System

Abstract

An object of a system according to an embodiment is to efficiently generate and provide elements and objects constituting virtual spaces by using generation AI.SOLUTION: A system includes a generation unit, a combination unit, and a provision unit. The generation unit generates elements and objects of a scenario, a landscape, a living thing, a building, music, and art using the generation AI. The combining unit combines the elements and objects generated by the generating unit. The providing unit provides the elements and objects combined by the combining unit to the user.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 technology has the drawback of requiring time and effort to generate the elements and objects that make up a virtual space, as they are generated manually.

[0005] The system according to the embodiment aims to efficiently generate and provide elements and objects that make up a virtual space using a generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a combination unit, and a provision unit. The generation unit generates elements and objects such as scenarios, landscapes, creatures, buildings, music, and art using a generation AI. The combination unit combines the elements and objects generated by the generation unit. The provision unit provides the elements and objects combined by the combination unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate and provide elements and objects that make up a virtual space using a generation AI. [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 virtual space system according to an embodiment of the present invention is a system in which a generation AI automatically generates all elements, allowing people to explore and enjoy a variety of experiences. As a result, the virtual space system can provide infinite possibilities and diversity by combining elements and objects such as scenarios, landscapes, creatures, buildings, music, and art.

[0029] A virtual space system according to an embodiment includes a generation unit, a combination unit, and a provision unit. The generation unit generates elements and objects, such as scenarios, landscapes, creatures, buildings, music, and art, using a generation AI. For example, the generation AI generates a scenario including a story plot and a dialogue flow using technologies such as deep learning and GAN (generative artificial network). The generation AI can also generate landscapes, such as natural landscapes and urban landscapes. The generation AI can also generate creatures, such as animals, plants, and fictional creatures. For example, the generation AI uses deep learning to generate a story plot based on user prompts. The generation AI uses GAN to generate realistic natural landscapes. The generation AI uses deep learning to generate fictional creatures. The combination unit combines the elements and objects generated by the generation unit. For example, the combination unit can perform random combinations or combinations based on a theme. The combination unit can also combine elements and objects based on user instructions. For example, the combination unit combines the generated scenarios and landscapes to create a virtual space that a user can explore. The combination unit combines the generated creatures and buildings to create a scenario that a user can experience. The combination unit combines the generated music and art to create a virtual space that the user can enjoy. The providing unit provides the elements and objects combined by the combination unit to the user. For example, the providing unit displays the elements and objects through a user interface. The providing unit can also provide text, images, audio, video, etc. as output formats. For example, the providing unit displays the virtual space through a web browser. The providing unit displays the virtual space through a VR headset. The providing unit displays the virtual space through a smartphone app. As a result, the virtual space system according to the embodiment can generate a variety of elements and objects using a generation AI, combine them, and provide them to the user, thereby providing infinite possibilities and diversity.

[0030] The generation unit can generate more personalized elements by reflecting the user's past behavioral history and preferences in the elements. For example, the generation unit uses a generation AI to analyze the user's past behavioral history and generate a personalized scenario based on that data. For example, the scenario may incorporate themes and characters that the user has previously liked. The generation unit also uses a generation AI to learn the user's preferences and generate personalized scenery based on that data. For example, the AI ​​may reflect the user's preferred color tones and types of scenery. The generation unit also uses a generation AI to analyze the user's past music playback history and generate personalized music based on that data. For example, the AI ​​may incorporate the user's favorite genres and artist styles. This allows the generation unit to provide a more personalized experience by generating elements based on the user's past behavioral history and preferences.

[0031] The generation unit can reflect different cultural and historical backgrounds in the elements, providing a diverse cultural experience. For example, the generation AI in the generation unit generates scenarios that incorporate elements of different cultures. For example, it can create a scenario that reflects traditional Japanese culture or a scenario set in medieval Europe. The generation unit also generates landscapes that reflect historical backgrounds. For example, it can recreate the streets of ancient Rome or the scenery of Renaissance Italy. The generation unit also generates music from different cultures. For example, it can create music that incorporates traditional African music or classical Indian music. This allows the generation unit to provide a diverse cultural experience by reflecting different cultures and historical backgrounds.

[0032] The generation unit can add a function that allows the user to make interactive changes to elements in real time. For example, the generation unit provides a function that allows the user to make changes in real time to a scenario generated by the generation AI. For example, the generation unit allows the user to select the development of the scenario or the behavior of characters. The generation unit also provides a function that allows the user to make changes in real time to scenery generated by the generation AI. For example, the generation unit allows the user to change the weather or time of day. The generation unit also provides a function that allows the user to make changes in real time to music generated by the generation AI. For example, the generation unit allows the user to adjust the type of instrument or tempo. This allows the user to change elements interactively in real time, providing a more personalized experience.

[0033] The generative AI can provide a more personalized experience by reflecting the user's past experience history and preferences based on these elements. The generative AI, for example, analyzes the user's past experience history and provides a personalized scenario based on that data. For example, it incorporates elements of scenarios that the user liked in the past. The generative AI can also analyze the user's past experience history and provide personalized scenery based on that data. For example, it can recreate places the user has visited in the past or scenery that the user liked. The generative AI can also analyze the user's past experience history and provide personalized music based on that data. For example, it can incorporate the style of music that the user liked in the past. This allows the user to provide a more personalized experience by providing an experience based on the user's past experience history.

[0034] Generative AI can provide experiences in different industries and fields based on elements, and discover new market needs. For example, generative AI can provide experiences in different industries based on generated scenarios. For example, it can provide simulations in the medical field or interactive stories in the entertainment field. Generative AI can also provide experiences in different fields based on generated landscapes. For example, it can provide virtual tours in the tourism industry or historical reenactments in the education field. Generative AI can also provide experiences in different industries based on generated music. For example, it can create new songs in the music industry or provide healing music in the relaxation industry. In this way, new market needs can be discovered by providing experiences in different industries and fields.

[0035] Generative AI can add a function that allows users to share experiences based on elements and collaborate with other users to create an experience. For example, generative AI provides a function that allows users to collaborate with other users to create an experience based on a generated scenario. For example, multiple users can participate in the same scenario and collaborate to advance the story. Generative AI also provides a function that allows users to collaborate with other users to create an experience based on a generated landscape. For example, multiple users can explore the same virtual space and collaborate to change the landscape. Generative AI also provides a function that allows users to collaborate with other users to create an experience based on generated music. For example, multiple users can play the same piece of music and collaborate to create music. This allows users to share experiences and collaborate to create an experience with other users, providing a more interactive experience.

[0036] Generative AI can support more personalized creative expression by reflecting the user's past creative history and preferences in the elements. For example, generative AI can analyze the user's past creative history and provide a personalized scenario based on that data. For example, it can incorporate elements of characters and stories created by the user in the past. Generative AI can also learn the user's preferences and provide personalized scenery based on that data. For example, it can reflect the user's preferred color tones and design elements. Generative AI can also analyze the user's past music production history and provide personalized music based on that data. For example, it can incorporate the user's preferred genres and styles. This allows for more personalized creative expression by supporting creative expression based on the user's past creative history and preferences.

[0037] Generative AI can support diverse creative expressions by reflecting different art forms and styles in elements. For example, generative AI can generate scenarios that incorporate different art forms. For example, it can create scenarios that combine elements such as painting, sculpture, and performance art. Generative AI can also generate landscapes that reflect different styles. For example, it can create landscapes that incorporate art styles such as impressionism, cubism, and surrealism. Generative AI can also generate music that reflects different musical styles. For example, it can create music that combines musical styles such as classical, jazz, and rock. This allows it to support diverse creative expressions by reflecting different art forms and styles.

[0038] Generative AI can support creative expression by adding the ability for users to interactively change elements in real time. For example, generative AI provides a function that allows users to make changes to generated scenarios in real time. For example, it allows users to select the development of the scenario or the actions of characters. Generative AI also provides a function that allows users to make changes to generated scenery in real time. For example, it allows users to change the weather or time of day. Generative AI also provides a function that allows users to make changes to generated music in real time. For example, it allows users to adjust the type of instrument or tempo. This allows users to interactively change elements in real time, providing more personalized creative expression.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The generation unit can analyze the user's health state in real time and adjust the elements according to their health state. For example, the generation AI can analyze the user's heart rate and stress level in real time and fine-tune the scenario development according to their health state. If the user is feeling highly stressed, the generation unit can add relaxing elements to reflect their health state. The generation unit can also analyze the user's health state in real time and fine-tune the color tone and weather of the scenery generated by the generation AI according to their health state. If the user is feeling tired, the generation unit can change the scenery to a more gentle color tone to reflect their health state. The generation unit can also analyze the user's health state in real time and fine-tune the tempo and melody of the music generated by the generation AI according to their health state. If the user needs relaxation, the tempo of the music can be slowed down to reflect their health state. This allows the generated elements to be fine-tuned according to the user's health state, providing a more health-conscious experience.

[0041] The generation unit can generate educational elements by reflecting the user's learning history and knowledge level in the elements. For example, the generation AI analyzes the user's past learning history and generates a personalized educational scenario based on that data. The scenario incorporates the user's past themes and areas of interest. The generation unit also learns the user's knowledge level and generates personalized educational scenes based on that data, reflecting color tones and types of scenery that are easy for the user to understand. Furthermore, the generation unit analyzes the user's past learning history and generates personalized educational music based on that data, incorporating the user's favorite genres and artist styles. This makes it possible to provide a more educational experience by generating elements based on the user's learning history and knowledge level.

[0042] The generation unit can generate a collaborative experience by reflecting the user's social relationships in the elements. For example, the generation AI analyzes the user's social network and generates a personalized collaborative scenario based on that data. The scenario incorporates themes and characters the user wants to share with friends and family. The generation unit also learns the user's social relationships and generates a personalized collaborative landscape based on that data, reflecting the color tones and types of scenery the user enjoys with friends and family. The generation unit also analyzes the user's social network and generates personalized collaborative music based on that data, incorporating genres and artist styles that the user enjoys with friends and family. This allows for a more collaborative experience by generating elements based on the user's social relationships.

[0043] The generation unit can collect user feedback on elements in real time and make adjustments based on the feedback. For example, the generation unit can collect user feedback on a scenario generated by the generation AI in real time and fine-tune the scenario's development based on the feedback. If the user does not like a particular scenario development, the scenario is changed to reflect that feedback. The generation unit can also collect user feedback on scenery generated by the generation AI in real time and fine-tune the scenery's color tone and weather based on the feedback. If the user does not like a particular scenery, the scenery is changed to reflect that feedback. The generation unit can also collect user feedback on music generated by the generation AI in real time and fine-tune the music's tempo and melody based on the feedback. If the user does not like a particular piece of music, the music is changed to reflect that feedback. This allows for fine-tuning of generated elements based on user feedback, providing an experience that is more tailored to the user.

[0044] The generation unit can analyze the user's physical movements in real time for elements and make adjustments according to the movements. For example, the generation AI can analyze the user's physical movements in real time for a scenario generated by the generation AI and fine-tune the development of the scenario according to the movements. When the user makes a specific movement, the scenario is changed to reflect that movement. The generation unit can also analyze the user's physical movements in real time for scenery generated by the generation AI and fine-tune the color tone and weather of the scenery according to the movements. When the user makes a specific movement, the scenery is changed to reflect that movement. The generation unit can also analyze the user's physical movements in real time for music generated by the generation AI and fine-tune the tempo and melody of the music according to the movements. When the user makes a specific movement, the music is changed to reflect that movement. This allows for fine-tuning of the generated elements according to the user's physical movements, providing a more interactive experience.

[0045] The generation unit can analyze the user's environmental data in real time and adjust the elements according to the environment. For example, the generation AI can analyze environmental data around the user in real time for a scenario it generates and fine-tune the scenario's development according to the environment. If the user is in a quiet environment, the scenario is changed to reflect that environment. The generation unit can also analyze environmental data around the user in real time for a landscape it generates and fine-tune the color tone and weather of the landscape according to the environment. If the user is in a bright environment, the landscape is changed to reflect that environment. The generation unit can also analyze environmental data around the user in real time for music it generates and fine-tune the tempo and melody of the music according to the environment. If the user is in a noisy environment, the music is changed to reflect that environment. This allows for fine-tuning of the generated elements according to the user's environmental data, providing an experience that is more suited to the environment.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The generation unit uses generative AI to generate elements and objects such as scenarios, landscapes, creatures, buildings, music, and art. For example, the generative AI uses technologies such as deep learning and GAN (generative artificial network) to generate scenarios including story plots and dialogue flows. The generative AI can also generate landscapes such as natural landscapes and urban landscapes. Furthermore, the generative AI can generate creatures such as animals, plants, and fictional creatures. For example, the generative AI uses deep learning to generate story plots based on user prompts. The generative AI uses GAN to generate realistic natural landscapes. The generative AI uses deep learning to generate fictional creatures. Step 2: The combination unit combines the elements and objects generated by the generation unit. For example, the combination unit can perform random combinations or combinations based on a theme. The combination unit can also combine elements and objects based on user instructions. For example, the combination unit combines the generated scenarios and landscapes to create a virtual space that the user can explore. The combination unit combines the generated creatures and structures to create a scenario that the user can experience. The combination unit combines the generated music and art to create a virtual space that the user can enjoy. Step 3: The providing unit provides the elements and objects combined by the combining unit to the user. For example, the providing unit displays the elements and objects through a user interface. The providing unit may also provide text, images, audio, video, etc. as output formats. For example, the providing unit displays the virtual space through a web browser. The providing unit displays the virtual space through a VR headset. The providing unit displays the virtual space through a smartphone app.

[0048] (Example 2) The virtual space system according to an embodiment of the present invention is a system in which a generation AI automatically generates all elements, allowing people to explore and enjoy a variety of experiences. As a result, the virtual space system can provide infinite possibilities and diversity by combining elements and objects such as scenarios, landscapes, creatures, buildings, music, and art.

[0049] A virtual space system according to an embodiment includes a generation unit, a combination unit, and a provision unit. The generation unit generates elements and objects, such as scenarios, landscapes, creatures, buildings, music, and art, using a generation AI. For example, the generation AI generates a scenario including a story plot and a dialogue flow using technologies such as deep learning and GAN (generative artificial network). The generation AI can also generate landscapes, such as natural landscapes and urban landscapes. The generation AI can also generate creatures, such as animals, plants, and fictional creatures. For example, the generation AI uses deep learning to generate a story plot based on user prompts. The generation AI uses GAN to generate realistic natural landscapes. The generation AI uses deep learning to generate fictional creatures. The combination unit combines the elements and objects generated by the generation unit. For example, the combination unit can perform random combinations or combinations based on a theme. The combination unit can also combine elements and objects based on user instructions. For example, the combination unit combines the generated scenarios and landscapes to create a virtual space that a user can explore. The combination unit combines the generated creatures and buildings to create a scenario that a user can experience. The combination unit combines the generated music and art to create a virtual space that the user can enjoy. The providing unit provides the elements and objects combined by the combination unit to the user. For example, the providing unit displays the elements and objects through a user interface. The providing unit can also provide text, images, audio, video, etc. as output formats. For example, the providing unit displays the virtual space through a web browser. The providing unit displays the virtual space through a VR headset. The providing unit displays the virtual space through a smartphone app. As a result, the virtual space system according to the embodiment can generate a variety of elements and objects using a generation AI, combine them, and provide them to the user, thereby providing infinite possibilities and diversity.

[0050] The generation unit can analyze the user's emotions in real time for the elements and make adjustments according to the emotions. For example, the generation unit can analyze the user's emotions in real time for a scenario generated by the generation AI and fine-tune the development of the scenario according to the emotions. For example, if the user feels surprised, the generation unit can add a new surprise element to the scenario to reflect that emotion. The generation unit can also analyze the user's emotions in real time for a landscape generated by the generation AI and fine-tune the color tone and weather of the landscape according to the emotions. For example, if the user feels relaxed, the generation unit can change the landscape to a more gentle color tone to reflect that emotion. The generation unit can also analyze the user's emotions in real time for music generated by the generation AI and fine-tune the tempo and melody of the music according to the emotions. For example, if the user feels excited, the tempo of the music can be increased to reflect that emotion. This allows for fine-tuning of the generated elements according to the user's emotions, providing a more personalized experience.

[0051] The generation unit can generate more personalized elements by reflecting the user's past behavioral history and preferences in the elements. For example, the generation unit uses a generation AI to analyze the user's past behavioral history and generate a personalized scenario based on that data. For example, the scenario may incorporate themes and characters that the user has previously liked. The generation unit also uses a generation AI to learn the user's preferences and generate personalized scenery based on that data. For example, the AI ​​may reflect the user's preferred color tones and types of scenery. The generation unit also uses a generation AI to analyze the user's past music playback history and generate personalized music based on that data. For example, the AI ​​may incorporate the user's favorite genres and artist styles. This allows the generation unit to provide a more personalized experience by generating elements based on the user's past behavioral history and preferences.

[0052] The generation unit can use the emotion estimation function to reflect the user's emotions and generate scenarios and scenery based on those emotions. For example, the generation unit uses the emotion estimation function to analyze the user's emotions and generate a scenario based on those emotions. For example, if the user is feeling happy, the generation unit creates a positive scenario that reflects that emotion. The generation unit also uses the emotion estimation function to analyze the user's emotions and generate scenery based on those emotions. For example, if the user is feeling calm, the generation unit creates a calm scenery that reflects that emotion. The generation unit also uses the emotion estimation function to analyze the user's emotions and generate music based on those emotions. For example, if the user is feeling excited, the generation unit creates energetic music that reflects that emotion. In this way, by generating scenarios and scenery based on the user's emotions, it is possible to provide an experience that is more in tune with the user's emotions.

[0053] The generation unit can reflect different cultural and historical backgrounds in the elements, providing a diverse cultural experience. For example, the generation AI in the generation unit generates scenarios that incorporate elements of different cultures. For example, it can create a scenario that reflects traditional Japanese culture or a scenario set in medieval Europe. The generation unit also generates landscapes that reflect historical backgrounds. For example, it can recreate the streets of ancient Rome or the scenery of Renaissance Italy. The generation unit also generates music from different cultures. For example, it can create music that incorporates traditional African music or classical Indian music. This allows the generation unit to provide a diverse cultural experience by reflecting different cultures and historical backgrounds.

[0054] The generation unit can add a function that allows the user to make interactive changes to elements in real time. For example, the generation unit provides a function that allows the user to make changes in real time to a scenario generated by the generation AI. For example, the generation unit allows the user to select the development of the scenario or the behavior of characters. The generation unit also provides a function that allows the user to make changes in real time to scenery generated by the generation AI. For example, the generation unit allows the user to change the weather or time of day. The generation unit also provides a function that allows the user to make changes in real time to music generated by the generation AI. For example, the generation unit allows the user to adjust the type of instrument or tempo. This allows the user to change elements interactively in real time, providing a more personalized experience.

[0055] The generation unit can use the emotion estimation function to reflect the user's emotions and generate music and art based on those emotions. For example, the generation unit uses the emotion estimation function to analyze the user's emotions and generate music based on those emotions. For example, if the user is feeling sad, it creates melancholic music that reflects that emotion. The generation unit also uses the emotion estimation function to analyze the user's emotions and generate artwork based on those emotions. For example, if the user is feeling happy, it creates a painting in bright colors that reflects that emotion. The generation unit also uses the emotion estimation function to analyze the user's emotions and generate poetry or stories based on those emotions. For example, if the user is feeling surprised, it creates a story that includes a surprising element that reflects that emotion. This allows the generation of music and art based on the user's emotions to provide an experience that is more in tune with the user's emotions.

[0056] The generation AI can analyze the user's emotions based on these elements in real time and generate an experience scenario that corresponds to that emotion. For example, the generation AI can analyze the user's emotions in real time for a generated scenario and automatically generate a new scenario that corresponds to that emotion. For example, if the user is feeling excited, an action scene that reflects that emotion is added. The generation AI can also analyze the user's emotions in real time for a generated landscape and automatically generate a new landscape that corresponds to that emotion. For example, if the user is feeling relaxed, a calm landscape that reflects that emotion is added. The generation AI can also analyze the user's emotions in real time for generated music and automatically generate new music that corresponds to that emotion. For example, if the user is feeling sad, melancholic music that reflects that emotion is added. This allows the system to automatically generate an experience scenario that corresponds to the user's emotions, providing an experience that is more in tune with their emotions.

[0057] The generative AI can provide a more personalized experience by reflecting the user's past experience history and preferences based on these elements. The generative AI, for example, analyzes the user's past experience history and provides a personalized scenario based on that data. For example, it incorporates elements of scenarios that the user liked in the past. The generative AI can also analyze the user's past experience history and provide personalized scenery based on that data. For example, it can recreate places the user has visited in the past or scenery that the user liked. The generative AI can also analyze the user's past experience history and provide personalized music based on that data. For example, it can incorporate the style of music that the user liked in the past. This allows the user to provide a more personalized experience by providing an experience based on the user's past experience history.

[0058] The generative AI can use an emotion estimation function to reflect the user's emotions based on the elements, providing an emotion-based experience. For example, the generative AI can use the emotion estimation function to analyze the user's emotions and provide a scenario based on those emotions. For example, if the user is feeling happy, it can create a positive scenario that reflects that emotion. The generative AI can also use the emotion estimation function to analyze the user's emotions and provide a landscape based on that emotion. For example, if the user is feeling calm, it can create a serene landscape that reflects that emotion. The generative AI can also use the emotion estimation function to analyze the user's emotions and provide music based on that emotion. For example, if the user is feeling excited, it can create energetic music that reflects that emotion. This makes it possible to provide an experience that is more in tune with the user's emotions by providing an experience based on the user's emotions.

[0059] Generative AI can provide experiences in different industries and fields based on elements, and discover new market needs. For example, generative AI can provide experiences in different industries based on generated scenarios. For example, it can provide simulations in the medical field or interactive stories in the entertainment field. Generative AI can also provide experiences in different fields based on generated landscapes. For example, it can provide virtual tours in the tourism industry or historical reenactments in the education field. Generative AI can also provide experiences in different industries based on generated music. For example, it can create new songs in the music industry or provide healing music in the relaxation industry. In this way, new market needs can be discovered by providing experiences in different industries and fields.

[0060] Generative AI can add a function that allows users to share experiences based on elements and collaborate with other users to create an experience. For example, generative AI provides a function that allows users to collaborate with other users to create an experience based on a generated scenario. For example, multiple users can participate in the same scenario and collaborate to advance the story. Generative AI also provides a function that allows users to collaborate with other users to create an experience based on a generated landscape. For example, multiple users can explore the same virtual space and collaborate to change the landscape. Generative AI also provides a function that allows users to collaborate with other users to create an experience based on generated music. For example, multiple users can play the same piece of music and collaborate to create music. This allows users to share experiences and collaborate to create an experience with other users, providing a more interactive experience.

[0061] The generative AI can use an emotion estimation function to reflect the user's emotions based on the elements, providing a collaborative experience based on emotions. For example, the generative AI can use the emotion estimation function to analyze the user's emotions and provide a collaborative scenario based on those emotions. For example, multiple users can share the same emotion and progress through a scenario that reflects that emotion. The generative AI can also use the emotion estimation function to analyze the user's emotions and provide a collaborative landscape based on those emotions. For example, multiple users can share the same emotion and explore a landscape that reflects that emotion. The generative AI can also use the emotion estimation function to analyze the user's emotions and provide collaborative music based on those emotions. For example, multiple users can share the same emotion and collaboratively play a piece of music that reflects that emotion. This allows for a collaborative experience based on the user's emotions, providing an experience that is more in tune with their emotions.

[0062] The generative AI can analyze the user's emotions in relation to elements in real time and support creative expression according to those emotions. For example, the generative AI can analyze the user's emotions in real time and provide a creative scenario according to those emotions. For example, if the user is feeling happy, it can create a positive scenario that reflects that emotion. The generative AI can also analyze the user's emotions in real time and provide a creative landscape according to those emotions. For example, if the user is feeling surprised, it can create a landscape that includes surprising elements that reflect that emotion. The generative AI can also analyze the user's emotions in real time and provide creative music according to those emotions. For example, if the user is feeling relaxed, it can create calm music that reflects that emotion. This can support creative expression according to the user's emotions, thereby providing creative expression that is more in line with the emotions.

[0063] Generative AI can support more personalized creative expression by reflecting the user's past creative history and preferences in the elements. For example, generative AI can analyze the user's past creative history and provide a personalized scenario based on that data. For example, it can incorporate elements of characters and stories created by the user in the past. Generative AI can also learn the user's preferences and provide personalized scenery based on that data. For example, it can reflect the user's preferred color tones and design elements. Generative AI can also analyze the user's past music production history and provide personalized music based on that data. For example, it can incorporate the user's preferred genres and styles. This allows for more personalized creative expression by supporting creative expression based on the user's past creative history and preferences.

[0064] The generative AI can use an emotion estimation function to reflect the user's emotions in elements and support creative expression based on emotions. For example, the generative AI can use the emotion estimation function to analyze the user's emotions and provide a creative scenario based on those emotions. For example, if the user is feeling happy, it can create a positive scenario that reflects that emotion. The generative AI can also use the emotion estimation function to analyze the user's emotions and provide a creative landscape based on that emotion. For example, if the user is feeling calm, it can create a calm landscape that reflects that emotion. The generative AI can also use the emotion estimation function to analyze the user's emotions and provide creative music based on that emotion. For example, if the user is feeling excited, it can create energetic music that reflects that emotion. This allows for creative expression that is more in line with the user's emotions by supporting creative expression based on the user's emotions.

[0065] Generative AI can support diverse creative expressions by reflecting different art forms and styles in elements. For example, generative AI can generate scenarios that incorporate different art forms. For example, it can create scenarios that combine elements such as painting, sculpture, and performance art. Generative AI can also generate landscapes that reflect different styles. For example, it can create landscapes that incorporate art styles such as impressionism, cubism, and surrealism. Generative AI can also generate music that reflects different musical styles. For example, it can create music that combines musical styles such as classical, jazz, and rock. This allows it to support diverse creative expressions by reflecting different art forms and styles.

[0066] Generative AI can support creative expression by adding the ability for users to interactively change elements in real time. For example, generative AI provides a function that allows users to make changes to generated scenarios in real time. For example, it allows users to select the development of the scenario or the actions of characters. Generative AI also provides a function that allows users to make changes to generated scenery in real time. For example, it allows users to change the weather or time of day. Generative AI also provides a function that allows users to make changes to generated music in real time. For example, it allows users to adjust the type of instrument or tempo. This allows users to interactively change elements in real time, providing more personalized creative expression.

[0067] The generative AI can use an emotion estimation function to reflect the user's emotions in elements, supporting interactive creative expression based on emotions. For example, the generative AI can use the emotion estimation function to analyze the user's emotions and provide an interactive scenario based on those emotions. For example, if the user is feeling happy, it creates a positive scenario that reflects that emotion. The generative AI can also use the emotion estimation function to analyze the user's emotions and provide an interactive landscape based on that emotion. For example, if the user is feeling calm, it creates a calm landscape that reflects that emotion. The generative AI can also use the emotion estimation function to analyze the user's emotions and provide interactive music based on that emotion. For example, if the user is feeling excited, it creates energetic music that reflects that emotion. This supports interactive creative expression based on the user's emotions, making it possible to provide creative expression that is more in tune with emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The generation unit can analyze the user's health state in real time and adjust the elements according to their health state. For example, the generation AI can analyze the user's heart rate and stress level in real time and fine-tune the scenario development according to their health state. If the user is feeling highly stressed, the generation unit can add relaxing elements to reflect their health state. The generation unit can also analyze the user's health state in real time and fine-tune the color tone and weather of the scenery generated by the generation AI according to their health state. If the user is feeling tired, the generation unit can change the scenery to a more gentle color tone to reflect their health state. The generation unit can also analyze the user's health state in real time and fine-tune the tempo and melody of the music generated by the generation AI according to their health state. If the user needs relaxation, the tempo of the music can be slowed down to reflect their health state. This allows the generated elements to be fine-tuned according to the user's health state, providing a more health-conscious experience.

[0070] The generation unit can generate educational elements by reflecting the user's learning history and knowledge level in the elements. For example, the generation AI analyzes the user's past learning history and generates a personalized educational scenario based on that data. The scenario incorporates the user's past themes and areas of interest. The generation unit also learns the user's knowledge level and generates personalized educational scenes based on that data, reflecting color tones and types of scenery that are easy for the user to understand. Furthermore, the generation unit analyzes the user's past learning history and generates personalized educational music based on that data, incorporating the user's favorite genres and artist styles. This makes it possible to provide a more educational experience by generating elements based on the user's learning history and knowledge level.

[0071] The generation unit can analyze the user's emotions in real time for the elements and provide an interactive experience that corresponds to the emotions. For example, the generation unit can analyze the user's emotions in real time for a scenario generated by the generation AI and interactively change the scenario's development according to the emotions. If the user feels surprised, the generation unit can add a new surprising element to the scenario to reflect that emotion. The generation unit can also analyze the user's emotions in real time for a landscape generated by the generation AI and interactively change the color tone and weather of the landscape according to the emotions. If the user feels relaxed, the generation unit can change the landscape to a more gentle color tone to reflect that emotion. The generation unit can also analyze the user's emotions in real time for music generated by the generation AI and interactively change the tempo and melody of the music according to the emotions. If the user feels excited, the tempo of the music can be increased to reflect that emotion. This allows for an interactive experience that corresponds to the user's emotions, providing an experience that is more in tune with their emotions.

[0072] The generation unit can generate a collaborative experience by reflecting the user's social relationships in the elements. For example, the generation AI analyzes the user's social network and generates a personalized collaborative scenario based on that data. The scenario incorporates themes and characters the user wants to share with friends and family. The generation unit also learns the user's social relationships and generates a personalized collaborative landscape based on that data, reflecting the color tones and types of scenery the user enjoys with friends and family. The generation unit also analyzes the user's social network and generates personalized collaborative music based on that data, incorporating genres and artist styles that the user enjoys with friends and family. This allows for a more collaborative experience by generating elements based on the user's social relationships.

[0073] The generation unit can analyze the user's emotions in real time for the elements and support creative expression according to the emotions. For example, the generation AI analyzes the user's emotions in real time and provides a creative scenario according to those emotions. If the user is feeling joy, it creates a positive scenario that reflects that emotion. The generation unit also analyzes the user's emotions in real time and provides a creative landscape according to those emotions. If the user is feeling surprised, it creates a landscape that includes surprising elements that reflect that emotion. The generation unit also analyzes the user's emotions in real time and provides creative music according to those emotions. If the user is feeling relaxed, it creates calm music that reflects that emotion. This supports creative expression according to the user's emotions, making it possible to provide creative expression that is more in line with the emotions.

[0074] The generation unit can collect user feedback on elements in real time and make adjustments based on the feedback. For example, the generation unit can collect user feedback on a scenario generated by the generation AI in real time and fine-tune the scenario's development based on the feedback. If the user does not like a particular scenario development, the scenario is changed to reflect that feedback. The generation unit can also collect user feedback on scenery generated by the generation AI in real time and fine-tune the scenery's color tone and weather based on the feedback. If the user does not like a particular scenery, the scenery is changed to reflect that feedback. The generation unit can also collect user feedback on music generated by the generation AI in real time and fine-tune the music's tempo and melody based on the feedback. If the user does not like a particular piece of music, the music is changed to reflect that feedback. This allows for fine-tuning of generated elements based on user feedback, providing an experience that is more tailored to the user.

[0075] The generation unit can analyze the user's emotions for the elements in real time and provide a shared experience that corresponds to the emotions. For example, the generation AI can analyze the user's emotions in real time and provide a shared scenario that corresponds to those emotions. Multiple users share the same emotion and progress through a scenario that reflects that emotion. The generation unit can also analyze the user's emotions in real time and provide a shared landscape that corresponds to those emotions. Multiple users share the same emotion and explore a landscape that reflects that emotion. The generation unit can also analyze the user's emotions in real time and provide shared music that corresponds to those emotions. Multiple users share the same emotion and jointly play music that reflects that emotion. This allows for a shared experience based on the user's emotions, thereby providing an experience that is more in tune with the emotions.

[0076] The generation unit can analyze the user's physical movements in real time for elements and make adjustments according to the movements. For example, the generation AI can analyze the user's physical movements in real time for a scenario generated by the generation AI and fine-tune the development of the scenario according to the movements. When the user makes a specific movement, the scenario is changed to reflect that movement. The generation unit can also analyze the user's physical movements in real time for scenery generated by the generation AI and fine-tune the color tone and weather of the scenery according to the movements. When the user makes a specific movement, the scenery is changed to reflect that movement. The generation unit can also analyze the user's physical movements in real time for music generated by the generation AI and fine-tune the tempo and melody of the music according to the movements. When the user makes a specific movement, the music is changed to reflect that movement. This allows for fine-tuning of the generated elements according to the user's physical movements, providing a more interactive experience.

[0077] The generation unit can analyze the user's emotions for the elements in real time and provide a personalized experience according to the emotions. For example, the generation AI analyzes the user's emotions in real time and provides a personalized scenario according to those emotions. If the user is feeling happy, it creates a positive scenario that reflects that emotion. The generation unit also analyzes the user's emotions in real time and provides a personalized landscape according to those emotions. If the user is feeling calm, it creates a calm landscape that reflects that emotion. Furthermore, the generation unit analyzes the user's emotions in real time and provides personalized music according to those emotions. If the user is feeling excited, it creates energetic music that reflects that emotion. This makes it possible to provide a personalized experience based on the user's emotions, thereby providing an experience that is more in tune with their emotions.

[0078] The generation unit can analyze the user's environmental data in real time and adjust the elements according to the environment. For example, the generation AI can analyze environmental data around the user in real time for a scenario it generates and fine-tune the scenario's development according to the environment. If the user is in a quiet environment, the scenario is changed to reflect that environment. The generation unit can also analyze environmental data around the user in real time for a landscape it generates and fine-tune the color tone and weather of the landscape according to the environment. If the user is in a bright environment, the landscape is changed to reflect that environment. The generation unit can also analyze environmental data around the user in real time for music it generates and fine-tune the tempo and melody of the music according to the environment. If the user is in a noisy environment, the music is changed to reflect that environment. This allows for fine-tuning of the generated elements according to the user's environmental data, providing an experience that is more suited to the environment.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The generation unit uses generative AI to generate elements and objects such as scenarios, landscapes, creatures, buildings, music, and art. For example, the generative AI uses technologies such as deep learning and GAN (generative artificial network) to generate scenarios including story plots and dialogue flows. The generative AI can also generate landscapes such as natural landscapes and urban landscapes. Furthermore, the generative AI can generate creatures such as animals, plants, and fictional creatures. For example, the generative AI uses deep learning to generate story plots based on user prompts. The generative AI uses GAN to generate realistic natural landscapes. The generative AI uses deep learning to generate fictional creatures. Step 2: The combination unit combines the elements and objects generated by the generation unit. For example, the combination unit can perform random combinations or combinations based on a theme. The combination unit can also combine elements and objects based on user instructions. For example, the combination unit combines the generated scenarios and landscapes to create a virtual space that the user can explore. The combination unit combines the generated creatures and structures to create a scenario that the user can experience. The combination unit combines the generated music and art to create a virtual space that the user can enjoy. Step 3: The providing unit provides the elements and objects combined by the combining unit to the user. For example, the providing unit displays the elements and objects through a user interface. The providing unit may also provide text, images, audio, video, etc. as output formats. For example, the providing unit displays the virtual space through a web browser. The providing unit displays the virtual space through a VR headset. The providing unit displays the virtual space through a smartphone app.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 generation part uses generative AI to generate scenarios, landscapes, creatures, buildings, music, and artistic elements and objects. a combination unit that combines the elements and objects generated by the generation unit; a providing unit that provides the elements or objects combined by the combining unit to a user. A system characterized by:

2. The generation unit Analyzing the user's emotions in real time for the elements and making adjustments according to the emotions 2. The system of claim 1.

3. The generation unit The above elements reflect different cultural and historical backgrounds to provide a diverse cultural experience.

2. The system of claim 1.

4. The generated AI is Analyzing the user's emotions in real time based on the elements and generating an experience scenario according to the emotions.

2. The system of claim 1.

5. The generated AI is Add a function that allows the user to share experiences based on the elements and create experiences collaboratively with other users.

2. The system of claim 1.

6. The generated AI is Analyzing the user's emotions in relation to the elements in real time and supporting creative expression according to the emotions 2. The system of claim 1.

7. The generated AI is The elements reflect the user's emotions, and creative expression based on the emotions is supported.

2. The system of claim 1.

8. The generated AI is The element reflects the user's emotion, and interactive creative expression based on the emotion is supported.

2. The system of claim 1.

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