System

Abstract

An object of the system according to the embodiment is to enable an individual to easily create and share a high-quality video work.SOLUTION: A system according to an embodiment includes a genre selection unit, a story generation unit, a character generation unit, a video generation unit, a music generation unit, and a sharing unit. The genre selection unit selects a genre or a theme. The story generation unit generates a story on the basis of the genre or theme selected by the genre selection unit. The character generation unit generates a character on the basis of the story generated by the story generation unit. The video generation unit generates a video on the basis of the character generated by the character generation unit. The music generation unit generates music based on the image generated by the image generation unit. The sharing unit shares a video work including the music generated by the music generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the production of video works requires a lot of time and effort, making it particularly difficult for individuals to easily create high-quality video works.

[0005] The system according to the embodiment aims to enable individuals to easily create and share high-quality video works. [Means for solving the problem]

[0006] The system according to the embodiment includes a genre selection unit, a story generation unit, a character generation unit, a video generation unit, a music generation unit, and a sharing unit. The genre selection unit selects a genre or theme. The story generation unit generates a story based on the genre or theme selected by the genre selection unit. The character generation unit generates a character based on the story generated by the story generation unit. The video generation unit generates a video based on the character generated by the character generation unit. The music generation unit generates music based on the video generated by the video generation unit. The sharing unit shares the video work including the music generated by the music generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows individuals to easily create and share high-quality video works. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The CineAI Studio system according to an embodiment of the present invention is a system in which generative AI automatically creates and shares stories, characters, images, and music based on a genre or theme selected by the customer. This allows customers to easily create and share original video works.

[0029] The CineAI Studio system according to the embodiment includes a genre selection unit, a story generation unit, a character generation unit, a video generation unit, a music generation unit, and a sharing unit. The genre selection unit allows a customer to select a genre or theme. For example, a customer can select a genre such as action, drama, comedy, horror, or fantasy, or a specific theme (e.g., friendship, adventure, love, or revenge). The story generation unit allows a generation AI to generate a story based on the genre or theme selected by the genre selection unit. For example, the generation AI creates a detailed story including characters, a plot, and a setting based on a genre or theme provided as a prompt. The character generation unit allows the generation AI to generate characters based on the story generated by the story generation unit. For example, the generation AI creates characters appropriate for the story by setting the appearance, personality, background, etc. of the characters appearing in the story in detail. The video generation unit allows the generation AI to generate video based on the characters generated by the character generation unit. For example, the generation AI sets video scenes, camera angles, special effects, etc. based on the story and characters, creating a visually appealing video work. The music generation unit allows the generation AI to generate music based on the video generated by the video generation unit. For example, the generative AI creates appropriate music to match the atmosphere and emotion of a scene and incorporates it into the video work. The sharing unit shares the video work that includes the music generated by the music generation unit. For example, the created video work can be shared on an online posting platform. Other users can view the shared work and comment and rate it. This allows the CineAI Studio system to allow customers to easily create and share original video works. For example, even people with no experience in filmmaking can create professional-looking video works with the help of AI. In addition, by receiving feedback from other users, they can identify areas for improvement in their work and use them in their next production.

[0030] The genre selection unit can analyze a customer's past selection history and suggest genres and themes based on the customer's preferences. For example, the genre selection unit stores the history of genres and themes selected by the customer in a database and uses that data to suggest the most suitable genre or theme for the next selection. For example, a customer who has frequently selected horror movies in the past can be suggested a new horror movie. The genre selection unit also analyzes the customer's selection history and refers to data on other customers with similar selection patterns to suggest the most suitable genre or theme. For example, it can suggest themes selected by other customers who like the same genre. The genre selection unit can also use generation AI to automatically discover new genres and themes based on the customer's selection history and suggest them to the customer. For example, it can suggest new genres that are highly related to genres selected in the past. This makes it possible to suggest genres and themes that suit individual preferences based on the customer's past selection history.

[0031] The genre selection unit can automatically provide the latest trends and news related to the genre or theme selected by the customer using the generation AI. For example, the genre selection unit automatically collects the latest movie and drama trend information related to the genre or theme selected by the customer and provides it to the customer. For example, it displays information on the latest hit movies and dramas. The genre selection unit also automatically collects news articles and blog posts related to the genre or theme selected by the customer to help with selection. For example, it displays movie critic reviews and industry news. The genre selection unit also collects trending information on social media related to the genre or theme selected by the customer to help with selection. For example, it displays information on movies and dramas that are trending on Twitter and Instagram. This allows the latest trends and news related to the genre or theme selected by the customer to be provided.

[0032] The story generation unit can generate a story so that it always includes specific scenes and events specified by the customer. For example, the story generation unit uses the generation AI to generate a story so that it always includes specific scenes and events specified by the customer. For example, it always includes a climax scene or a scene featuring a specific character. The story generation unit also uses the generation AI to build a story plot based on the scenes and events specified by the customer. For example, it develops the story around the specified scenes. The story generation unit also incorporates the scenes and events specified by the customer as important elements of the story, and the generation AI generates a story based on those elements. For example, it sets the specified event to be a turning point in the story. This makes it possible to generate a story that includes the specific scenes and events specified by the customer.

[0033] The story generation unit can present multiple plot options during the story generation process and allow the customer to select from them. For example, the story generation unit may present multiple plot options during the story generation process using a generation AI and allow the customer to select from them. For example, different endings or character fates may be presented as options. The story generation unit may also have the generation AI generate a story based on the plot option selected by the customer. For example, the development of the story may change depending on the selected option. The story generation unit may also have the generation AI present plot options in real time during the story generation process and allow the customer to select from them on the spot. For example, the generation AI may present options as the story progresses and continue the story based on the option selected by the customer. This allows the generation AI to present multiple plot options during the story generation process and allow the customer to select from them.

[0034] The character generation unit can reflect specific appearances and personality traits specified by the customer. For example, the character generation unit uses generation AI to generate characters based on the specific appearances and personality traits specified by the customer. For example, it can reflect hair color, eye shape, and personality traits. The character generation unit also generates characters that reflect appearances and personality traits related to the genre or theme selected by the customer. For example, it can reflect the appearance of elves or dragons that appear in fantasy works. The character generation unit also sets the detailed appearance and personality traits specified by the customer, and the generation AI generates characters based on that information. For example, it can reflect specific clothing, accessories, and personality traits. This makes it possible to generate characters that reflect the specific appearances and personality traits specified by the customer.

[0035] The character generation unit sets a character's background story in detail, and is able to clarify the character's actions and motivations. For example, the generation AI sets a character's background story in detail, and based on that information, clarifies the character's actions and motivations. For example, it sets the character's past events and family structure. The character generation unit also sets a background story related to the genre or theme selected by the customer, and based on that information, clarifies the character's actions and motivations. For example, it sets an adventurer's past adventures and failures. The character generation unit also sets a character's background story in detail, and based on that information, the generation AI generates the character's actions and motivations. For example, it sets a character with specific goals or dreams. This allows the character's background story to be set in detail, and actions and motivations to be clarified.

[0036] The video generation unit can reflect specific scenes and camera angles specified by the customer. For example, the video generation unit uses generation AI to generate video based on the specific scenes and camera angles specified by the customer. For example, it always includes a climax scene or a scene featuring a specific character. The video generation unit also generates video that reflects scenes and camera angles related to the genre or theme selected by the customer. For example, it sets dynamic camera angles for action scenes. The video generation unit also sets the scenes and camera angles specified by the customer in detail, and the generation AI generates video based on that information. For example, it can reflect specific viewpoints and camera work. This makes it possible to generate video that reflects the specific scenes and camera angles specified by the customer.

[0037] The video generation unit can present multiple video options during the video generation process, allowing the customer to select from them. For example, the video generation unit uses a generation AI to present multiple video options during the video generation process, allowing the customer to select from them. For example, different camera angles or scene variations are presented. The video generation unit also uses the generation AI to generate a video based on the video option selected by the customer. For example, the development of the video changes depending on the selected option. The video generation unit also uses the generation AI to present video options in real time during the video generation process, allowing the customer to select from them on the spot. For example, options are presented as the video is in progress, and the video continues based on the option selected by the customer. This allows multiple video options to be presented during the video generation process, allowing the customer to select from them.

[0038] The music generation unit can reflect specific instruments and musical styles specified by the customer. For example, the music generation unit uses the generative AI to generate music based on the specific instruments and musical styles specified by the customer. For example, it can reflect piano, guitar, classical, or jazz styles. The music generation unit also generates music that reflects instruments and musical styles related to the genre or theme selected by the customer. For example, it can set eerie music for a horror work or grand music for a fantasy work. The music generation unit also sets the instruments and musical styles specified by the customer in detail, and the generative AI generates music based on that information. For example, it can reflect specific rhythms and melodies. This makes it possible to generate music that reflects the specific instruments and musical styles specified by the customer.

[0039] The music generation unit can present multiple music options during the music generation process, allowing the customer to select from them. For example, the music generation unit may present multiple music options during the music generation process using a generation AI, allowing the customer to select from them. For example, different instrument arrangements and tempo variations may be presented. The music generation unit may also have the generation AI generate music based on the music options selected by the customer. For example, the mood or style of the music may change depending on the selected option. The music generation unit may also have the generation AI present music options in real time during the music generation process, allowing the customer to select from them on the spot. For example, the generation AI may present options while the music is playing, and continue the music based on the option selected by the customer. This allows multiple music options to be presented during the music generation process, allowing the customer to select from them.

[0040] The sharing unit can automatically analyze feedback on shared works and suggest improvements to customers. For example, the sharing unit can automatically analyze feedback on shared works and suggest specific improvements to customers. For example, it can analyze comments and ratings to extract improvements. The sharing unit also allows the generation AI to automatically suggest improvements based on feedback received by customers. For example, it can analyze the content of the feedback and present specific suggestions for correction. The sharing unit also builds a system in which the generation AI suggests improvements to customers based on the content of the feedback. For example, it can analyze trends in feedback to extract common improvements. This allows the sharing unit to automatically analyze feedback on shared works and suggest improvements to customers.

[0041] The sharing unit can provide a function that allows the generating AI to automatically improve the work based on the content of the feedback. The sharing unit, for example, provides a function that allows the generating AI to automatically improve the work based on the content of the feedback. For example, it analyzes comments and ratings and makes corrections automatically. The sharing unit also builds a system that allows the generating AI to automatically improve the work based on feedback received from customers. For example, it analyzes the content of the feedback and presents specific suggestions for correction. The sharing unit also provides a function that allows the generating AI to automatically improve the work based on the content of the feedback. For example, it analyzes trends in the feedback to extract common areas for improvement and makes corrections automatically. This makes it possible to provide a function that allows the generating AI to automatically improve the work based on the content of the feedback.

[0042] The sharing unit automatically translates feedback on shared works into different languages, allowing users to obtain feedback from an international perspective. For example, the sharing unit automatically translates feedback on shared works into different languages ​​and collects feedback from an international perspective. For example, the sharing unit translates into multiple languages, such as English, French, and Chinese. The sharing unit also builds a system that posts the translated feedback on a multilingual platform and obtains feedback from users around the world. The sharing unit also collects advice and suggestions for improvement from an international perspective based on the feedback translated into different languages, thereby improving the quality of the works. For example, the sharing unit reflects feedback that takes cultural and market differences into consideration. This allows the sharing unit to automatically translate feedback on shared works into different languages ​​and obtain feedback from an international perspective.

[0043] The sharing unit can recommend similar works created by other users based on the content of the feedback. For example, the sharing unit automatically recommends similar works created by other users based on the content of the feedback. For example, works in the same genre or theme are displayed. The sharing unit also builds a system that recommends similar works created by other users based on feedback received by customers. For example, the sharing unit analyzes the content of the feedback and displays related works. The sharing unit also recommends similar works created by other users based on the content of the feedback. For example, the sharing unit analyzes feedback trends and displays works with a common theme or genre. This makes it possible to recommend similar works created by other users based on the content of the feedback.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The CineAI Studio system can also add interactive elements. For example, it can provide an interactive feature that allows customers to change the storyline by making choices as the story progresses. It can also add a feature that allows customers to change character actions and lines in real time. It can also provide a feature that allows customers to record their own voices and dub characters. This allows customers to become more deeply involved in the story and create original video works that are tailored to their individual tastes.

[0046] The genre selection unit can further analyze the customer's past viewing history and rating data to suggest more accurate genres and themes. For example, new suggestions can be made based on the genres and themes of works that the customer has previously rated highly. It can also analyze the viewing time and frequency of works that the customer has viewed to suggest genres and themes that are best suited to specific times of day or situations. It can also analyze the customer's social media activity to suggest genres and themes based on topics and topics that interest them. This allows for suggestions that are more tailored to the customer's preferences.

[0047] The genre selection unit can also use the customer's current location information to suggest trends and popular genres for each region. For example, it can suggest movie or drama genres that are popular in a particular region. It can also suggest genres and themes that match the season or event. For example, it can suggest horror movies around Halloween and family movies around Christmas. It can also suggest related genres and themes based on places the customer has visited or events they have participated in. This makes it possible to make suggestions that are tailored to the customer's situation and environment.

[0048] The story generation unit can also reflect specific cultural and historical backgrounds specified by the customer. For example, it can generate stories based on the culture and history of a specific country or region. It can also generate stories that reflect specific eras or events specified by the customer. For example, it can generate stories set in medieval Europe or ancient Egypt. It can also generate stories that reflect specific social issues or themes specified by the customer. For example, it can generate stories themed around environmental issues or human rights issues. This makes it possible to generate stories that are tailored to the customer's interests and concerns.

[0049] The story generation unit can also generate stories based on specific literary works or movies specified by the customer. For example, it can generate stories based on the settings and characters of a customer's favorite novels or movies. It can also generate stories that reflect the style of a specific author or director specified by the customer. For example, it can reflect the writing style of a specific author or the visual style of a specific director. It can also generate stories based on famous works related to a specific genre or theme specified by the customer. For example, it can generate stories based on classic horror movies. This makes it possible to generate stories that suit the customer's preferences.

[0050] The character generation unit can further reflect the voice of a specific voice actor or actor designated by the customer. For example, it can generate a character based on the voice of a customer's favorite voice actor or actor. It can also generate a character that reflects a specific voice tone or accent designated by the customer. For example, it can reflect an accent from a specific region or a voice tone that expresses a specific emotion. Furthermore, it is possible for the customer to set the characteristics of a specific voice in detail, and the generation AI can generate a character based on that information. This makes it possible to generate a character that reflects the specific voice designated by the customer.

[0051] The character generation unit can also reflect specific occupations and roles specified by the customer. For example, the generation AI generates a character based on the specific occupation or role specified by the customer. For example, it can reflect occupations such as doctor, detective, or adventurer. It can also generate characters that reflect occupations and roles related to the genre or theme selected by the customer. For example, it can reflect the roles of wizards and knights that appear in fantasy works. Furthermore, it is possible for customers to specify the occupations and roles in detail, and the generation AI can generate characters based on that information. This makes it possible to generate characters that reflect the specific occupations and roles specified by the customer.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: Genre selection allows customers to select a genre or theme. For example, they can choose genres such as action, drama, comedy, horror, fantasy, or a specific theme (e.g., friendship, adventure, love, revenge, etc.). Step 2: In the story generation section, the AI ​​generates a story based on the genre and theme selected by the genre selection section. For example, the AI ​​creates a detailed story including characters, plot, setting, etc. based on the genre and theme given as a prompt. Step 3: In the character generation section, the AI ​​generates characters based on the story generated by the story generation section. For example, the AI ​​creates characters that fit the story by setting details such as the appearance, personality, and background of the characters that appear in the story. Step 4: In the video generation section, the AI ​​generates a video based on the characters generated by the character generation section. For example, the AI ​​generates a video by setting video scenes, camera angles, special effects, etc. based on the story and characters, creating a visually appealing video work. Step 5: The music generation unit uses the AI ​​to generate music based on the video generated by the video generation unit. For example, the AI ​​creates appropriate music to match the atmosphere and emotion of the scene and incorporates it into the video work. Step 6: The sharing unit shares the video work including the music generated by the music generation unit. For example, the created video work can be shared on an online posting platform. Other users can view the shared work and make comments and ratings.

[0054] (Example 2) The CineAI Studio system according to an embodiment of the present invention is a system in which generative AI automatically creates and shares stories, characters, images, and music based on a genre or theme selected by the customer. This allows customers to easily create and share original video works.

[0055] The CineAI Studio system according to the embodiment includes a genre selection unit, a story generation unit, a character generation unit, a video generation unit, a music generation unit, and a sharing unit. The genre selection unit allows a customer to select a genre or theme. For example, a customer can select a genre such as action, drama, comedy, horror, or fantasy, or a specific theme (e.g., friendship, adventure, love, or revenge). The story generation unit allows a generation AI to generate a story based on the genre or theme selected by the genre selection unit. For example, the generation AI creates a detailed story including characters, a plot, and a setting based on a genre or theme provided as a prompt. The character generation unit allows the generation AI to generate characters based on the story generated by the story generation unit. For example, the generation AI creates characters appropriate for the story by setting the appearance, personality, background, etc. of the characters appearing in the story in detail. The video generation unit allows the generation AI to generate video based on the characters generated by the character generation unit. For example, the generation AI sets video scenes, camera angles, special effects, etc. based on the story and characters, creating a visually appealing video work. The music generation unit allows the generation AI to generate music based on the video generated by the video generation unit. For example, the generative AI creates appropriate music to match the atmosphere and emotion of a scene and incorporates it into the video work. The sharing unit shares the video work that includes the music generated by the music generation unit. For example, the created video work can be shared on an online posting platform. Other users can view the shared work and comment and rate it. This allows the CineAI Studio system to allow customers to easily create and share original video works. For example, even people with no experience in filmmaking can create professional-looking video works with the help of AI. In addition, by receiving feedback from other users, they can identify areas for improvement in their work and use them in their next production.

[0056] The genre selection unit can analyze a customer's past selection history and suggest genres and themes based on the customer's preferences. For example, the genre selection unit stores the history of genres and themes selected by the customer in a database and uses that data to suggest the most suitable genre or theme for the next selection. For example, a customer who has frequently selected horror movies in the past can be suggested a new horror movie. The genre selection unit also analyzes the customer's selection history and refers to data on other customers with similar selection patterns to suggest the most suitable genre or theme. For example, it can suggest themes selected by other customers who like the same genre. The genre selection unit can also use generation AI to automatically discover new genres and themes based on the customer's selection history and suggest them to the customer. For example, it can suggest new genres that are highly related to genres selected in the past. This makes it possible to suggest genres and themes that suit individual preferences based on the customer's past selection history.

[0057] The genre selection unit can automatically provide the latest trends and news related to the genre or theme selected by the customer using the generation AI. For example, the genre selection unit automatically collects the latest movie and drama trend information related to the genre or theme selected by the customer and provides it to the customer. For example, it displays information on the latest hit movies and dramas. The genre selection unit also automatically collects news articles and blog posts related to the genre or theme selected by the customer to help with selection. For example, it displays movie critic reviews and industry news. The genre selection unit also collects trending information on social media related to the genre or theme selected by the customer to help with selection. For example, it displays information on movies and dramas that are trending on Twitter and Instagram. This allows the latest trends and news related to the genre or theme selected by the customer to be provided.

[0058] The genre selection unit can use the emotion estimation function to suggest genres and themes that are optimal for the customer's current emotional state. For example, when a customer uses the service, the genre selection unit analyzes facial expressions and voice to estimate the customer's current emotional state and suggests genres and themes that match that emotion. For example, if the customer wants to relax, it suggests comedy. The genre selection unit also estimates the customer's current emotional state based on the customer's input and selection history and suggests genres and themes that are optimal for that emotion. For example, if the customer is feeling stressed, it suggests a soothing theme. The genre selection unit also uses the emotion estimation function to monitor the customer's emotional response to the genre or theme selected by the customer in real time and make optimal suggestions. For example, it updates the suggestions if the customer's emotions change during selection. This makes it possible to suggest optimal genres and themes based on the customer's current emotional state.

[0059] The story generation unit can generate a story so that it always includes specific scenes and events specified by the customer. For example, the story generation unit uses the generation AI to generate a story so that it always includes specific scenes and events specified by the customer. For example, it always includes a climax scene or a scene featuring a specific character. The story generation unit also uses the generation AI to build a story plot based on the scenes and events specified by the customer. For example, it develops the story around the specified scenes. The story generation unit also incorporates the scenes and events specified by the customer as important elements of the story, and the generation AI generates a story based on those elements. For example, it sets the specified event to be a turning point in the story. This makes it possible to generate a story that includes the specific scenes and events specified by the customer.

[0060] The story generation unit can present multiple plot options during the story generation process and allow the customer to select from them. For example, the story generation unit may present multiple plot options during the story generation process using a generation AI and allow the customer to select from them. For example, different endings or character fates may be presented as options. The story generation unit may also have the generation AI generate a story based on the plot option selected by the customer. For example, the development of the story may change depending on the selected option. The story generation unit may also have the generation AI present plot options in real time during the story generation process and allow the customer to select from them on the spot. For example, the generation AI may present options as the story progresses and continue the story based on the option selected by the customer. This allows the generation AI to present multiple plot options during the story generation process and allow the customer to select from them.

[0061] The story generation unit can use the emotion estimation function to automatically select a story development that customers will most emotionally empathize with. For example, the story generation unit uses the emotion estimation function to automatically select a story development that customers will most emotionally empathize with. For example, it may prioritize selecting developments with high emotion scores. The story generation unit also monitors customers' emotional reactions in real time and selects the optimal story development based on the results. For example, it may prioritize selecting scenes that heighten emotions. The story generation unit also uses the generation AI to automatically select a story development that customers will most emotionally empathize with based on the emotion estimation data. For example, it may select a plot option with a high emotion score. This makes it possible to automatically select a story development that customers will most emotionally empathize with.

[0062] The character generation unit can reflect specific appearances and personality traits specified by the customer. For example, the character generation unit uses generation AI to generate characters based on the specific appearances and personality traits specified by the customer. For example, it can reflect hair color, eye shape, and personality traits. The character generation unit also generates characters that reflect appearances and personality traits related to the genre or theme selected by the customer. For example, it can reflect the appearance of elves or dragons that appear in fantasy works. The character generation unit also sets the detailed appearance and personality traits specified by the customer, and the generation AI generates characters based on that information. For example, it can reflect specific clothing, accessories, and personality traits. This makes it possible to generate characters that reflect the specific appearances and personality traits specified by the customer.

[0063] The character generation unit sets a character's background story in detail, and is able to clarify the character's actions and motivations. For example, the generation AI sets a character's background story in detail, and based on that information, clarifies the character's actions and motivations. For example, it sets the character's past events and family structure. The character generation unit also sets a background story related to the genre or theme selected by the customer, and based on that information, clarifies the character's actions and motivations. For example, it sets an adventurer's past adventures and failures. The character generation unit also sets a character's background story in detail, and based on that information, the generation AI generates the character's actions and motivations. For example, it sets a character with specific goals or dreams. This allows the character's background story to be set in detail, and actions and motivations to be clarified.

[0064] The character generation unit can use the emotion estimation function to automatically select a character that the customer most emotionally empathizes with. For example, the character generation unit uses the emotion estimation function to automatically select a character that the customer most emotionally empathizes with. For example, it prioritizes selecting a character with a high emotion score. The character generation unit also monitors the customer's emotional reactions in real time and selects the optimal character based on the results. For example, it prioritizes selecting a character that elicits strong emotions. The character generation unit also uses the emotion estimation data to automatically select a character that the customer most emotionally empathizes with. For example, it selects a character with a high emotion score. This makes it possible to automatically select a character that the customer most emotionally empathizes with.

[0065] The video generation unit can reflect specific scenes and camera angles specified by the customer. For example, the video generation unit uses generation AI to generate video based on the specific scenes and camera angles specified by the customer. For example, it always includes a climax scene or a scene featuring a specific character. The video generation unit also generates video that reflects scenes and camera angles related to the genre or theme selected by the customer. For example, it sets dynamic camera angles for action scenes. The video generation unit also sets the scenes and camera angles specified by the customer in detail, and the generation AI generates video based on that information. For example, it can reflect specific viewpoints and camera work. This makes it possible to generate video that reflects the specific scenes and camera angles specified by the customer.

[0066] The video generation unit can present multiple video options during the video generation process, allowing the customer to select from them. For example, the video generation unit uses a generation AI to present multiple video options during the video generation process, allowing the customer to select from them. For example, different camera angles or scene variations are presented. The video generation unit also uses the generation AI to generate a video based on the video option selected by the customer. For example, the development of the video changes depending on the selected option. The video generation unit also uses the generation AI to present video options in real time during the video generation process, allowing the customer to select from them on the spot. For example, options are presented as the video is in progress, and the video continues based on the option selected by the customer. This allows multiple video options to be presented during the video generation process, allowing the customer to select from them.

[0067] The video generation unit can use the emotion estimation function to automatically select video scenes that customers will most emotionally empathize with. For example, the video generation unit uses the emotion estimation function to automatically select video scenes that customers will most emotionally empathize with. For example, it prioritizes selecting scenes with high emotion scores. The video generation unit also monitors the customer's emotional reactions in real time and selects the optimal video scenes based on the results. For example, it prioritizes selecting scenes that heighten emotions. The video generation unit also uses the generation AI to automatically select video scenes that customers will most emotionally empathize with based on the emotion estimation data. For example, it selects scenes with high emotion scores. This makes it possible to automatically select video scenes that customers will most emotionally empathize with.

[0068] The music generation unit can reflect specific instruments and musical styles specified by the customer. For example, the music generation unit uses the generative AI to generate music based on the specific instruments and musical styles specified by the customer. For example, it can reflect piano, guitar, classical, or jazz styles. The music generation unit also generates music that reflects instruments and musical styles related to the genre or theme selected by the customer. For example, it can set eerie music for a horror work or grand music for a fantasy work. The music generation unit also sets the instruments and musical styles specified by the customer in detail, and the generative AI generates music based on that information. For example, it can reflect specific rhythms and melodies. This makes it possible to generate music that reflects the specific instruments and musical styles specified by the customer.

[0069] The music generation unit can present multiple music options during the music generation process, allowing the customer to select from them. For example, the music generation unit may present multiple music options during the music generation process using a generation AI, allowing the customer to select from them. For example, different instrument arrangements and tempo variations may be presented. The music generation unit may also have the generation AI generate music based on the music options selected by the customer. For example, the mood or style of the music may change depending on the selected option. The music generation unit may also have the generation AI present music options in real time during the music generation process, allowing the customer to select from them on the spot. For example, the generation AI may present options while the music is playing, and continue the music based on the option selected by the customer. This allows multiple music options to be presented during the music generation process, allowing the customer to select from them.

[0070] The music generation unit can use the emotion estimation function to automatically select music that the customer most emotionally empathizes with. For example, the music generation unit uses the emotion estimation function to automatically select music that the customer most emotionally empathizes with. For example, it prioritizes selecting music with a high emotion score. The music generation unit also monitors the customer's emotional reactions in real time and selects the optimal music based on the results. For example, it prioritizes selecting music that heightens emotions. The music generation unit also uses the generation AI to automatically select music that the customer most emotionally empathizes with based on the emotion estimation data. For example, it selects music with a high emotion score. This makes it possible to automatically select music that the customer most emotionally empathizes with.

[0071] The sharing unit can automatically analyze feedback on shared works and suggest improvements to customers. For example, the sharing unit can automatically analyze feedback on shared works and suggest specific improvements to customers. For example, it can analyze comments and ratings to extract improvements. The sharing unit also allows the generation AI to automatically suggest improvements based on feedback received by customers. For example, it can analyze the content of the feedback and present specific suggestions for correction. The sharing unit also builds a system in which the generation AI suggests improvements to customers based on the content of the feedback. For example, it can analyze trends in feedback to extract common improvements. This allows the sharing unit to automatically analyze feedback on shared works and suggest improvements to customers.

[0072] The sharing unit can provide a function that allows the generating AI to automatically improve the work based on the content of the feedback. The sharing unit, for example, provides a function that allows the generating AI to automatically improve the work based on the content of the feedback. For example, it analyzes comments and ratings and makes corrections automatically. The sharing unit also builds a system that allows the generating AI to automatically improve the work based on feedback received from customers. For example, it analyzes the content of the feedback and presents specific suggestions for correction. The sharing unit also provides a function that allows the generating AI to automatically improve the work based on the content of the feedback. For example, it analyzes trends in the feedback to extract common areas for improvement and makes corrections automatically. This makes it possible to provide a function that allows the generating AI to automatically improve the work based on the content of the feedback.

[0073] The sharing unit can use the emotion estimation function to analyze the customer's emotional response to feedback and prioritize displaying positive feedback. The sharing unit, for example, uses the emotion estimation function to analyze the customer's emotional response to feedback and prioritize displaying positive feedback. For example, feedback with a high emotion score is displayed prominently. The sharing unit also monitors the customer's emotional response in real time and, based on the results, prioritizes displaying positive feedback. For example, feedback that increases emotion is displayed preferentially. The sharing unit also builds a system that analyzes the customer's emotional response to feedback based on the emotion estimation data and prioritizes displaying positive feedback. For example, feedback with a high emotion score is displayed prominently. This makes it possible to analyze the customer's emotional response to feedback and prioritize displaying positive feedback.

[0074] The sharing unit automatically translates feedback on shared works into different languages, allowing users to obtain feedback from an international perspective. For example, the sharing unit automatically translates feedback on shared works into different languages ​​and collects feedback from an international perspective. For example, the sharing unit translates into multiple languages, such as English, French, and Chinese. The sharing unit also builds a system that posts the translated feedback on a multilingual platform and obtains feedback from users around the world. The sharing unit also collects advice and suggestions for improvement from an international perspective based on the feedback translated into different languages, thereby improving the quality of the works. For example, the sharing unit reflects feedback that takes cultural and market differences into consideration. This allows the sharing unit to automatically translate feedback on shared works into different languages ​​and obtain feedback from an international perspective.

[0075] The sharing unit can recommend similar works created by other users based on the content of the feedback. For example, the sharing unit automatically recommends similar works created by other users based on the content of the feedback. For example, works in the same genre or theme are displayed. The sharing unit also builds a system that recommends similar works created by other users based on feedback received by customers. For example, the sharing unit analyzes the content of the feedback and displays related works. The sharing unit also recommends similar works created by other users based on the content of the feedback. For example, the sharing unit analyzes feedback trends and displays works with a common theme or genre. This makes it possible to recommend similar works created by other users based on the content of the feedback.

[0076] The sharing unit can use the emotion estimation function to monitor the customer's emotional response to feedback in real time and provide optimal feedback. The sharing unit, for example, uses the emotion estimation function to monitor the customer's emotional response to feedback in real time and provide optimal feedback based on the results. For example, it prioritizes displaying feedback that increases emotion. The sharing unit also analyzes the customer's emotional response in real time and adjusts the content of the feedback based on the data. For example, it corrects elements that decrease emotion. The sharing unit also analyzes the customer's emotional response to feedback based on the emotion estimation data and builds a system that provides optimal feedback. For example, it prioritizes displaying feedback with a high emotion score. This makes it possible to monitor the customer's emotional response to feedback in real time and provide optimal feedback.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The CineAI Studio system can also add interactive elements. For example, it can provide an interactive feature that allows customers to change the storyline by making choices as the story progresses. It can also add a feature that allows customers to change character actions and lines in real time. It can also provide a feature that allows customers to record their own voices and dub characters. This allows customers to become more deeply involved in the story and create original video works that are tailored to their individual tastes.

[0079] The genre selection unit can further analyze the customer's past viewing history and rating data to suggest more accurate genres and themes. For example, new suggestions can be made based on the genres and themes of works that the customer has previously rated highly. It can also analyze the viewing time and frequency of works that the customer has viewed to suggest genres and themes that are best suited to specific times of day or situations. It can also analyze the customer's social media activity to suggest genres and themes based on topics and topics that interest them. This allows for suggestions that are more tailored to the customer's preferences.

[0080] The genre selection unit can also use the customer's current location information to suggest trends and popular genres for each region. For example, it can suggest movie or drama genres that are popular in a particular region. It can also suggest genres and themes that match the season or event. For example, it can suggest horror movies around Halloween and family movies around Christmas. It can also suggest related genres and themes based on places the customer has visited or events they have participated in. This makes it possible to make suggestions that are tailored to the customer's situation and environment.

[0081] The genre selection unit can use the emotion estimation function to suggest genres and themes that promote relaxation or excitement based on the customer's emotional state. For example, if a customer wants to relax, comedy or romance can be suggested, and conversely, if a customer is looking for excitement or thrills, action or horror can be suggested. It can also suggest genres and themes that stabilize emotions depending on the customer's emotional state. For example, if the customer is feeling stressed, a soothing theme can be suggested. Furthermore, it is possible to monitor the customer's emotional state in real time and update the suggestions according to changes in emotion. This makes it possible to suggest genres and themes that are optimal for the customer's emotional state.

[0082] The story generation unit can also reflect specific cultural and historical backgrounds specified by the customer. For example, it can generate stories based on the culture and history of a specific country or region. It can also generate stories that reflect specific eras or events specified by the customer. For example, it can generate stories set in medieval Europe or ancient Egypt. It can also generate stories that reflect specific social issues or themes specified by the customer. For example, it can generate stories themed around environmental issues or human rights issues. This makes it possible to generate stories that are tailored to the customer's interests and concerns.

[0083] The story generation unit can also generate stories based on specific literary works or movies specified by the customer. For example, it can generate stories based on the settings and characters of a customer's favorite novels or movies. It can also generate stories that reflect the style of a specific author or director specified by the customer. For example, it can reflect the writing style of a specific author or the visual style of a specific director. It can also generate stories based on famous works related to a specific genre or theme specified by the customer. For example, it can generate stories based on classic horror movies. This makes it possible to generate stories that suit the customer's preferences.

[0084] The story generation unit can use the emotion estimation function to select a story development that emphasizes the relationships between characters that customers most emotionally empathize with. For example, the story can be developed around the relationships between characters with high emotion scores. It can also monitor customers' emotional reactions in real time and adjust character relationships based on the results. For example, it can prioritize scenes that heighten emotions. Furthermore, it is conceivable that the generation AI can automatically select the relationships between characters that customers most emotionally empathize with based on emotion estimation data. This makes it possible to select a story development that emphasizes the relationships between characters that customers most emotionally empathize with.

[0085] The character generation unit can further reflect the voice of a specific voice actor or actor designated by the customer. For example, it can generate a character based on the voice of a customer's favorite voice actor or actor. It can also generate a character that reflects a specific voice tone or accent designated by the customer. For example, it can reflect an accent from a specific region or a voice tone that expresses a specific emotion. Furthermore, it is possible for the customer to set the characteristics of a specific voice in detail, and the generation AI can generate a character based on that information. This makes it possible to generate a character that reflects the specific voice designated by the customer.

[0086] The character generation unit can also reflect specific occupations and roles specified by the customer. For example, the generation AI generates a character based on the specific occupation or role specified by the customer. For example, it can reflect occupations such as doctor, detective, or adventurer. It can also generate characters that reflect occupations and roles related to the genre or theme selected by the customer. For example, it can reflect the roles of wizards and knights that appear in fantasy works. Furthermore, it is possible for customers to specify the occupations and roles in detail, and the generation AI can generate characters based on that information. This makes it possible to generate characters that reflect the specific occupations and roles specified by the customer.

[0087] The character generation unit can use the emotion estimation function to emphasize the character growth process that customers most emotionally empathize with. For example, the story can be developed around the growth process of a character with a high emotion score. It can also monitor customers' emotional reactions in real time and adjust the character growth process based on the results. For example, it can prioritize scenes that heighten emotions. Furthermore, it is possible for the generation AI to automatically select the character growth process that customers most emotionally empathize with based on emotion estimation data. This makes it possible to select a story development that emphasizes the character growth process that customers most emotionally empathize with.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: Genre selection allows customers to select a genre or theme. For example, they can choose genres such as action, drama, comedy, horror, fantasy, or a specific theme (e.g., friendship, adventure, love, revenge, etc.). Step 2: In the story generation section, the AI ​​generates a story based on the genre and theme selected by the genre selection section. For example, the AI ​​creates a detailed story including characters, plot, setting, etc. based on the genre and theme given as a prompt. Step 3: In the character generation section, the AI ​​generates characters based on the story generated by the story generation section. For example, the AI ​​creates characters that fit the story by setting details such as the appearance, personality, and background of the characters that appear in the story. Step 4: In the video generation section, the AI ​​generates a video based on the characters generated by the character generation section. For example, the AI ​​generates a video by setting video scenes, camera angles, special effects, etc. based on the story and characters, creating a visually appealing video work. Step 5: The music generation unit uses the AI ​​to generate music based on the video generated by the video generation unit. For example, the AI ​​creates appropriate music to match the atmosphere and emotion of the scene and incorporates it into the video work. Step 6: The sharing unit shares the video work including the music generated by the music generation unit. For example, the created video work can be shared on an online posting platform. Other users can view the shared work and make comments and ratings.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] 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.

[0149] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A genre selection section for selecting genres and themes; a story generation unit that generates a story based on the genre and theme selected by the genre selection unit; a character generation unit that generates a character based on the story generated by the story generation unit; an image generation unit that generates an image based on the character generated by the character generation unit; a music generation unit that generates music based on the video generated by the video generation unit; a sharing unit for sharing a video work including the music generated by the music generating unit; A system characterized by:

2. The genre selection unit Analyzing the customer's past selection history and suggesting the genre or theme based on the customer's preferences 2. The system of claim 1.

3. The genre selection unit The AI ​​automatically provides the latest trends and news related to the genre or theme selected by the customer.

2. The system of claim 1.

4. The genre selection unit Suggesting the genre or theme that best suits the customer's current emotional state 2. The system of claim 1.

5. The story generation unit Generate stories to ensure they include specific scenes and events specified by the customer 2. The system of claim 1.

6. The story generation unit Present multiple plot options during the story generation process and allow the customer to choose 2. The system of claim 1.

7. The story generation unit Automatically select the storyline that most emotionally resonates with customers 2. The system of claim 1.

Images