System

Abstract

An object of the system according to the embodiment is to generate an animation by inputting a name of a local area and to spread local sightseeing spots.SOLUTION: A system includes a region name inputting part, a scenario generating part, a video generating part, a sound generating part, and a generation AI part. The region name input unit inputs a region name. The scenario generation unit generates an animation scenario on the basis of the region name input by the region name input unit. The image generation unit generates an animation image on the basis of the scenario generated by the scenario generation unit. The sound generation unit generates a sound of the animation based on the scenario generated by the scenario generation unit. The generation AI unit controls the scenario generation unit, the video generation unit, and the sound 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] Conventional technologies have not provided sufficient effective means to solve the problem of rural depopulation, and there is room for improvement.

[0005] The system according to the embodiment aims to promote local tourist attractions by generating animations by inputting the name of a local area. [Means for solving the problem]

[0006] The system according to the embodiment includes a region name input unit, a scenario generation unit, a video generation unit, an audio generation unit, and a generation AI unit. The region name input unit inputs a region name. The scenario generation unit generates an anime scenario based on the region name input by the region name input unit. The video generation unit generates anime video based on the scenario generated by the scenario generation unit. The audio generation unit generates audio for the anime based on the scenario generated by the scenario generation unit. The generation AI unit controls the scenario generation unit, the video generation unit, and the audio generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate animation by inputting the name of a region, thereby promoting tourist attractions in the region. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 animation generation system according to an embodiment of the present invention is a system in which, by inputting the name of a region, the generation AI generates a scenario, images, and sounds, and promotes the region's tourist attractions through the animation. This allows the animation generation system to solve the problem of depopulation in rural areas and increase the number of tourists.

[0029] An animation generation system according to an embodiment includes a locality name input unit, a scenario generation unit, a video generation unit, an audio generation unit, and a generation AI unit. The locality name input unit inputs the name of a locality. For example, a user inputs "XX City." The scenario generation unit generates an animation scenario based on the locality name input by the locality name input unit. For example, the scenario generation unit creates a story incorporating the history, culture, and scenery of the locality. The video generation unit generates animation video based on the scenario generated by the scenario generation unit. For example, the video generation unit creates video that realistically reproduces the scenery, buildings, and natural environment of the locality. The audio generation unit generates audio for the animation based on the scenario generated by the scenario generation unit. For example, the audio generation unit creates character voices, background music, sound effects, and the like. The generation AI unit controls the scenario generation unit, the video generation unit, and the audio generation unit. For example, the generation AI unit oversees the scenario, video, and audio generation processes and manages overall quality. As a result, the animation generation system according to an embodiment can generate an animation set in a locality and promote tourist attractions simply by inputting the name of the locality.

[0030] The region name input unit allows the user to input the region name along with the desired theme and genre, and the generation AI unit can customize the anime based on the theme and genre. For example, when the user inputs the name of a region, the region name input unit also inputs the desired theme and genre. For example, if "XX City" and "Fantasy" are input, the generation AI unit generates a fantasy anime set in that region. The user is also given the option to select the desired genre along with the region name, and the generation AI unit customizes the scenario and visuals based on that selection. For example, if "History" is selected, a scenario incorporating historical events is generated. Furthermore, when inputting the name of a region, multiple themes and genres can be selected, and the generation AI unit customizes the anime based on those selections. For example, if "Romance" and "Comedy" are selected, a romantic comedy scenario is generated. This allows for the generation of anime tailored to the user's desired theme and genre, thereby meeting a wider variety of needs.

[0031] The locality name input unit allows the user to input personal anecdotes and memories related to a locality when inputting the name of the locality, and the generation AI unit can generate a scenario based on the personal anecdotes and memories. For example, when a user inputs the name of a locality, the locality name input unit inputs personal anecdotes and memories related to the locality. For example, if the user inputs "XX City" and "a park where I played as a child," the generation AI unit reflects the anecdote in the scenario. The system also provides the user with the option to input memories related to the locality along with the name of the locality, and the generation AI unit customizes the scenario based on the input. For example, if the user inputs "a shrine visited on a family trip," the shrine will appear in the scenario. The system also allows the user to input detailed personal anecdotes and memories when inputting the name of a locality, and the generation AI unit generates a scenario based on the input. For example, if the user inputs "a cafe where I spent time with friends from high school," the cafe will become an important setting in the scenario. This allows for the provision of more personalized content by generating scenarios that reflect the user's personal anecdotes and memories.

[0032] The locality name input unit allows users to upload photos and videos of the locality in addition to entering the locality's name, and the generation AI unit can generate more realistic images based on the photos and videos. For example, when a user enters the name of a locality, the locality name input unit uploads photos and videos of that locality. For example, if a user uploads "X City" and a scenic photo of that city, the generation AI unit generates a realistic image based on that photo. The generation AI unit also analyzes the video uploaded by the user along with the locality's name, and customizes the image based on the content. For example, if a user uploads a video of a tourist attraction, that landmark will appear in the image. Furthermore, when entering the name of a locality, users can upload multiple photos and videos, and the generation AI unit generates an image based on those. For example, if seasonal scenic photos are uploaded, images of each season will be generated. This allows for more realistic animation by generating realistic images based on local photos and videos.

[0033] The region name input unit allows a user to select multiple regions when inputting a region name, and the generation AI unit can generate a crossover animation based on the multiple regions. The region name input unit, for example, allows a user to select multiple regions when inputting a region name. For example, if "XX City" and "△△ Town" are selected, the generation AI unit generates a crossover animation set in those regions. In addition, along with the region name, the user is given the option to select multiple regions, and the generation AI unit customizes the scenario and images based on that selection. For example, if "□□ Village" and "◇◇ City" are selected, a scenario in which those regions intersect is generated. Furthermore, when inputting a region name, multiple regions can be selected in detail, and the generation AI unit generates a crossover animation based on that selection. For example, if "▲▲ Town" and "■■ City" are selected, an image in which those regions coexist is generated. This allows for the generation of crossover animations set in multiple regions, introducing a wider variety of tourist attractions.

[0034] When generating video, the generation AI unit incorporates seasonal local scenery and events, allowing it to express the charm of each season. For example, when generating video, the generation AI unit incorporates seasonal local scenery. For example, it can reflect cherry blossoms in spring, the sea in summer, autumn leaves, and snowy winter scenery in the video. It also collects information on seasonal events along with the name of the region, and customizes the video based on that information. For example, it can incorporate summer festivals and fireworks displays into the video. When generating video, the generation AI unit also collects seasonal scenery and events in real time and creates video based on that data. For example, it can reflect tourist attractions for each season in the video. This allows it to express the charm of each season by incorporating seasonal local scenery and events.

[0035] The generation AI unit can incorporate local natural sounds and traditional music when generating audio to enhance the sense of realism. For example, the generation AI unit incorporates local natural sounds when generating audio. For example, it can reflect the sound of a babbling brook, birds chirping, and the sound of the wind in the audio. In addition, traditional music information is collected along with the name of the region, and the generation AI unit customizes the audio based on that information. For example, it can incorporate music played at local festivals and the sounds of traditional instruments. In addition, when generating audio, the generation AI unit collects natural sounds and traditional music in real time and creates the audio based on that data. For example, it can reflect music that matches the scenery of the region in the audio. In this way, incorporating local natural sounds and traditional music can enhance the sense of realism.

[0036] The generation AI unit incorporates future prediction data for local areas when generating scenarios, allowing it to create science fiction anime set in local areas of the future. For example, the generation AI unit incorporates future prediction data for local areas when generating scenarios. For example, it depicts future local areas based on demographic and economic forecasts. It also collects future prediction data along with the names of local areas, and the generation AI unit creates science fiction anime scenarios based on that data. For example, it generates scenarios that incorporate future technology and infrastructure. In addition, when generating scenarios, the generation AI unit collects future prediction data in real time, and creates science fiction anime set in local areas of the future based on that data. For example, it reflects future tourist attractions and events in the scenario. In this way, by incorporating future prediction data for local areas, it is possible to create science fiction anime set in local areas of the future.

[0037] When generating video, the generation AI unit can recreate historical landscapes based on past photographs and footage of the region. For example, when generating video, the generation AI unit incorporates past photographs and footage of the region. For example, old townscapes and buildings can be reflected in the video. In addition, past photographs and footage can be collected along with the names of the region, and the generation AI unit recreates historical landscapes based on that data. For example, pre-war landscapes and Showa-era townscapes can be incorporated into the video. In addition, when generating video, the generation AI unit collects past photographs and footage in real time, and recreates historical landscapes based on that data. For example, past tourist attractions and events can be reflected in the video. This allows historical charm to be expressed by recreating historical landscapes based on past photographs and footage of the region.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The anime generation system can further include a preference analysis unit that analyzes the user's preferences and viewing history. For example, it can analyze the genres and themes of anime that the user has watched in the past and customize the scenario and images based on that data. If the user watches a lot of action anime, the generation AI unit can generate a scenario that includes many action scenes. It can also analyze the user's preferences in real time and adjust the content of the anime based on the results. For example, it can reflect the trends in anime that the user has recently watched. It can also provide an option for the user to input their preferences in detail, and the generation AI unit can customize the anime based on that input. This allows the system to generate anime that matches the user's preferences, thereby providing more personalized content.

[0040] The animation generation system can further include a health monitoring unit that monitors the user's health condition. For example, when the user inputs the name of a region, the health monitoring unit measures the user's heart rate and stress level and customizes scenarios and images based on that data. If the user's heart rate is high, a relaxing scenario is generated. The health monitoring unit can also analyze the user's health condition in real time and adjust the content of the animation based on the results. For example, if the user's stress level is high, a relaxing image is generated. The system also provides an option for the user to input details about their health condition, and the generation AI unit customizes the animation based on that input. This allows the system to generate animation tailored to the user's health condition, thereby providing more health-conscious content.

[0041] The animation generation system can also include a specialty product introduction section that introduces local specialties and famous products. For example, when a user enters the name of a region, the system automatically searches for the region's specialty products and famous products and incorporates them into the scenario and video. If a user enters "X city," the city's specialty fruits and crafts will appear in the scenario. The specialty product introduction section can also collect local specialties and famous products in real time, and the generation AI section can customize the scenario and video based on that data. For example, seasonal specialties can be reflected in the scenario. The system also provides an option for users to enter detailed information about local specialties and famous products, and the generation AI section customizes the animation based on that input. This allows the introduction of local specialties and famous products to attract tourists and further spread the appeal of the region.

[0042] The animation generation system can also include a cultural introduction section that introduces local traditional crafts and culture. For example, when a user enters the name of a region, the system automatically searches for the region's traditional crafts and culture and incorporates them into the scenario and video. If a user enters "X city," the city's traditional crafts, such as pottery and textiles, will appear in the scenario. The cultural introduction section can also collect local traditional crafts and culture in real time, and the generation AI section can customize the scenario and video based on that data. For example, traditional seasonal events can be reflected in the scenario. The system also provides an option for users to enter detailed information about traditional crafts and culture, and the generation AI section customizes the animation based on that input. This allows the introduction of local traditional crafts and culture to attract tourists and further spread the appeal of the region.

[0043] The animation generation system can further include a future prediction unit that incorporates future prediction data for local areas. For example, when a user inputs the name of a local area, the future prediction unit will depict the future of that local area based on the local demographic and economic forecasts. If a user inputs "X city," the future appearance of that city will be reflected in the scenario. The future prediction unit can also collect future prediction data for local areas in real time, and the generation AI unit can customize the scenario and images based on that data. For example, it can generate scenarios that incorporate future technology and infrastructure. It also provides an option for users to input detailed future prediction data, and the generation AI unit will customize the animation based on that input. In this way, by incorporating future prediction data for local areas, it is possible to create science fiction anime set in local areas of the future.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The user inputs the name of a region in the region name input section. For example, the user inputs "XX City." Step 2: The scenario generation unit generates an anime scenario based on the name of the region input by the region name input unit. For example, the scenario generation unit creates a story that incorporates the history, culture, and scenery of the region. Step 3: The image generation unit generates animated images based on the scenario generated by the scenario generation unit. For example, the image generation unit creates images that realistically reproduce local scenery, buildings, and natural environments. Step 4: The sound generation unit generates sounds for the animation based on the scenario generated by the scenario generation unit. For example, the sound generation unit creates character voices, background music, sound effects, etc. Step 5: The generation AI unit controls the scenario generation unit, video generation unit, and audio generation unit. For example, the generation AI unit oversees the scenario, video, and audio generation processes and manages the overall quality.

[0046] (Example 2) The animation generation system according to an embodiment of the present invention is a system in which, by inputting the name of a region, the generation AI generates a scenario, images, and sounds, and promotes the region's tourist attractions through the animation. This allows the animation generation system to solve the problem of depopulation in rural areas and increase the number of tourists.

[0047] An animation generation system according to an embodiment includes a locality name input unit, a scenario generation unit, a video generation unit, an audio generation unit, and a generation AI unit. The locality name input unit inputs the name of a locality. For example, a user inputs "XX City." The scenario generation unit generates an animation scenario based on the locality name input by the locality name input unit. For example, the scenario generation unit creates a story incorporating the history, culture, and scenery of the locality. The video generation unit generates animation video based on the scenario generated by the scenario generation unit. For example, the video generation unit creates video that realistically reproduces the scenery, buildings, and natural environment of the locality. The audio generation unit generates audio for the animation based on the scenario generated by the scenario generation unit. For example, the audio generation unit creates character voices, background music, sound effects, and the like. The generation AI unit controls the scenario generation unit, the video generation unit, and the audio generation unit. For example, the generation AI unit oversees the scenario, video, and audio generation processes and manages overall quality. As a result, the animation generation system according to an embodiment can generate an animation set in a locality and promote tourist attractions simply by inputting the name of the locality.

[0048] The region name input unit inputs the user's emotional state in addition to the name of the region, and the generation AI unit can generate scenarios and images based on the emotional state. For example, when a user inputs the name of a region, the region name input unit also inputs the user's emotional state. For example, if the user inputs "I want to relax," the generation AI unit generates relaxing scenarios and images that match that emotion. In addition, the user's emotional state is analyzed in real time along with the region name, and the generation AI unit customizes the scenario and images based on that emotion. For example, if the user is analyzed as "excited," a scenario with many action scenes is generated. In addition, when inputting the name of a region, an option to select the emotional state is provided, and the generation AI unit generates scenarios and images based on that selection. For example, if "sad" is selected, a moving scenario is generated. This allows the generation of animation that matches the user's emotions, providing more emotionally appealing content.

[0049] The region name input unit allows the user to input the region name along with the desired theme and genre, and the generation AI unit can customize the anime based on the theme and genre. For example, when the user inputs the name of a region, the region name input unit also inputs the desired theme and genre. For example, if "XX City" and "Fantasy" are input, the generation AI unit generates a fantasy anime set in that region. The user is also given the option to select the desired genre along with the region name, and the generation AI unit customizes the scenario and visuals based on that selection. For example, if "History" is selected, a scenario incorporating historical events is generated. Furthermore, when inputting the name of a region, multiple themes and genres can be selected, and the generation AI unit customizes the anime based on those selections. For example, if "Romance" and "Comedy" are selected, a romantic comedy scenario is generated. This allows for the generation of anime tailored to the user's desired theme and genre, thereby meeting a wider variety of needs.

[0050] The locality name input unit allows the user to input personal anecdotes and memories related to a locality when inputting the name of the locality, and the generation AI unit can generate a scenario based on the personal anecdotes and memories. For example, when a user inputs the name of a locality, the locality name input unit inputs personal anecdotes and memories related to the locality. For example, if the user inputs "XX City" and "a park where I played as a child," the generation AI unit reflects the anecdote in the scenario. The system also provides the user with the option to input memories related to the locality along with the name of the locality, and the generation AI unit customizes the scenario based on the input. For example, if the user inputs "a shrine visited on a family trip," the shrine will appear in the scenario. The system also allows the user to input detailed personal anecdotes and memories when inputting the name of a locality, and the generation AI unit generates a scenario based on the input. For example, if the user inputs "a cafe where I spent time with friends from high school," the cafe will become an important setting in the scenario. This allows for the provision of more personalized content by generating scenarios that reflect the user's personal anecdotes and memories.

[0051] The locality name input unit allows users to upload photos and videos of the locality in addition to entering the locality's name, and the generation AI unit can generate more realistic images based on the photos and videos. For example, when a user enters the name of a locality, the locality name input unit uploads photos and videos of that locality. For example, if a user uploads "X City" and a scenic photo of that city, the generation AI unit generates a realistic image based on that photo. The generation AI unit also analyzes the video uploaded by the user along with the locality's name, and customizes the image based on the content. For example, if a user uploads a video of a tourist attraction, that landmark will appear in the image. Furthermore, when entering the name of a locality, users can upload multiple photos and videos, and the generation AI unit generates an image based on those. For example, if seasonal scenic photos are uploaded, images of each season will be generated. This allows for more realistic animation by generating realistic images based on local photos and videos.

[0052] The region name input unit allows a user to select multiple regions when inputting a region name, and the generation AI unit can generate a crossover animation based on the multiple regions. The region name input unit, for example, allows a user to select multiple regions when inputting a region name. For example, if "XX City" and "△△ Town" are selected, the generation AI unit generates a crossover animation set in those regions. In addition, along with the region name, the user is given the option to select multiple regions, and the generation AI unit customizes the scenario and images based on that selection. For example, if "□□ Village" and "◇◇ City" are selected, a scenario in which those regions intersect is generated. Furthermore, when inputting a region name, multiple regions can be selected in detail, and the generation AI unit generates a crossover animation based on that selection. For example, if "▲▲ Town" and "■■ City" are selected, an image in which those regions coexist is generated. This allows for the generation of crossover animations set in multiple regions, introducing a wider variety of tourist attractions.

[0053] The locality name input unit uses an emotion estimation function to analyze the user's emotion in real time when entering the locality name, and the generation AI unit can adjust the tone and atmosphere of the animation based on the emotion. For example, when a user enters the name of a locality, the locality name input unit uses the emotion estimation function to analyze the user's emotion in real time. For example, if the user enters "X city" and the analysis results in "fun," the generation AI unit generates an animation with a bright tone. The unit also analyzes the user's emotional state in real time along with the locality name, and customizes the scenario and images based on that emotion. For example, if the user is analyzed as "sad," the generation AI unit generates an emotional scenario. The unit also analyzes the user's emotion in real time when entering the locality name using the emotion estimation function, and adjusts the tone and atmosphere of the animation based on the results. For example, if the analysis results in "excited," the generation AI unit generates an animation with many action scenes. This allows the user to adjust the tone and atmosphere of the animation based on their emotion, providing more emotionally appealing content.

[0054] The generation AI unit collects emotional data of local residents and tourists when generating a scenario, and can create emotionally rich scenarios based on the emotional data. For example, the generation AI unit collects emotional data of local residents and tourists when generating a scenario. For example, it analyzes interviews with residents and reviews by tourists and reflects those emotions in the scenario. It also collects emotional data of residents and tourists along with the name of the region, and creates emotionally rich scenarios based on that data. For example, it incorporates the joy and sadness of residents into the scenario. In addition, when generating a scenario, the generation AI unit collects emotional data in real time and creates emotionally rich scenarios based on that data. For example, it reflects the excitement and surprise of tourists in the scenario. In this way, by creating emotionally rich scenarios based on the emotional data of local residents and tourists, it is possible to provide content that appeals to more emotions.

[0055] When generating video, the generation AI unit incorporates seasonal local scenery and events, allowing it to express the charm of each season. For example, when generating video, the generation AI unit incorporates seasonal local scenery. For example, it can reflect cherry blossoms in spring, the sea in summer, autumn leaves, and snowy winter scenery in the video. It also collects information on seasonal events along with the name of the region, and customizes the video based on that information. For example, it can incorporate summer festivals and fireworks displays into the video. When generating video, the generation AI unit also collects seasonal scenery and events in real time and creates video based on that data. For example, it can reflect tourist attractions for each season in the video. This allows it to express the charm of each season by incorporating seasonal local scenery and events.

[0056] The generation AI unit can incorporate local natural sounds and traditional music when generating audio to enhance the sense of realism. For example, the generation AI unit incorporates local natural sounds when generating audio. For example, it can reflect the sound of a babbling brook, birds chirping, and the sound of the wind in the audio. In addition, traditional music information is collected along with the name of the region, and the generation AI unit customizes the audio based on that information. For example, it can incorporate music played at local festivals and the sounds of traditional instruments. In addition, when generating audio, the generation AI unit collects natural sounds and traditional music in real time and creates the audio based on that data. For example, it can reflect music that matches the scenery of the region in the audio. In this way, incorporating local natural sounds and traditional music can enhance the sense of realism.

[0057] The generation AI unit incorporates future prediction data for local areas when generating scenarios, allowing it to create science fiction anime set in local areas of the future. For example, the generation AI unit incorporates future prediction data for local areas when generating scenarios. For example, it depicts future local areas based on demographic and economic forecasts. It also collects future prediction data along with the names of local areas, and the generation AI unit creates science fiction anime scenarios based on that data. For example, it generates scenarios that incorporate future technology and infrastructure. In addition, when generating scenarios, the generation AI unit collects future prediction data in real time, and creates science fiction anime set in local areas of the future based on that data. For example, it reflects future tourist attractions and events in the scenario. In this way, by incorporating future prediction data for local areas, it is possible to create science fiction anime set in local areas of the future.

[0058] When generating video, the generation AI unit can recreate historical landscapes based on past photographs and footage of the region. For example, when generating video, the generation AI unit incorporates past photographs and footage of the region. For example, old townscapes and buildings can be reflected in the video. In addition, past photographs and footage can be collected along with the names of the region, and the generation AI unit recreates historical landscapes based on that data. For example, pre-war landscapes and Showa-era townscapes can be incorporated into the video. In addition, when generating video, the generation AI unit collects past photographs and footage in real time, and recreates historical landscapes based on that data. For example, past tourist attractions and events can be reflected in the video. This allows historical charm to be expressed by recreating historical landscapes based on past photographs and footage of the region.

[0059] The generation AI unit uses the emotion estimation function to analyze users' emotional reactions to the scenario, video, and audio of the generated animation, and can improve the animation based on the feedback. For example, the generation AI unit collects users' emotional reactions to the generated animation in real time and uses that data to improve the animation's scenario, video, and audio. For example, it emphasizes scenes that receive many positive reactions. It also uses the emotion estimation function to analyze users' emotional reactions and adjusts the animation's scenario, video, and audio based on the results. For example, it modifies scenes that receive many negative reactions. The generation AI unit also identifies areas for improvement in the animation based on the user's emotional reaction data and reflects these in the next generation. For example, it makes suggestions to improve parts with low emotional scores. This allows the animation to be improved based on the user's emotional reactions, providing more emotionally appealing content.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The anime generation system can further include a preference analysis unit that analyzes the user's preferences and viewing history. For example, it can analyze the genres and themes of anime that the user has watched in the past and customize the scenario and images based on that data. If the user watches a lot of action anime, the generation AI unit can generate a scenario that includes many action scenes. It can also analyze the user's preferences in real time and adjust the content of the anime based on the results. For example, it can reflect the trends in anime that the user has recently watched. It can also provide an option for the user to input their preferences in detail, and the generation AI unit can customize the anime based on that input. This allows the system to generate anime that matches the user's preferences, thereby providing more personalized content.

[0062] The animation generation system can further include an emotion estimation unit that estimates the user's emotional state. For example, when a user inputs the name of a region, the emotion estimation unit analyzes the user's facial expression and tone of voice to estimate their emotional state. The generation AI unit customizes the scenario and images based on the estimated emotions. For example, if the user feels like "I want to relax," it generates a relaxing scenario and images. The emotion estimation unit can also analyze the user's emotions in real time and adjust the content of the animation based on the results. For example, if the user is analyzed as being "excited," it generates a scenario with many action scenes. This allows the system to generate animation that matches the user's emotions, providing content that appeals to the user's emotions more.

[0063] The animation generation system can further include a health monitoring unit that monitors the user's health condition. For example, when the user inputs the name of a region, the health monitoring unit measures the user's heart rate and stress level and customizes scenarios and images based on that data. If the user's heart rate is high, a relaxing scenario is generated. The health monitoring unit can also analyze the user's health condition in real time and adjust the content of the animation based on the results. For example, if the user's stress level is high, a relaxing image is generated. The system also provides an option for the user to input details about their health condition, and the generation AI unit customizes the animation based on that input. This allows the system to generate animation tailored to the user's health condition, thereby providing more health-conscious content.

[0064] The animation generation system can further include an emotion estimation unit that estimates the user's emotional state. For example, when a user inputs the name of a region, the emotion estimation unit analyzes the user's facial expression and tone of voice to estimate their emotional state. Based on the estimated emotion, the generation AI unit customizes the scenario and images. For example, if the user feels "nostalgic," the system generates a scenario that reflects nostalgic memories. The emotion estimation unit can also analyze the user's emotions in real time and adjust the content of the animation based on the results. For example, if the user is analyzed as feeling "fun," the system generates a scenario with many fun scenes. This allows the system to generate animation that matches the user's emotions, providing content that appeals to the user's emotions more.

[0065] The animation generation system can also include a specialty product introduction section that introduces local specialties and famous products. For example, when a user enters the name of a region, the system automatically searches for the region's specialty products and famous products and incorporates them into the scenario and video. If a user enters "X city," the city's specialty fruits and crafts will appear in the scenario. The specialty product introduction section can also collect local specialties and famous products in real time, and the generation AI section can customize the scenario and video based on that data. For example, seasonal specialties can be reflected in the scenario. The system also provides an option for users to enter detailed information about local specialties and famous products, and the generation AI section customizes the animation based on that input. This allows the introduction of local specialties and famous products to attract tourists and further spread the appeal of the region.

[0066] The animation generation system can further include an emotion estimation unit that estimates the user's emotional state. For example, when a user selects multiple regions, the emotion estimation unit analyzes the user's facial expressions and tone of voice to estimate their emotional state. Based on the estimated emotions, the generation AI unit customizes the scenario and images. For example, if the user feels "excited," it generates an adventurous scenario. The emotion estimation unit can also analyze the user's emotions in real time and adjust the content of the animation based on the results. For example, if the analysis indicates that the user "wants to relax," it generates a relaxing scenario. This allows the system to generate animation that matches the user's emotions, thereby providing content that appeals to the user's emotions more.

[0067] The animation generation system can also include a cultural introduction section that introduces local traditional crafts and culture. For example, when a user enters the name of a region, the system automatically searches for the region's traditional crafts and culture and incorporates them into the scenario and video. If a user enters "X city," the city's traditional crafts, such as pottery and textiles, will appear in the scenario. The cultural introduction section can also collect local traditional crafts and culture in real time, and the generation AI section can customize the scenario and video based on that data. For example, traditional seasonal events can be reflected in the scenario. The system also provides an option for users to enter detailed information about traditional crafts and culture, and the generation AI section customizes the animation based on that input. This allows the introduction of local traditional crafts and culture to attract tourists and further spread the appeal of the region.

[0068] The animation generation system can further include an emotion estimation unit that estimates the user's emotional state. For example, when a user inputs the name of a region, the emotion estimation unit analyzes the user's facial expression and tone of voice to estimate their emotional state. Based on the estimated emotion, the generation AI unit customizes the scenario and images. For example, if the user feels "moved," it generates an emotional scenario. The emotion estimation unit can also analyze the user's emotions in real time and adjust the content of the animation based on the results. For example, if the user is analyzed as "having fun," it generates a scenario with many happy scenes. This allows the system to generate animation that matches the user's emotions, providing content that appeals to the user's emotions more.

[0069] The animation generation system can further include a future prediction unit that incorporates future prediction data for local areas. For example, when a user inputs the name of a local area, the future prediction unit will depict the future of that local area based on the local demographic and economic forecasts. If a user inputs "X city," the future appearance of that city will be reflected in the scenario. The future prediction unit can also collect future prediction data for local areas in real time, and the generation AI unit can customize the scenario and images based on that data. For example, it can generate scenarios that incorporate future technology and infrastructure. It also provides an option for users to input detailed future prediction data, and the generation AI unit will customize the animation based on that input. In this way, by incorporating future prediction data for local areas, it is possible to create science fiction anime set in local areas of the future.

[0070] The animation generation system can further include an emotion estimation unit that estimates the user's emotional state. For example, when a user inputs the name of a region, the emotion estimation unit analyzes the user's facial expression and tone of voice to estimate their emotional state. Based on the estimated emotion, the generation AI unit customizes the scenario and images. For example, if the user feels "excited," it generates a scenario with many action scenes. The emotion estimation unit can also analyze the user's emotions in real time and adjust the content of the animation based on the results. For example, if the analysis indicates that the user "wants to relax," it generates a relaxing scenario. This allows the system to generate animation that matches the user's emotions, providing content that appeals to the user's emotions more.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The user inputs the name of a region in the region name input section. For example, the user inputs "XX City." Step 2: The scenario generation unit generates an anime scenario based on the name of the region input by the region name input unit. For example, the scenario generation unit creates a story that incorporates the history, culture, and scenery of the region. Step 3: The image generation unit generates animated images based on the scenario generated by the scenario generation unit. For example, the image generation unit creates images that realistically reproduce local scenery, buildings, and natural environments. Step 4: The sound generation unit generates sounds for the animation based on the scenario generated by the scenario generation unit. For example, the sound generation unit creates character voices, background music, sound effects, etc. Step 5: The generation AI unit controls the scenario generation unit, video generation unit, and audio generation unit. For example, the generation AI unit oversees the scenario, video, and audio generation processes and manages the overall quality.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] 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 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 region name input section for inputting a region name; a scenario generation unit that generates an animation scenario based on the name of the region input by the region name input unit; a video generation unit that generates an animation video based on the scenario generated by the scenario generation unit; a sound generation unit that generates sounds for the animation based on the scenario generated by the scenario generation unit; a generation AI unit that controls the scenario generation unit, the video generation unit, and the audio generation unit. A system characterized by:

2. The locality name input section is Enter the name of the region along with the theme or genre you want, The generation AI unit Customize your anime based on the theme or genre 2. The system of claim 1.

3. The locality name input section is In addition to entering the name of the region, you can upload photos and videos of the region. The generation AI unit Generate more realistic images based on the photos and videos 2. The system of claim 1.

4. The generation AI unit When generating the images, seasonal scenery and events from the region are incorporated. Expressing the charm of each season 2. The system of claim 1.

5. The generation AI unit When generating scenarios, we incorporate local future forecast data. Create a sci-fi anime set in a rural area of ​​the future 2. The system of claim 1.

6. The locality name input section is Enter the name of the region plus the user's emotional state, The generation AI unit Generate scenarios and images based on the emotional state 2. The system of claim 1.

7. The locality name input section is It analyzes the sentiment of users when they enter the name of a region in real time, The generation AI unit Adjust the tone and atmosphere of your animation based on those emotions 2. The system of claim 1.

8. The generation AI unit When generating scenarios, we collect emotional data from local residents and tourists. Create an emotionally rich scenario based on the emotion data 2. The system of claim 1.

Images