and scene details. Step 2: The generation unit analyzes the information entered by the reception unit and generates high-quality drawings that are faithful to the original artwork. For example, the generation unit uses AI to analyze the entered information and generates drawings based on criteria such as resolution, color reproduction, and detail reproduction. Step 3: The providing unit provides the drawings generated by the generating unit. For example, the providing unit provides the generated drawings in digital format so that animation production companies and animators can easily use them.

[0059] (Example of form 2) An animation production support system according to an embodiment of the present invention is a system that uses AI to improve the productivity of animation production. In this animation production support system, the user inputs various conditions and advice to the AI, which analyzes the input information and generates high-quality drawings that are faithful to the original drawings. The generated drawings are provided to animation production companies and animators, improving the productivity of animation production. This lowers the barrier to production and allows supply to keep up with demand. For example, this system is particularly effective in markets where there is high demand for animation based on Japanese manga. By providing an animation production solution using AI, we aim to create a new revenue stream and expand high-quality Japanese manga culture and animation to the world. In terms of implementation, currently animators spend a lot of effort focusing on the production of one work at a time, but by realizing production support with generation AI, animators will be able to give instructions to the AI ​​and proceed with the production of multiple works simultaneously. This reduces the burden on animators and allows them to produce high-quality animation efficiently. In this way, the animation production support system can improve the productivity of animation production.

[0060] The animation production support system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives conditions and advice entered by the user. For example, the user can input conditions and advice such as color specifications, character designs, and scene details into the reception unit. The generation unit analyzes the information entered by the reception unit and generates high-quality drawings that are faithful to the original drawings. For example, the generation unit uses AI to analyze the entered information and generates drawings based on criteria such as resolution, color reproduction, and detail reproduction. The provision unit provides the drawings generated by the generation unit to animation production companies and animators. For example, the provision unit provides the generated drawings in digital format so that animation production companies and animators can easily use them. As a result, the animation production support system can generate high-quality drawings that are faithful to the original drawings simply by inputting conditions and advice, thereby improving the productivity of animation production.

[0061] The reception desk accepts conditions and suggestions entered by users. For example, users can input conditions and suggestions such as color specifications, character designs, and scene details. Specifically, users can specify details such as color palettes, character appearance, clothing, expressions, poses, and background settings through a dedicated interface. Furthermore, they can finely set scene details such as time of day, weather, location, character movements, and dialogue. The reception desk efficiently collects this information and stores it in a database. Conditions and suggestions entered by users can be accepted in various formats, not only text, but also images, sketches, and voice memos. This allows users to communicate their ideas more concretely, and the system can prepare to generate high-quality drawings based on that information. In addition, the reception desk has a function that records and allows users to reuse conditions and suggestions they have entered in the past. This saves users the trouble of re-entering conditions they have set once, allowing them to work more efficiently. The reception desk can also analyze user input in real time and provide feedback as needed. For example, if there are inconsistencies in the input or if better settings can be considered, the system will automatically provide advice and support the user in setting the optimal conditions. This allows the reception unit to accurately understand the user's intentions and provides a foundation for the generation unit to produce high-quality drawings.

[0062] The generation unit analyzes the information input by the reception unit and generates high-quality drawings that are faithful to the original artwork. For example, the generation unit uses AI to analyze the input information and generates drawings based on criteria such as resolution, color reproduction, and detail reproduction. Specifically, the generation unit utilizes deep learning technology to train a model for generating optimal drawings based on the conditions and advice input by the user. This model is designed to learn from a large amount of animation data and to handle various styles and techniques. The generation unit first preprocesses the input information and extracts the necessary features. Next, the AI ​​model generates drawings based on these features. The generated drawings are evaluated based on criteria such as resolution, color reproduction, and detail reproduction, and corrections are made as needed. For example, if the character's expression or pose differs from the user's intention, the AI ​​automatically corrects it and provides the optimal result. The generation unit can also simulate multiple scenarios and select the most appropriate drawing. This allows the generation unit to quickly generate high-quality drawings that accurately reflect the user's intentions. Furthermore, the generation unit has built a feedback loop to continuously improve the generated drawings. Based on user feedback, the AI ​​model is retrained and the changes are reflected in the next generation of drawings. This allows the generation unit to consistently provide high-quality drawings that incorporate the latest technology and meet user needs.

[0063] The provisioning department provides the drawings generated by the generation department to animation production companies and animators. For example, the provisioning department provides the generated drawings in digital format, making them easily usable by animation production companies and animators. Specifically, the provisioning department saves the generated drawings as high-resolution image files or vector data and uploads them to cloud storage or a dedicated database. Animation production companies and animators can access, download, and use this data. The provisioning department can appropriately convert and provide the drawing data according to the format and resolution specified by the user. For example, it can accommodate various needs, such as file formats compatible with specific animation software or high-resolution data for printing. Furthermore, the provisioning department can also manage versions of the drawing data and set access permissions. This allows multiple animators to work efficiently while maintaining data integrity, even when working simultaneously. In addition, the provisioning department can update the generated drawing data in real time, always providing the latest information. For example, if a user inputs new conditions or suggestions, the generation department will generate the drawings again, and the provisioning department will immediately provide the updated data. This allows animation production companies and animators to always work based on the latest drawing data. Furthermore, the provisioning department collects user feedback and provides it to the generation and reception departments, contributing to the improvement of the entire system. In this way, the provisioning department can not only improve the productivity of animation production but also increase user satisfaction.

[0064] The animation production support system includes an evaluation unit that assesses the quality of the generated animation. The evaluation unit evaluates the quality of the generated animation based on criteria such as visual aesthetics, technical accuracy, and user satisfaction. The evaluation unit can, for example, use AI to evaluate the quality of the animation. This allows for the provision of high-quality animation by evaluating the quality of the generated animation.

[0065] The animation production support system includes a progress management unit for managing multiple projects simultaneously. This unit manages the simultaneous progress of multiple projects. For example, it manages schedules and allocates resources for multiple projects such as animation series, films, and short films. The progress management unit can, for example, use AI for progress management. This allows for improved animation production efficiency by managing multiple projects simultaneously.

[0066] The reception desk estimates the user's emotions and adjusts the input interface for conditions and advice based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of conditions and advice. This allows users to comfortably input conditions and advice by adjusting the input interface according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The reception desk analyzes past input history and presents input suggestions to make it easier for the user to input. For example, the reception desk automatically displays conditions and advice that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest conditions and advice to be used at specific times based on the user's past input history. This allows the user to input conditions and advice efficiently by analyzing past input history. Input history includes, but is not limited to, data, frequency, and patterns of conditions and advice entered in the past. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI.

[0068] The reception desk analyzes the entered conditions and suggestions in real time and provides appropriate feedback. For example, the reception desk provides real-time appropriate feedback on the conditions and suggestions entered by the user. The reception desk can also suggest additional suggestions or conditions based on the user's input. Furthermore, if there are errors in the user's input, the reception desk can suggest corrections in real time. This allows users to enter appropriate conditions and suggestions by providing real-time feedback. The definition of real-time includes, but is not limited to, the number of seconds within which feedback is provided after input. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI.

[0069] The reception desk estimates the user's emotions and prioritizes the input content based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize inputting important conditions and advice. If the user is relaxed, the reception desk can also prioritize inputting detailed conditions and advice. Furthermore, if the user is in a hurry, the reception desk can also prioritize inputting the most important conditions and advice. This ensures that important conditions and advice are prioritized by determining the priority of input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The reception desk prioritizes receiving region-specific conditions and advice based on the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize receiving region-specific conditions and advice for that region. Furthermore, if the user is traveling, the reception desk can also prioritize receiving region-specific conditions and advice for the destination region. Additionally, if the user has moved, the reception desk can prioritize receiving region-specific conditions and advice for the new region. This allows for the input of conditions and advice that are appropriate for the user by prioritizing region-specific information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI.

[0071] The reception desk analyzes the user's social media activity and automatically suggests relevant conditions and advice. For example, the reception desk can automatically suggest relevant conditions and advice based on the user's social media activity. The reception desk can also suggest appropriate conditions and advice based on information shared by the user on social media. Furthermore, the reception desk can analyze the user's social media activity and suggest conditions and advice based on trends. In this way, by analyzing social media activity, it is possible to suggest conditions and advice that are suitable for the user. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI.

[0072] The generation unit estimates the user's emotions and adjusts the drawing style based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a soft-touch drawing style. It can also generate a drawing style with vibrant colors if the user is excited. Furthermore, if the user is sad, it can generate a drawing style with calm tones. This allows for the generation of drawings that match the user's intentions by adjusting the drawing style according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The generation unit improves accuracy by referring to past drawing data when analyzing the input information. For example, the generation unit improves the accuracy of analyzing the input information based on past drawing data. The generation unit can also refer to past drawing data and generate drawings based on similar conditions and advice. Furthermore, the generation unit can analyze past drawing data and propose the optimal drawing style. In this way, the accuracy of analyzing the input information can be improved by referring to past drawing data. Past drawing data includes, but is not limited to, past project files and data of completed works. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI.

[0074] The generation unit allows the user to select different animation styles when generating drawings. For example, the generation unit generates drawings based on an animation style selected by the user. The generation unit can also suggest different animation styles and allow the user to select one. Furthermore, if the user selects multiple animation styles, the generation unit can generate drawings based on each style. This allows the user to meet diverse needs by allowing them to select different animation styles. Different animation styles include, but are not limited to, 2D animation, 3D animation, and hand-drawn styles. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0075] The generation unit estimates the user's emotions and adjusts the level of detail in the drawing based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a detailed drawing. It can also generate a simplified drawing if the user is in a hurry. Furthermore, if the user is excited, the generation unit can generate a visually stimulating drawing. This allows for the generation of drawings that match the user's intentions by adjusting the level of detail according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The generation unit customizes the style when generating artwork by referring to the user's past artwork history. For example, the generation unit customizes the drawing style based on the user's past artwork history. The generation unit can also refer to the style of artwork previously created by the user and generate artwork in a similar style. Furthermore, the generation unit can analyze the user's past artwork history and suggest the optimal drawing style. This allows the generation of a drawing style that suits the user's preferences by referring to past artwork history. Past artwork history includes, but is not limited to, data on previously created artwork and user ratings. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI.

[0077] The generation unit reflects region-specific styles based on the user's geographical location information when generating drawings. For example, if the user is in a specific region, the generation unit generates drawings that reflect the region-specific style of that region. Furthermore, if the user is traveling, the generation unit can generate drawings that reflect the region-specific style of the destination. In addition, if the user moves, the generation unit can generate drawings that reflect the new region-specific style. This allows for the generation of drawings appropriate to the user's region by reflecting region-specific styles. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI.

[0078] The delivery unit estimates the user's emotions and adjusts the method of delivering the artwork based on the estimated emotions. For example, if the user is relaxed, the delivery unit may select a delivery method that includes detailed explanations. If the user is in a hurry, the delivery unit may select a method that allows for quick delivery. Furthermore, if the user is excited, the delivery unit may select a visually stimulating delivery method. In this way, by adjusting the delivery method according to the user's emotions, artwork can be delivered in a way that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The delivery unit selects the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the delivery unit selects the optimal delivery method based on the user's past feedback. The delivery unit can also prioritize selecting delivery methods that the user has preferred in the past. Furthermore, the delivery unit can analyze the user's past feedback and propose the optimal delivery method. This allows the delivery unit to select a delivery method that is suitable for the user by referring to past feedback. Past feedback includes, but is not limited to, user evaluation comments and evaluation scores. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or not using AI.

[0080] The provider will allow users to select different delivery formats depending on the intended use of the artwork at the time of delivery. For example, the provider will select different delivery formats depending on the intended use of the artwork. The provider can also suggest the most suitable delivery format based on the intended use selected by the user. Furthermore, the provider can suggest multiple delivery formats depending on the intended use of the artwork. This allows users to meet their needs by selecting a delivery format that suits their intended use. Intended uses include, but are not limited to, commercial use, personal use, and educational use. Some or all of the processing described above in the provider may be performed using AI, for example, or without AI.

[0081] The service provider estimates the user's emotions and determines the priority of the artwork to be provided based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize detailed artwork. If the user is in a hurry, the service provider may also prioritize simplified artwork. Furthermore, if the user is excited, the service provider may also prioritize visually stimulating artwork. In this way, by determining the priority of artwork according to the user's emotions, the service provider can provide artwork that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The service provider selects the optimal service delivery method based on the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider selects a service delivery method specific to that region. Furthermore, if the user is traveling, the service provider can select a service delivery method specific to the destination region. In addition, if the user moves, the service provider can select a service delivery method specific to the new region. This allows the service provider to deliver artwork in a way that is appropriate for the user by selecting the optimal service delivery method based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above-described processing in the service provider may be performed using, for example, AI, or without AI.

[0083] The service provider analyzes the user's social media activity and suggests relevant artwork at the time of delivery. For example, the service provider can automatically suggest relevant artwork based on the user's social media activity. The service provider can also suggest appropriate artwork based on information shared by the user on social media. Furthermore, the service provider can analyze the user's social media activity and suggest artwork based on trends. In this way, by analyzing social media activity, it is possible to suggest artwork that is suitable for the user. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI.

[0084] The evaluation unit estimates the user's emotions and adjusts the drawing evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the evaluation unit applies detailed evaluation criteria. If the user is in a hurry, the evaluation unit can also apply simplified evaluation criteria. Furthermore, if the user is excited, the evaluation unit can apply visually stimulating evaluation criteria. In this way, by adjusting the evaluation criteria according to the user's emotions, an evaluation appropriate to the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The evaluation unit optimizes the evaluation algorithm by referring to past evaluation data during the evaluation process. For example, the evaluation unit optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also refer to past evaluation data and perform evaluations based on similar conditions and advice. Furthermore, the evaluation unit can analyze past evaluation data and propose optimal evaluation criteria. This allows for the optimization of the evaluation algorithm and the performance of highly accurate evaluations by referring to past evaluation data. Past evaluation data includes, but is not limited to, past evaluation results and evaluation criteria. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or without using AI.

[0086] The evaluation unit estimates the user's emotions and determines the priority of evaluations based on the estimated emotions. For example, if the user is relaxed, the evaluation unit may prioritize detailed evaluations. It can also prioritize simplified evaluations if the user is in a hurry. Furthermore, if the user is excited, the evaluation unit may prioritize visually stimulating evaluations. This allows for evaluations tailored to the user by prioritizing evaluations according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The evaluation unit adjusts the evaluation criteria based on the user's geographical location information during the evaluation process. For example, if the user is in a specific region, the evaluation unit applies evaluation criteria specific to that region. Furthermore, if the user is traveling, the evaluation unit can apply evaluation criteria specific to the destination region. Additionally, if the user moves, the evaluation unit can apply evaluation criteria specific to the new region. This allows for evaluations tailored to the user by adjusting the evaluation criteria based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the evaluation unit may be performed using, for example, AI, or without AI.

[0088] The progress management unit estimates the user's emotions and adjusts the progress management method based on the estimated emotions. For example, if the user is relaxed, the progress management unit applies a detailed progress management method. If the user is in a hurry, the progress management unit can also apply a simplified progress management method. Furthermore, if the user is excited, the progress management unit can apply a visually stimulating progress management method. In this way, by adjusting the progress management method according to the user's emotions, progress management can be made appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The project management department selects the optimal project management method by referring to past project management data during project management. For example, the project management department selects the optimal project management method based on past project management data. The project management department can also refer to past project management data and perform project management based on similar conditions and advice. Furthermore, the project management department can analyze past project management data and propose the optimal project management method. This allows for the selection of the optimal project management method and efficient project management by referring to past project management data. Past project management data includes, but is not limited to, past project progress status and project management methods. Some or all of the above processes in the project management department may be performed using, for example, AI, or not using AI.

[0090] The progress management unit estimates the user's emotions and determines the priority of progress management based on the estimated emotions. For example, if the user is relaxed, the progress management unit will prioritize detailed progress management. If the user is in a hurry, the progress management unit can also prioritize simplified progress management. Furthermore, if the user is excited, the progress management unit can prioritize visually stimulating progress management. In this way, by determining the priority of progress management according to the user's emotions, progress management can be tailored to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The progress management unit selects the optimal progress management method based on the user's geographical location information during progress management. For example, if the user is in a specific region, the progress management unit selects a region-specific progress management method. Furthermore, if the user is traveling, the progress management unit can select a region-specific progress management method. Additionally, if the user moves, the progress management unit can select a new region-specific progress management method. This allows for user-friendly progress management by selecting the optimal method based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above-described processes in the progress management unit may be performed using, for example, AI, or without AI.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The animation production support system can estimate the user's emotions and suggest animation themes based on those emotions. For example, if the user is feeling happy, it can suggest bright and cheerful animation themes. If the user is feeling sad, it can suggest emotional animation themes. Furthermore, if the user is excited, it can suggest action-packed animation themes. By suggesting animation themes that match the user's emotions, it can create works that align with the user's intentions.

[0094] The animation production support system can analyze the user's past drawing style and suggest the most suitable style. For example, if the user previously preferred a hand-drawn style, it will suggest a hand-drawn style. Similarly, if the user previously preferred 3D animation, it can suggest a 3D animation style. Furthermore, if the user previously preferred a specific color scheme, it can suggest a drawing style that reflects that color scheme. In this way, by analyzing the user's past drawing style, it can suggest a drawing style that matches the user's preferences.

[0095] The animation production support system can estimate the user's emotions and adjust the color tone of the animation based on those emotions. For example, if the user is relaxed, it can generate animation with soft colors. If the user is excited, it can generate animation with vibrant colors. Furthermore, if the user is sad, it can generate animation with calm colors. By adjusting the color tone of the animation according to the user's emotions, it can generate animation that matches the user's intentions.

[0096] The animation production support system can generate animation that reflects the unique culture and scenery of a region based on the user's geographical location. For example, if the user is in Japan, it can generate animation that reflects traditional Japanese scenery and culture. If the user is in France, it can generate animation that reflects French scenery and culture. Furthermore, if the user is traveling, it can generate animation that reflects the unique scenery and culture of the region they are visiting. In this way, by reflecting the unique culture and scenery of a region, it can generate animation that is suitable for the user.

[0097] The animation production support system can estimate the user's emotions and adjust the detail of the animation based on those emotions. For example, if the user is relaxed, it can generate detailed animation. If the user is in a hurry, it can generate simplified animation. Furthermore, if the user is excited, it can generate visually stimulating animation. By adjusting the animation details according to the user's emotions, it can generate animation that matches the user's intentions.

[0098] The animation production support system can analyze users' social media activity and suggest drawing styles based on trends. For example, it can suggest drawing styles that match current trends based on information shared by users on social media. It can also suggest drawing styles that reflect the styles of artists and influencers that users follow. Furthermore, it can analyze popular themes and styles from users' social media activity and suggest drawing styles based on that. In this way, by analyzing social media activity, it can suggest drawing styles that are suitable for the user.

[0099] The animation production support system can estimate the user's emotions and adjust the animation speed based on those emotions. For example, if the user is relaxed, it will generate animation at a slow pace. If the user is in a hurry, it can generate animation quickly. Furthermore, if the user is excited, it can generate animation at a visually stimulating pace. By adjusting the animation speed according to the user's emotions, it can produce animation that matches the user's intentions.

[0100] The animation production support system can suggest the optimal drawing style based on the user's past feedback. For example, it can prioritize suggesting drawing styles that the user has previously given high ratings to. It can also avoid drawing styles that the user has previously given low ratings to. Furthermore, it can analyze the user's past feedback and suggest the optimal drawing style. In this way, it can suggest drawing styles that match the user's preferences based on past feedback.

[0101] The animation production support system can estimate the user's emotions and adjust the composition of the animation based on those emotions. For example, if the user is relaxed, it can generate animation with a stable composition. If the user is excited, it can generate animation with a dynamic composition. Furthermore, if the user is sad, it can generate animation with a calm composition. In this way, by adjusting the composition of the animation according to the user's emotions, it can generate animation that matches the user's intentions.

[0102] The animation production support system can generate character designs that reflect the region specific to the user's location, based on the user's geographical location information. For example, if the user is in Japan, it can generate character designs that reflect traditional Japanese character designs. If the user is in the United States, it can generate character designs that reflect American culture. Furthermore, if the user is traveling, it can generate character designs that reflect the region specific to their travel destination. In this way, by reflecting region-specific character designs, it can generate character designs that are suitable for the user.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The reception desk receives the conditions and suggestions entered by the user. For example, the user can enter conditions and suggestions such as color specifications, character designs, and scene details. Step 2: The generation unit analyzes the information entered by the reception unit and generates high-quality drawings that are faithful to the original artwork. For example, the generation unit uses AI to analyze the entered information and generates drawings based on criteria such as resolution, color reproduction, and detail reproduction. Step 3: The providing unit provides the drawings generated by the generating unit. For example, the providing unit provides the generated drawings in digital format so that animation production companies and animators can easily use them.

[0105] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0108] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, evaluation unit, and progress management unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives conditions and advice entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information to generate high-quality drawings that are faithful to the original drawings. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated drawings to animation production companies and animators. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the quality of the generated drawings. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the simultaneous progress of multiple works. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 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.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, evaluation unit, and progress management unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives conditions and advice entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information to generate high-quality drawings that are faithful to the original drawings. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated drawings to animation production companies and animators. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the quality of the generated drawings. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the simultaneous progress of multiple works. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, evaluation unit, and progress management unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives conditions and advice entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information to generate high-quality drawings that are faithful to the original drawings. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated drawings to animation production companies and animators. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the quality of the generated drawings. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the simultaneous progress of multiple works. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, evaluation unit, and progress management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives conditions and advice entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information to generate high-quality drawings that are faithful to the original drawings. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated drawings to animation production companies and animators. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the quality of the generated drawings. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the simultaneous progress of multiple works. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0158] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) The reception area where you input conditions and advice, A generation unit analyzes the information input by the reception unit and generates high-quality drawings that are faithful to the original drawings, The system includes a providing unit that provides the drawing generated by the generation unit. A system characterized by the following features. (Note 2) It includes an evaluation unit that assesses the quality of the generated artwork. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a project management department for managing multiple projects simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for conditions and advice based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It analyzes past input history and presents input suggestions to make it easier for the user to input. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system analyzes the input conditions and advice in real time and provides appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Based on the user's geographical location, priority will be given to region-specific conditions and advice. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes users' social media activity and automatically suggests relevant conditions and advice. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the drawing style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When analyzing the input information, past drawing data is referenced to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is Allows the selection of different animation styles during the animation generation process. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the level of detail in the artwork based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating artwork, the style is customized by referencing the user's past artwork history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating artwork, the system reflects region-specific styles based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the method of providing the artwork based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing the service, we will refer to past user feedback to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the artwork, users can choose from different delivery formats depending on the intended use of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the artwork to be provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, At the time of delivery, the optimal delivery method will be selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest relevant artwork. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, The system estimates the user's emotions and adjusts the evaluation criteria for the artwork based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation process, the evaluation algorithm is optimized by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During the evaluation process, the evaluation criteria are adjusted based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned progress management department, It estimates the user's emotions and adjusts the progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned progress management department, During project management, refer to past project data to select the most suitable project management method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned progress management department, It estimates the user's emotions and determines the priority of progress management based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned progress management department, During project management, the optimal project management method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where you input conditions and advice, A generation unit analyzes the information input by the reception unit and generates high-quality drawings that are faithful to the original drawings, The system includes a providing unit that provides the drawing generated by the generation unit. A system characterized by the following features.

2. It includes an evaluation unit that assesses the quality of the generated artwork. The system according to feature 1.

3. Equipped with a project management department for managing multiple projects simultaneously. The system according to feature 1.

4. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for conditions and advice based on the estimated user emotions. The system according to feature 1.

5. The aforementioned reception unit is It analyzes past input history and presents input suggestions to make it easier for the user to input. The system according to feature 1.

6. The aforementioned reception unit is The system analyzes the input conditions and advice in real time and provides appropriate feedback. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Based on the user's geographical location, priority will be given to region-specific conditions and advice. The system according to feature 1.

9. The aforementioned reception unit is It analyzes users' social media activity and automatically suggests relevant conditions and advice. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and adjusts the drawing style based on those estimated emotions. The system according to feature 1.

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