system

Abstract

The system according to this embodiment aims to quickly and effectively create promotional animations for regions and companies. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and an addition unit. The reception unit takes a template as input. The generation unit generates a story and characters based on the information input by the reception unit. The addition unit adds voice acting and music to the story and characters 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to create a PR animation for a region or a company quickly and effectively, and specialized knowledge and resources are required.

[0005] The system according to the embodiment aims to quickly and effectively create a PR animation for a region or a company.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an addition unit. The reception unit inputs a template. The generation unit generates a story and characters based on the information input by the reception unit. The addition unit adds the voices and music of voice actors to the story and characters generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and effectively create promotional animations for regions and companies. [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 device 32. The processor 28, the RAM 30, and the storage device 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).

[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 device 50. The processor 46, the RAM 48, and the storage device 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) The PR original animation generation system according to an embodiment of the present invention is a system that generates original PR animations for regions and companies using AI technology. This PR original animation generation system allows users to input templates, and the AI ​​learns the characteristics, traditions, selling points, and negative factors to consider for the region or company, and then generates a story and characters based on this information. Furthermore, by collaborating with voice acting agencies, music production companies, and animation studios, the system adds depth to the animation by incorporating voice acting and music. This system is characterized by its speed and ease of use, and by closely collaborating with the animation industry, it can protect culture. It can also adjust the quality according to the budget, allowing for the widespread dissemination of appeal to the world within reasonable limits. Moreover, collaboration with voice actors and music production companies can open up new business opportunities such as concerts, events, and merchandise sales. For example, a user inputs a template. At this time, they input information such as the characteristics, traditions, selling points, and negative factors to consider for the region or company. For example, they might input information such as "I want to promote local specialties" or "I want to introduce a new product from my company." This information is input into the AI. Next, the AI ​​learns from the input information and generates a story and characters. The AI ​​creates original stories and characters based on the characteristics, traditions, selling points, and negative aspects of a region or company. For example, it can generate stories introducing local specialties or characters promoting a company's new products. Furthermore, it collaborates with voice acting agencies, music production companies, and animation studios to add voice acting and music to the animation, adding depth to the work. For instance, using the voices of famous voice actors or creating original music can enhance the quality of the animation. This system is characterized by its speed and ease of use. Users can create original animations in a short time simply by inputting templates. It also helps preserve culture by closely collaborating with the animation industry. For example, by collaborating with animation production companies, it can preserve Japanese animation culture while providing new PR methods. Moreover, the quality can be adjusted according to the budget.Users can choose the animation quality to match their budget. For example, they can create a simple animation on a low budget or a lavish animation on a high budget. This allows them to spread their appeal to a wide audience within reasonable limits. Furthermore, collaboration with voice actors and music production companies can open up new business opportunities such as concerts, events, and merchandise sales. For instance, selling merchandise featuring anime characters or holding concerts by voice actors can have various effects, such as revitalizing local communities, promoting products, and promoting attractive employees. In this way, the PR original animation generation system can effectively promote regions and companies.

[0029] The PR original animation generation system according to the embodiment comprises a reception unit, a generation unit, and an addition unit. The reception unit receives a template input from the user. The template includes, but is not limited to, examples of regional or corporate characteristics, traditions, selling points, and negative factors to be considered. The reception unit accepts templates in, for example, text or image format. The reception unit can also send the information entered by the user to the AI. The generation unit uses the AI ​​to generate a story and characters based on the information entered by the reception unit. The generation unit can generate, for example, a story introducing a local specialty product or a character promoting a company's new product. The generation unit uses the AI ​​to learn regional or corporate characteristics, traditions, selling points, and negative factors to be considered, and then creates a story and characters based on that. For example, the generation unit uses a text generation AI (e.g., LLM) to generate a story. The generation unit can also use an image generation AI to generate characters. The generation unit generates original stories and characters based on the information learned by the AI. For example, the generation unit generates a story introducing a local specialty product and creates a scene in which a character introduces that specialty product. The addition unit adds voice acting and music to the story and characters generated by the generation unit. The addition unit collaborates with, for example, voice acting agencies, music production companies, and animation companies to add voice acting and music to the animation. The addition unit uses AI to select voice acting and music and add them to the story and characters. For example, the addition unit enhances the quality of the animation by using the voices of famous voice actors or creating original music. The addition unit adds depth to the animation based on the voice acting and music selected by the AI. For example, the addition unit adjusts the voice acting to match the character and adds music in accordance with the progression of the story. As a result, the PR original animation generation system according to this embodiment can consistently perform everything from template input to story and character generation, and the addition of voice acting and music.

[0030] The reception desk accepts user input via templates. These templates may include, but are not limited to, regional or company characteristics, traditions, selling points, and negative factors to consider. The reception desk accepts templates in text and image formats, for example. Specifically, users can use a dedicated interface to describe in detail local specialties, company history, and product features. For example, they might enter "famous local fruit" or "traditional crafts" as local specialties, and "innovative technology" or "environmentally friendly products" as company characteristics. They can also enter negative factors such as "past scandals" or "the presence of competitors." The reception desk can accept this information not only in text format but also in image format. For example, users can upload local scenery, company logos, or product photos. Furthermore, the reception desk can also send the information entered by users to an AI. Specifically, the reception desk analyzes the text and image data entered by users and converts it into a format that the AI ​​can easily understand. For example, text data is analyzed using natural language processing technology, and image data is analyzed using image recognition technology. This allows the AI ​​to accurately understand the information provided by the user and utilize it in the next step, the generation phase.

[0031] The generation unit uses AI to generate stories and characters based on information entered by the reception unit. For example, the generation unit can generate stories introducing local specialties or characters promoting a company's new products. Specifically, the generation unit uses text generation AI (e.g., LLM) to generate stories. LLM automatically generates stories incorporating local specialties and company characteristics based on information provided by the user. For example, a story introducing local specialties can describe the history of the specialties, the manufacturing process, and how they are involved in the lives of local people. When generating characters to promote a company's new products, image generation AI is used to design the characters. The image generation AI automatically generates characters that reflect the company's image and product characteristics based on information provided by the user. For example, a character promoting environmentally friendly products can incorporate designs and colors that symbolize nature. The generation unit uses AI to learn the characteristics, traditions, selling points, and negative factors to consider for the region and company, and then creates stories and characters based on that. For example, the generation unit generates a story introducing local specialties and creates scenes in which the character introduces those specialties. This allows the generation unit to create original stories and characters based on information provided by the user, thereby building the foundation for original promotional animations.

[0032] The addition unit adds voice acting and music to the story and characters generated by the generation unit. Specifically, the addition unit collaborates with voice acting agencies, music production companies, and animation studios to add voice acting and music to the animation. For example, it collaborates with voice acting agencies to select voice actors suitable for the characters and to record their lines. AI can be used to select voice actors whose voices best suit the character's personality and the atmosphere of the story. For example, a cheerful and energetic voice actor will be selected for a lively and bright character, and a calm voice actor will be selected for a character with a calm demeanor. The addition unit also collaborates with music production companies to create music that matches the story. AI can also be used to select music that matches the progression of the story and the characters' emotions. For example, music with a melody that enhances the emotion will be selected for emotional scenes, and music with a rhythm that heightens the tension will be selected for action scenes. Based on the voice acting and music selected by the AI, the addition unit adds depth to the animation. Specifically, it adjusts the voice actors' voices to match the characters and adds music in accordance with the progression of the story. For example, the voice actor's voice can be synchronized with the character's mouth movements, and the music can be faded in and out according to scene changes. This allows the additional unit to add voice acting and music to the story and characters generated by the generation unit, creating a high-quality original PR animation.

[0033] The generation unit can learn the characteristics, traditions, selling points, and negative factors to consider for a region or company, and generate stories and characters based on that information. For example, the generation unit can learn the history and culture of a region and generate stories based on that information. For example, the generation unit can generate stories that introduce local specialties and tourist attractions. The generation unit can also learn the brand image and product characteristics of a company and generate characters based on that information. For example, the generation unit can generate characters that promote a company's new products. The generation unit generates original stories and characters based on the information learned by the AI. For example, the generation unit can generate a story that introduces local specialties and create scenes in which characters introduce those specialties. This makes it possible to generate stories and characters that reflect the characteristics of a region or company. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can generate stories and characters using a generation AI model that takes the characteristics, traditions, selling points, and negative factors to consider for a region or company as input and outputs stories and characters.

[0034] The add-on unit can collaborate with voice acting agencies, music production companies, and animation studios to add voice acting and music to anime. For example, the add-on unit can contract with voice acting agencies and add voice actors' voices to anime. For example, the add-on unit can enhance the quality of anime by using the voices of famous voice actors or creating original music. The add-on unit can also collaborate with music production companies to add original music to anime. For example, the add-on unit can collaborate with music production companies to create music and add it to anime. The add-on unit can also collaborate with animation studios to integrate the anime production process. For example, the add-on unit can collaborate with animation studios to produce anime and add voice acting and music. This adds depth to anime by adding voice acting and music. Some or all of the above processes in the add-on unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the add-on unit can input voice acting and music data into a generative AI and have the generative AI execute the creation of the audio and music to be added to the anime.

[0035] The additional section can adjust the animation quality according to the budget. For example, the additional section can create a simple animation on a low budget. For example, the additional section can reduce the number of animation frames and create an animation with a simple design. The additional section can also create a lavish animation on a high budget. For example, the additional section can increase the number of animation frames and create an animation with a detailed design. Furthermore, the additional section can select the technology to use according to the budget. For example, the additional section can use simple technology for low budgets and advanced technology for high budgets. This allows the animation quality to be adjusted according to the budget. Some or all of the above processing in the additional section may be performed using, for example, a generative AI, or not using a generative AI. For example, the additional section can input budget information into a generative AI and have the generative AI execute animation of a quality appropriate to the budget.

[0036] The additional unit can explore new business opportunities such as concerts, events, and merchandise sales. For example, the additional unit could sell merchandise featuring anime characters. For instance, it could produce and sell character figurines, posters, T-shirts, etc. The additional unit could also organize concerts featuring voice actors. For example, it could plan and hold live performances by voice actors. The additional unit could also organize events related to anime. For example, it could plan and hold anime screenings or fan meetings. This would open up new business opportunities. Some or all of the above-mentioned processes in the additional unit may be performed using, for example, a generative AI, or not. For example, the additional unit could input information about business opportunities into a generative AI and have the generative AI suggest the most suitable business opportunities.

[0037] The reception area allows users to input regional and corporate characteristics, traditions, selling points, and any negative factors they wish to consider. For example, the reception area allows users to input local specialties, tourist attractions, new products, and brand image of a company. For example, the reception area provides an input form where users can enter information in text format. The reception area can also accept information in image format. For example, the reception area allows users to upload images of local scenery or company logos. This allows for the specific input of regional and corporate characteristics. Some or all of the above processing in the reception area may be performed using, for example, a generative AI, or not. For example, the reception area can send the information entered by the user to a generative AI, which can analyze the information and generate stories and characters.

[0038] The reception desk can analyze the user's past input history and suggest the optimal template input method. For example, the reception desk can automatically display template items that the user has frequently entered in the past as candidates. For example, the reception desk can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest template items to be used during specific time periods based on the user's past input history. For example, the reception desk can suggest the optimal template input method based on items the user has entered during specific time periods in the past. In this way, the reception desk can suggest the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history data into a generative AI and have the generative AI suggest the optimal template input method.

[0039] The input field can automatically complete input fields based on the user's current projects and areas of interest when entering data into a template. For example, the input field can automatically complete template fields related to the user's current ongoing projects. For example, the input field can suggest relevant template fields based on the user's areas of interest. The input field can also automatically complete appropriate template fields based on areas the user has previously shown interest in. For example, the input field can automatically complete relevant template fields based on areas the user has previously shown interest in. This improves input efficiency by automatically completing input fields based on the current projects and areas of interest. Some or all of the above processing in the input field may be performed using, for example, a generative AI, or not using a generative AI. For example, the input field can input information about the user's projects and areas of interest into a generative AI and have the generative AI perform the automatic completion of input fields.

[0040] The reception desk can prioritize displaying templates that are highly relevant to the user, taking into account the user's geographical location when they input a template. For example, if the user is in a specific region, the reception desk will prioritize displaying templates related to that region. For example, if the user is traveling, the reception desk will suggest relevant templates based on their current location. The reception desk can also prioritize displaying templates related to an event if the user is participating in that event. For example, if the reception desk is participating in a specific event, the reception desk will prioritize displaying templates related to that event. In this way, by considering geographical location information, highly relevant templates can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI suggest highly relevant templates.

[0041] The reception unit can analyze the user's social media activity when a template is entered and suggest relevant templates. For example, the reception unit can suggest relevant templates based on topics that the user frequently mentions on social media. For example, the reception unit can suggest appropriate templates based on topics that the user's social media followers are interested in. The reception unit can also analyze the content of the user's social media posts and suggest relevant templates. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant templates. In this way, relevant templates can be suggested by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI and have the generative AI suggest relevant templates.

[0042] The generation unit can adjust the level of detail in story and character generation based on the characteristics of the region or company. For example, it can generate stories that emphasize local specialties or tourist attractions. For example, it can generate characters that describe the history or product characteristics of a company in detail. The generation unit can also adjust the level of detail in stories and characters by considering the cultural background of the region or company. For example, it can generate stories that reflect the culture and traditions of the region and create scenes in which characters introduce those cultures and traditions. By adjusting the level of detail in generation based on the characteristics of the region or company, more specific stories and characters can be generated. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or not. For example, the generation unit can input regional or company characteristic data into the generation AI and have the generation AI perform the process of adjusting the level of detail in generation.

[0043] The generation unit can achieve diverse expressions by applying different generation algorithms when generating stories and characters. For example, the generation unit can generate multiple stories by applying different generation algorithms, providing the user with choices. For example, the generation unit can express diverse appearances and personalities of characters by applying different generation algorithms. The generation unit can also express diverse story progression and endings by applying different generation algorithms. For example, the generation unit can express diverse story progression and endings by applying different generation algorithms. In this way, diverse expressions can be achieved by applying different generation algorithms. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can have a generation AI execute different generation algorithms to generate diverse stories and characters.

[0044] The generation unit can adjust the generated content by considering the historical background of the region or company when generating stories and characters. For example, the generation unit can generate stories based on historical events in the region. For example, the generation unit can generate characters that incorporate episodes from the company's founding. The generation unit can also adjust the details of stories and characters by considering the historical background of the region or company. For example, the generation unit can generate a story based on historical events in the region and create a scene in which a character introduces those events. This allows for the generation of more specific stories and characters by considering the historical background of the region or company. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input historical background data of the region or company into a generation AI and have the generation AI perform the adjustment of the generated content.

[0045] The generation unit can improve the accuracy of story and character generation by referring to relevant literature and materials. For example, the generation unit can improve the accuracy of the story by referring to literature on local specialties and tourist attractions. For example, the generation unit can refine the details of a character by referring to materials on a company's products and services. The generation unit can also improve the accuracy of story and character generation by referring to relevant literature and materials. For example, the generation unit can improve the accuracy of the story by referring to literature on local specialties and tourist attractions. In this way, the accuracy of generation can be improved by referring to relevant literature and materials. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data from relevant literature and materials into a generation AI and have the generation AI perform the generation accuracy improvement.

[0046] The additional section can select the most suitable sound effects based on the anime's theme when adding voice acting and music. For example, the additional section can select powerful sound effects for action scenes, sound effects that enhance emotions for romantic scenes, and humorous sound effects for comedy scenes. By selecting the most suitable sound effects based on the anime's theme, a more consistent sound expression becomes possible. Some or all of the above processing in the additional section may be performed using, for example, a generative AI, or without a generative AI. For example, the additional section can input anime theme information into a generative AI and have the generative AI select the most suitable sound effects.

[0047] The additional section can achieve diverse sound expressions by applying different acoustic algorithms when adding voice actors' voices or music. For example, the additional section can generate multiple sound effects by applying different acoustic algorithms, providing the user with choices. For example, the additional section can express diverse tones and pitches of voice actors' voices by applying different acoustic algorithms. The additional section can also express diverse rhythms and melodies of music by applying different acoustic algorithms. For example, the additional section can express diverse rhythms and melodies of music by applying different acoustic algorithms. In this way, diverse sound expressions can be achieved by applying different acoustic algorithms. Some or all of the above processing in the additional section may be performed using, for example, a generative AI, or without using a generative AI. For example, the additional section can have a generative AI execute different acoustic algorithms to generate diverse sound effects.

[0048] The additional section can adjust the sound content when adding voice actors' voices and music, taking into account the cultural background of the region and company. For example, the additional section can select sound effects that incorporate local traditional music. For example, the additional section can select sound effects that match the company's brand image. Furthermore, the additional section can adjust the tone and style of voice actors' voices and music, taking into account the cultural background of the region and company. For example, the additional section can select sound effects that incorporate local traditional music and sound effects that match the company's brand image. This makes it possible to achieve more appropriate sound expression by considering the cultural background of the region and company. Some or all of the above processing in the additional section may be performed using, for example, a generative AI, or not using a generative AI. For example, the additional section can input cultural background data of the region and company into a generative AI and have the generative AI perform the adjustment of the sound content.

[0049] The addition function can improve the accuracy of the additions by referencing related music and the voice actors' past works when adding voices and music. For example, the addition function can refer to a voice actor's past works to select the voice best suited to the character. For example, the addition function can refer to a music production company's past works to select music that matches the anime's theme. The addition function can also improve the accuracy of the sound by referencing related music and the voice actors' past works. For example, the addition function can refer to a voice actor's past works to select the voice best suited to the character, and refer to a music production company's past works to select music that matches the anime's theme. In this way, the accuracy of the additions can be improved by referencing related music and the voice actors' past works. Some or all of the above processing in the addition function may be performed using, for example, a generative AI, or not using a generative AI. For example, the addition function can input data on related music and voice actors' past works into a generative AI and have the generative AI perform the accuracy improvement of the additions.

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

[0051] The reception desk can analyze a user's past input history when they enter information into a template and suggest the most suitable method of template input. For example, the reception desk can automatically display template items that the user has frequently entered in the past as suggestions. It 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 template items that the user will use at specific times of the day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the most suitable input method.

[0052] The generation unit can adjust the generated content by considering the historical background of the region or company when generating stories and characters. For example, the generation unit can generate stories based on historical events in the region. It can also generate characters that incorporate episodes from the company's founding. Furthermore, the generation unit can adjust the details of the stories and characters by considering the historical background of the region or company. This allows for the generation of more specific stories and characters by taking the historical background of the region or company into consideration.

[0053] The additional section allows for the selection of optimal sound effects based on the anime's theme when adding voice acting and music. For example, it can select powerful sound effects for action scenes, emotionally resonant sound effects for romantic scenes, and humorous sound effects for comedic scenes. By selecting the most suitable sound effects based on the anime's theme, a more consistent sound expression becomes possible.

[0054] The reception system can prioritize displaying templates that are highly relevant to the user's geographical location when they input a template. For example, if a user is in a specific region, templates related to that region can be displayed preferentially. If a user is traveling, relevant templates can be suggested based on their current location. Furthermore, if a user is participating in a specific event, templates related to that event can be displayed preferentially. This allows for the display of highly relevant templates by considering geographical location.

[0055] The generation unit can improve the accuracy of story and character generation by referring to relevant literature and materials. For example, the generation unit can improve the accuracy of a story by referring to literature on local specialties and tourist attractions. It can also refine character details by referring to materials on a company's products and services. Furthermore, it can improve the accuracy of story and character generation by referring to relevant literature and materials. In this way, the accuracy of generation can be improved by referring to relevant literature and materials.

[0056] The additional features allow for adjustment of sound content when adding voice actors' voices and music, taking into account the cultural background of the region or company. For example, sound effects incorporating local traditional music can be selected. Alternatively, sound effects that match the company's brand image can be chosen. Furthermore, the tone and style of the voice actors' voices and music can be adjusted to reflect the cultural background of the region or company. This allows for more appropriate sound expression by considering the cultural context of the region or company.

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

[0058] Step 1: The reception desk receives the user's input into a template. The template may include, for example, regional or company characteristics, traditions, selling points, and any negative factors to consider. The reception desk accepts templates in text or image format. The reception desk can also send the user's input information to the AI. Step 2: The generation unit uses AI to generate stories and characters based on the information entered by the reception unit. The generation unit can generate stories introducing local specialty products or characters promoting a company's new products. The generation unit can generate stories using text generation AI (e.g., LLM) and characters using image generation AI. Step 3: The Addition Unit adds voice acting and music to the story and characters generated by the Generation Unit. The Addition Unit collaborates with voice acting agencies, music production companies, and animation studios, using AI to select voice actors' voices and music, and adds them to the story and characters. For example, the Addition Unit enhances the quality of the animation by using the voices of famous voice actors or creating original music.

[0059] (Example of form 2) The PR original animation generation system according to an embodiment of the present invention is a system that generates original PR animations for regions and companies using AI technology. This PR original animation generation system allows users to input templates, and the AI ​​learns the characteristics, traditions, selling points, and negative factors to consider for the region or company, and then generates a story and characters based on this information. Furthermore, by collaborating with voice acting agencies, music production companies, and animation studios, the system adds depth to the animation by incorporating voice acting and music. This system is characterized by its speed and ease of use, and by closely collaborating with the animation industry, it can protect culture. It can also adjust the quality according to the budget, allowing for the widespread dissemination of appeal to the world within reasonable limits. Moreover, collaboration with voice actors and music production companies can open up new business opportunities such as concerts, events, and merchandise sales. For example, a user inputs a template. At this time, they input information such as the characteristics, traditions, selling points, and negative factors to consider for the region or company. For example, they might input information such as "I want to promote local specialties" or "I want to introduce a new product from my company." This information is input into the AI. Next, the AI ​​learns from the input information and generates a story and characters. The AI ​​creates original stories and characters based on the characteristics, traditions, selling points, and negative aspects of a region or company. For example, it can generate stories introducing local specialties or characters promoting a company's new products. Furthermore, it collaborates with voice acting agencies, music production companies, and animation studios to add voice acting and music to the animation, adding depth to the work. For instance, using the voices of famous voice actors or creating original music can enhance the quality of the animation. This system is characterized by its speed and ease of use. Users can create original animations in a short time simply by inputting templates. It also helps preserve culture by closely collaborating with the animation industry. For example, by collaborating with animation production companies, it can preserve Japanese animation culture while providing new PR methods. Moreover, the quality can be adjusted according to the budget.Users can choose the animation quality to match their budget. For example, they can create a simple animation on a low budget or a lavish animation on a high budget. This allows them to spread their appeal to a wide audience within reasonable limits. Furthermore, collaboration with voice actors and music production companies can open up new business opportunities such as concerts, events, and merchandise sales. For instance, selling merchandise featuring anime characters or holding concerts by voice actors can have various effects, such as revitalizing local communities, promoting products, and promoting attractive employees. In this way, the PR original animation generation system can effectively promote regions and companies.

[0060] The PR original animation generation system according to the embodiment comprises a reception unit, a generation unit, and an addition unit. The reception unit receives a template input from the user. The template includes, but is not limited to, examples of regional or corporate characteristics, traditions, selling points, and negative factors to be considered. The reception unit accepts templates in, for example, text or image format. The reception unit can also send the information entered by the user to the AI. The generation unit uses the AI ​​to generate a story and characters based on the information entered by the reception unit. The generation unit can generate, for example, a story introducing a local specialty product or a character promoting a company's new product. The generation unit uses the AI ​​to learn regional or corporate characteristics, traditions, selling points, and negative factors to be considered, and then creates a story and characters based on that. For example, the generation unit uses a text generation AI (e.g., LLM) to generate a story. The generation unit can also use an image generation AI to generate characters. The generation unit generates original stories and characters based on the information learned by the AI. For example, the generation unit generates a story introducing a local specialty product and creates a scene in which a character introduces that specialty product. The addition unit adds voice acting and music to the story and characters generated by the generation unit. The addition unit collaborates with, for example, voice acting agencies, music production companies, and animation companies to add voice acting and music to the animation. The addition unit uses AI to select voice acting and music and add them to the story and characters. For example, the addition unit enhances the quality of the animation by using the voices of famous voice actors or creating original music. The addition unit adds depth to the animation based on the voice acting and music selected by the AI. For example, the addition unit adjusts the voice acting to match the character and adds music in accordance with the progression of the story. As a result, the PR original animation generation system according to this embodiment can consistently perform everything from template input to story and character generation, and the addition of voice acting and music.

[0061] The reception desk accepts user input via templates. These templates may include, but are not limited to, regional or company characteristics, traditions, selling points, and negative factors to consider. The reception desk accepts templates in text and image formats, for example. Specifically, users can use a dedicated interface to describe in detail local specialties, company history, and product features. For example, they might enter "famous local fruit" or "traditional crafts" as local specialties, and "innovative technology" or "environmentally friendly products" as company characteristics. They can also enter negative factors such as "past scandals" or "the presence of competitors." The reception desk can accept this information not only in text format but also in image format. For example, users can upload local scenery, company logos, or product photos. Furthermore, the reception desk can also send the information entered by users to an AI. Specifically, the reception desk analyzes the text and image data entered by users and converts it into a format that the AI ​​can easily understand. For example, text data is analyzed using natural language processing technology, and image data is analyzed using image recognition technology. This allows the AI ​​to accurately understand the information provided by the user and utilize it in the next step, the generation phase.

[0062] The generation unit uses AI to generate stories and characters based on information entered by the reception unit. For example, the generation unit can generate stories introducing local specialties or characters promoting a company's new products. Specifically, the generation unit uses text generation AI (e.g., LLM) to generate stories. LLM automatically generates stories incorporating local specialties and company characteristics based on information provided by the user. For example, a story introducing local specialties can describe the history of the specialties, the manufacturing process, and how they are involved in the lives of local people. When generating characters to promote a company's new products, image generation AI is used to design the characters. The image generation AI automatically generates characters that reflect the company's image and product characteristics based on information provided by the user. For example, a character promoting environmentally friendly products can incorporate designs and colors that symbolize nature. The generation unit uses AI to learn the characteristics, traditions, selling points, and negative factors to consider for the region and company, and then creates stories and characters based on that. For example, the generation unit generates a story introducing local specialties and creates scenes in which the character introduces those specialties. This allows the generation unit to create original stories and characters based on information provided by the user, thereby building the foundation for original promotional animations.

[0063] The addition unit adds voice acting and music to the story and characters generated by the generation unit. Specifically, the addition unit collaborates with voice acting agencies, music production companies, and animation studios to add voice acting and music to the animation. For example, it collaborates with voice acting agencies to select voice actors suitable for the characters and to record their lines. AI can be used to select voice actors whose voices best suit the character's personality and the atmosphere of the story. For example, a cheerful and energetic voice actor will be selected for a lively and bright character, and a calm voice actor will be selected for a character with a calm demeanor. The addition unit also collaborates with music production companies to create music that matches the story. AI can also be used to select music that matches the progression of the story and the characters' emotions. For example, music with a melody that enhances the emotion will be selected for emotional scenes, and music with a rhythm that heightens the tension will be selected for action scenes. Based on the voice acting and music selected by the AI, the addition unit adds depth to the animation. Specifically, it adjusts the voice actors' voices to match the characters and adds music in accordance with the progression of the story. For example, the voice actor's voice can be synchronized with the character's mouth movements, and the music can be faded in and out according to scene changes. This allows the additional unit to add voice acting and music to the story and characters generated by the generation unit, creating a high-quality original PR animation.

[0064] The generation unit can learn the characteristics, traditions, selling points, and negative factors to consider for a region or company, and generate stories and characters based on that information. For example, the generation unit can learn the history and culture of a region and generate stories based on that information. For example, the generation unit can generate stories that introduce local specialties and tourist attractions. The generation unit can also learn the brand image and product characteristics of a company and generate characters based on that information. For example, the generation unit can generate characters that promote a company's new products. The generation unit generates original stories and characters based on the information learned by the AI. For example, the generation unit can generate a story that introduces local specialties and create scenes in which characters introduce those specialties. This makes it possible to generate stories and characters that reflect the characteristics of a region or company. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can generate stories and characters using a generation AI model that takes the characteristics, traditions, selling points, and negative factors to consider for a region or company as input and outputs stories and characters.

[0065] The add-on unit can collaborate with voice acting agencies, music production companies, and animation studios to add voice acting and music to anime. For example, the add-on unit can contract with voice acting agencies and add voice actors' voices to anime. For example, the add-on unit can enhance the quality of anime by using the voices of famous voice actors or creating original music. The add-on unit can also collaborate with music production companies to add original music to anime. For example, the add-on unit can collaborate with music production companies to create music and add it to anime. The add-on unit can also collaborate with animation studios to integrate the anime production process. For example, the add-on unit can collaborate with animation studios to produce anime and add voice acting and music. This adds depth to anime by adding voice acting and music. Some or all of the above processes in the add-on unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the add-on unit can input voice acting and music data into a generative AI and have the generative AI execute the creation of the audio and music to be added to the anime.

[0066] The additional section can adjust the animation quality according to the budget. For example, the additional section can create a simple animation on a low budget. For example, the additional section can reduce the number of animation frames and create an animation with a simple design. The additional section can also create a lavish animation on a high budget. For example, the additional section can increase the number of animation frames and create an animation with a detailed design. Furthermore, the additional section can select the technology to use according to the budget. For example, the additional section can use simple technology for low budgets and advanced technology for high budgets. This allows the animation quality to be adjusted according to the budget. Some or all of the above processing in the additional section may be performed using, for example, a generative AI, or not using a generative AI. For example, the additional section can input budget information into a generative AI and have the generative AI execute animation of a quality appropriate to the budget.

[0067] The additional unit can explore new business opportunities such as concerts, events, and merchandise sales. For example, the additional unit could sell merchandise featuring anime characters. For instance, it could produce and sell character figurines, posters, T-shirts, etc. The additional unit could also organize concerts featuring voice actors. For example, it could plan and hold live performances by voice actors. The additional unit could also organize events related to anime. For example, it could plan and hold anime screenings or fan meetings. This would open up new business opportunities. Some or all of the above-mentioned processes in the additional unit may be performed using, for example, a generative AI, or not. For example, the additional unit could input information about business opportunities into a generative AI and have the generative AI suggest the most suitable business opportunities.

[0068] The reception area allows users to input regional and corporate characteristics, traditions, selling points, and any negative factors they wish to consider. For example, the reception area allows users to input local specialties, tourist attractions, new products, and brand image of a company. For example, the reception area provides an input form where users can enter information in text format. The reception area can also accept information in image format. For example, the reception area allows users to upload images of local scenery or company logos. This allows for the specific input of regional and corporate characteristics. Some or all of the above processing in the reception area may be performed using, for example, a generative AI, or not. For example, the reception area can send the information entered by the user to a generative AI, which can analyze the information and generate stories and characters.

[0069] The reception desk can estimate the user's emotions and customize the template input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input if the user is in a hurry, allowing for quick template input. For example, if the user is in a hurry, the reception desk can prioritize displaying the most important items to allow for quick input. This allows for a more user-friendly system by customizing the interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the interface customization.

[0070] The reception desk can analyze the user's past input history and suggest the optimal template input method. For example, the reception desk can automatically display template items that the user has frequently entered in the past as candidates. For example, the reception desk can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest template items to be used during specific time periods based on the user's past input history. For example, the reception desk can suggest the optimal template input method based on items the user has entered during specific time periods in the past. In this way, the reception desk can suggest the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history data into a generative AI and have the generative AI suggest the optimal template input method.

[0071] The input field can automatically complete input fields based on the user's current projects and areas of interest when entering data into a template. For example, the input field can automatically complete template fields related to the user's current ongoing projects. For example, the input field can suggest relevant template fields based on the user's areas of interest. The input field can also automatically complete appropriate template fields based on areas the user has previously shown interest in. For example, the input field can automatically complete relevant template fields based on areas the user has previously shown interest in. This improves input efficiency by automatically completing input fields based on the current projects and areas of interest. Some or all of the above processing in the input field may be performed using, for example, a generative AI, or not using a generative AI. For example, the input field can input information about the user's projects and areas of interest into a generative AI and have the generative AI perform the automatic completion of input fields.

[0072] The reception desk can estimate the user's emotions and determine the priority of template inputs based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize displaying important items and simplify the input process. For example, if the user is relaxed, the reception desk may prioritize displaying detailed items and provide a customizable input method. The reception desk can also prioritize displaying the most important items and enabling quick input if the user is in a hurry. For example, if the reception desk is in a hurry, the reception desk may prioritize displaying the most important items and enabling quick input. This allows for more efficient input by determining the priority of inputs according to the user's emotions. 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. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of inputs.

[0073] The reception desk can prioritize displaying templates that are highly relevant to the user, taking into account the user's geographical location when they input a template. For example, if the user is in a specific region, the reception desk will prioritize displaying templates related to that region. For example, if the user is traveling, the reception desk will suggest relevant templates based on their current location. The reception desk can also prioritize displaying templates related to an event if the user is participating in that event. For example, if the reception desk is participating in a specific event, the reception desk will prioritize displaying templates related to that event. In this way, by considering geographical location information, highly relevant templates can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI suggest highly relevant templates.

[0074] The reception unit can analyze the user's social media activity when a template is entered and suggest relevant templates. For example, the reception unit can suggest relevant templates based on topics that the user frequently mentions on social media. For example, the reception unit can suggest appropriate templates based on topics that the user's social media followers are interested in. The reception unit can also analyze the content of the user's social media posts and suggest relevant templates. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant templates. In this way, relevant templates can be suggested by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI and have the generative AI suggest relevant templates.

[0075] The generation unit can estimate the user's emotions and adjust the way the story and characters are presented based on those estimated emotions. For example, if the user is relaxed, the generation AI will generate a story that progresses at a leisurely pace. For example, if the user is in a hurry, the generation AI will generate a story that emphasizes the shortest route. Furthermore, if the user is excited, the generation AI can generate a story with visually stimulating effects. For example, if the user is excited, the generation AI will generate a story with visually stimulating effects. This allows for more appropriate expression by adjusting the way the story and characters are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the story and characters are expressed.

[0076] The generation unit can adjust the level of detail in story and character generation based on the characteristics of the region or company. For example, it can generate stories that emphasize local specialties or tourist attractions. For example, it can generate characters that describe the history or product characteristics of a company in detail. The generation unit can also adjust the level of detail in stories and characters by considering the cultural background of the region or company. For example, it can generate stories that reflect the culture and traditions of the region and create scenes in which characters introduce those cultures and traditions. By adjusting the level of detail in generation based on the characteristics of the region or company, more specific stories and characters can be generated. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or not. For example, the generation unit can input regional or company characteristic data into the generation AI and have the generation AI perform the process of adjusting the level of detail in generation.

[0077] The generation unit can achieve diverse expressions by applying different generation algorithms when generating stories and characters. For example, the generation unit can generate multiple stories by applying different generation algorithms, providing the user with choices. For example, the generation unit can express diverse appearances and personalities of characters by applying different generation algorithms. The generation unit can also express diverse story progression and endings by applying different generation algorithms. For example, the generation unit can express diverse story progression and endings by applying different generation algorithms. In this way, diverse expressions can be achieved by applying different generation algorithms. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can have a generation AI execute different generation algorithms to generate diverse stories and characters.

[0078] The generation unit can estimate the user's emotions and adjust the length of the story and characters based on the estimated emotions. For example, if the user is in a hurry, the generation unit's AI can generate a short, concise story. For example, if the user is relaxed, the generation unit's AI can generate a longer story with detailed explanations. The generation unit can also generate a story with visually stimulating effects if the user is excited. For example, if the user is excited, the generation unit's AI can generate a story with visually stimulating effects. This allows for the generation of stories and characters of more appropriate lengths by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the story and characters.

[0079] The generation unit can adjust the generated content by considering the historical background of the region or company when generating stories and characters. For example, the generation unit can generate stories based on historical events in the region. For example, the generation unit can generate characters that incorporate episodes from the company's founding. The generation unit can also adjust the details of stories and characters by considering the historical background of the region or company. For example, the generation unit can generate a story based on historical events in the region and create a scene in which a character introduces those events. This allows for the generation of more specific stories and characters by considering the historical background of the region or company. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input historical background data of the region or company into a generation AI and have the generation AI perform the adjustment of the generated content.

[0080] The generation unit can improve the accuracy of story and character generation by referring to relevant literature and materials. For example, the generation unit can improve the accuracy of the story by referring to literature on local specialties and tourist attractions. For example, the generation unit can refine the details of a character by referring to materials on a company's products and services. The generation unit can also improve the accuracy of story and character generation by referring to relevant literature and materials. For example, the generation unit can improve the accuracy of the story by referring to literature on local specialties and tourist attractions. In this way, the accuracy of generation can be improved by referring to relevant literature and materials. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data from relevant literature and materials into a generation AI and have the generation AI perform the generation accuracy improvement.

[0081] The additional section can estimate the user's emotions and select voice actors' voices and music based on the estimated emotions. For example, if the user is relaxed, the additional section will select calm voice actors' voices and gentle music. For example, if the user is excited, the additional section will select energetic voice actors' voices and upbeat music. Furthermore, if the user is moved, the additional section can select voice actors' voices that enhance those emotions and emotional music. For example, if the user is moved, the additional section will select voice actors' voices that enhance those emotions and emotional music. This allows for more appropriate sound expression by selecting voice actors' voices and music according to the user's emotions. 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. Some or all of the processing described above in the additional section may be performed using a generative AI, for example, or without a generative AI. For example, the additional section can input user emotion data into a generating AI and have the AI ​​select voice actors' voices and music.

[0082] The additional section can select the most suitable sound effects based on the anime's theme when adding voice acting and music. For example, the additional section can select powerful sound effects for action scenes, sound effects that enhance emotions for romantic scenes, and humorous sound effects for comedy scenes. By selecting the most suitable sound effects based on the anime's theme, a more consistent sound expression becomes possible. Some or all of the above processing in the additional section may be performed using, for example, a generative AI, or without a generative AI. For example, the additional section can input anime theme information into a generative AI and have the generative AI select the most suitable sound effects.

[0083] The additional section can achieve diverse sound expressions by applying different acoustic algorithms when adding voice actors' voices or music. For example, the additional section can generate multiple sound effects by applying different acoustic algorithms, providing the user with choices. For example, the additional section can express diverse tones and pitches of voice actors' voices by applying different acoustic algorithms. The additional section can also express diverse rhythms and melodies of music by applying different acoustic algorithms. For example, the additional section can express diverse rhythms and melodies of music by applying different acoustic algorithms. In this way, diverse sound expressions can be achieved by applying different acoustic algorithms. Some or all of the above processing in the additional section may be performed using, for example, a generative AI, or without using a generative AI. For example, the additional section can have a generative AI execute different acoustic algorithms to generate diverse sound effects.

[0084] The additional section can estimate the user's emotions and prioritize voice actors' voices and music based on the estimated emotions. For example, if the user is nervous, the additional section will prioritize calm voice actors' voices and gentle music. For example, if the user is relaxed, the additional section will prioritize voice actors' voices and music that enhance emotions. The additional section can also prioritize voice actors' voices and music that can be selected quickly if the user is in a hurry. For example, if the additional section is in a hurry, it will prioritize voice actors' voices and music that can be selected quickly. This allows for more appropriate sound expression by prioritizing voice actors' voices and music according to the user's emotions. 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. Some or all of the processing described above in the additional section may be performed using a generative AI, for example, or without a generative AI. For example, the additional section can input user emotion data into the generating AI, allowing the AI ​​to determine the priority of voice actors' voices and music.

[0085] The additional section can adjust the sound content when adding voice actors' voices and music, taking into account the cultural background of the region and company. For example, the additional section can select sound effects that incorporate local traditional music. For example, the additional section can select sound effects that match the company's brand image. Furthermore, the additional section can adjust the tone and style of voice actors' voices and music, taking into account the cultural background of the region and company. For example, the additional section can select sound effects that incorporate local traditional music and sound effects that match the company's brand image. This makes it possible to achieve more appropriate sound expression by considering the cultural background of the region and company. Some or all of the above processing in the additional section may be performed using, for example, a generative AI, or not using a generative AI. For example, the additional section can input cultural background data of the region and company into a generative AI and have the generative AI perform the adjustment of the sound content.

[0086] The addition function can improve the accuracy of the additions by referencing related music and the voice actors' past works when adding voices and music. For example, the addition function can refer to a voice actor's past works to select the voice best suited to the character. For example, the addition function can refer to a music production company's past works to select music that matches the anime's theme. The addition function can also improve the accuracy of the sound by referencing related music and the voice actors' past works. For example, the addition function can refer to a voice actor's past works to select the voice best suited to the character, and refer to a music production company's past works to select music that matches the anime's theme. In this way, the accuracy of the additions can be improved by referencing related music and the voice actors' past works. Some or all of the above processing in the addition function may be performed using, for example, a generative AI, or not using a generative AI. For example, the addition function can input data on related music and voice actors' past works into a generative AI and have the generative AI perform the accuracy improvement of the additions.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The reception desk can analyze a user's past input history when they enter information into a template and suggest the most suitable method of template input. For example, the reception desk can automatically display template items that the user has frequently entered in the past as suggestions. It 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 template items that the user will use at specific times of the day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the most suitable input method.

[0089] The generation unit can adjust the generated content by considering the historical background of the region or company when generating stories and characters. For example, the generation unit can generate stories based on historical events in the region. It can also generate characters that incorporate episodes from the company's founding. Furthermore, the generation unit can adjust the details of the stories and characters by considering the historical background of the region or company. This allows for the generation of more specific stories and characters by taking the historical background of the region or company into consideration.

[0090] The additional section allows for the selection of optimal sound effects based on the anime's theme when adding voice acting and music. For example, it can select powerful sound effects for action scenes, emotionally resonant sound effects for romantic scenes, and humorous sound effects for comedic scenes. By selecting the most suitable sound effects based on the anime's theme, a more consistent sound expression becomes possible.

[0091] The reception desk can estimate the user's emotions and customize the template input interface based on those emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick template input. In this way, a more user-friendly system can be provided by customizing the interface according to the user's emotions.

[0092] The generation unit can estimate the user's emotions and adjust the way the story and characters are portrayed based on those emotions. For example, if the user is relaxed, it can generate a story that progresses at a leisurely pace. If the user is in a hurry, it can generate a story that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a story with visually stimulating effects. By adjusting the way the story and characters are portrayed according to the user's emotions, a more appropriate expression becomes possible.

[0093] The additional section can estimate the user's emotions and select voice actors' voices and music based on those estimated emotions. For example, if the user is relaxed, a calm voice actor's voice and gentle music can be selected. If the user is excited, an energetic voice actor's voice and upbeat music can be selected. Furthermore, if the user is moved, a voice actor's voice that enhances the emotion and emotional music can be selected. This allows for more appropriate sound expression by selecting voice actors' voices and music according to the user's emotions.

[0094] The reception system can prioritize displaying templates that are highly relevant to the user's geographical location when they input a template. For example, if a user is in a specific region, templates related to that region can be displayed preferentially. If a user is traveling, relevant templates can be suggested based on their current location. Furthermore, if a user is participating in a specific event, templates related to that event can be displayed preferentially. This allows for the display of highly relevant templates by considering geographical location.

[0095] The generation unit can improve the accuracy of story and character generation by referring to relevant literature and materials. For example, the generation unit can improve the accuracy of a story by referring to literature on local specialties and tourist attractions. It can also refine character details by referring to materials on a company's products and services. Furthermore, it can improve the accuracy of story and character generation by referring to relevant literature and materials. In this way, the accuracy of generation can be improved by referring to relevant literature and materials.

[0096] The additional features allow for adjustment of sound content when adding voice actors' voices and music, taking into account the cultural background of the region or company. For example, sound effects incorporating local traditional music can be selected. Alternatively, sound effects that match the company's brand image can be chosen. Furthermore, the tone and style of the voice actors' voices and music can be adjusted to reflect the cultural background of the region or company. This allows for more appropriate sound expression by considering the cultural context of the region or company.

[0097] The additional section can estimate the user's emotions and prioritize voice actors' voices and music based on those emotions. For example, if the user is nervous, calm voice actors' voices and gentle music can be prioritized. If the user is relaxed, voice actors' voices and music that enhance their emotions can be prioritized. Furthermore, if the user is in a hurry, voice actors' voices and music that can be selected quickly can be prioritized. By prioritizing voice actors' voices and music according to the user's emotions, more appropriate sound expression becomes possible.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The reception desk receives the user's input into a template. The template may include, for example, regional or company characteristics, traditions, selling points, and any negative factors to consider. The reception desk accepts templates in text or image format. The reception desk can also send the user's input information to the AI. Step 2: The generation unit uses AI to generate stories and characters based on the information entered by the reception unit. The generation unit can generate stories introducing local specialty products or characters promoting a company's new products. The generation unit can generate stories using text generation AI (e.g., LLM) and characters using image generation AI. Step 3: The Addition Unit adds voice acting and music to the story and characters generated by the Generation Unit. The Addition Unit collaborates with voice acting agencies, music production companies, and animation studios, using AI to select voice actors' voices and music, and adds them to the story and characters. For example, the Addition Unit enhances the quality of the animation by using the voices of famous voice actors or creating original music.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements, including the reception unit, generation unit, and addition unit described above, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input a template. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, using AI to generate stories and characters. The addition unit is implemented by the control unit 46A of the smart device 14, adding voice acting and music to the generated stories and characters. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements, including the reception unit, generation unit, and addition unit described above, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input a template. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, using AI to generate a story and characters. The addition unit is implemented by the control unit 46A of the smart glasses 214, adding voice acting and music to the generated story and characters. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements, including the reception unit, generation unit, and addition unit described above, is implemented in 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, allowing the user to input a template. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, using AI to generate stories and characters. The addition unit is implemented by the control unit 46A of the headset terminal 314, adding voice acting and music to the generated stories and characters. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements, including the reception unit, generation unit, and addition unit described above, is implemented in 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, allowing the user to input a template. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, using AI to generate stories and characters. The addition unit is implemented by the control unit 46A of the robot 414, adding voice acting and music to the generated stories and characters. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) The reception desk where you input the template, A generation unit that generates stories and characters based on the information entered by the reception unit, The system includes an additional unit that adds voice acting and music to the story and characters generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It learns the characteristics, traditions, selling points, and negative factors to consider for a region or company, and then generates stories and characters based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned additional part is, We collaborate with voice acting agencies, music production companies, and animation studios to add voice acting and music to anime. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned additional part is, Adjust the animation quality according to the budget. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned additional part is, We will explore new business opportunities such as concerts, events, and merchandise sales. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Enter the characteristics of the region or company, its traditions, selling points, and any negative factors you would like to consider. 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 customizes the template input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal template input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When filling out a template, input fields are automatically completed based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of template inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input templates, the system prioritizes displaying templates that are more relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input templates, the system analyzes their social media activity and suggests relevant templates. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way the story and characters are portrayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating stories and characters, adjust the level of detail based on the characteristics of the region and company. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is By applying different generation algorithms during story and character creation, diverse forms of expression can be achieved. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the story and characters based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When creating stories and characters, we adjust the generated content while taking into account the historical background of the region and company. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating stories and characters, we improve the accuracy of the generation process by referring to relevant literature and materials. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned additional part is, The system estimates the user's emotions and selects voice actors' voices and music based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned additional part is, When adding voice acting and music, the most suitable sound effects are selected based on the anime's theme. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned additional part is, When adding voice actors' voices and music, different acoustic algorithms are applied to achieve diverse sonic expressions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned additional part is, It estimates the user's emotions and determines the priority of voice actors' voices and music based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned additional part is, When adding voice acting or music, we adjust the sound content to take into account the cultural background of the region or company. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned additional part is, When adding voice actors' voices and music, we refer to related music and the voice actors' past works to improve the accuracy of the additions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 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 desk where you input the template, A generation unit that generates stories and characters based on the information entered by the reception unit, The system includes an additional unit that adds voice acting and music to the story and characters generated by the generation unit. A system characterized by the following features.

2. The generating unit is It learns the characteristics, traditions, selling points, and negative factors to consider for a region or company, and then generates stories and characters based on that information. The system according to feature 1.

3. The aforementioned additional part is, We collaborate with voice acting agencies, music production companies, and animation studios to add voice acting and music to anime. The system according to feature 1.

4. The aforementioned additional part is, Adjust the animation quality according to the budget. The system according to feature 1.

5. The aforementioned additional part is, We will explore new business opportunities such as concerts, events, and merchandise sales. The system according to feature 1.

6. The aforementioned reception unit is Enter the characteristics of the region or company, its traditions, selling points, and any negative factors you would like to consider. The system according to feature 1.

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

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal template input method. The system according to feature 1.

Images