System

Abstract

It is an object of a system according to an embodiment to generate an AR content on the basis of a demand of a user even if the user does not have expert knowledge.SOLUTION: A system according to an embodiment includes a request understanding unit, an image analysis unit, and a generation unit. The desire understanding unit understands a user's desire. The image analysis unit analyzes an illustration or an image based on the request understood by the request understanding unit. The generation unit generates the AR content based on the illustration or the image analyzed by the image analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have the drawback that the process of generating AR content based on user requests is complex and difficult to execute without specialized knowledge.

[0005] The system according to the embodiment aims to generate AR content based on a user's request without requiring specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a request understanding unit, an image analysis unit, and a generation unit. The request understanding unit understands a user's request. The image analysis unit analyzes illustrations and images based on the request understood by the request understanding unit. The generation unit generates AR content based on the illustrations and images analyzed by the image analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate AR content based on a user's request without requiring specialized knowledge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AR generation system according to the embodiment of the present invention is a system that automatically generates AR content based on a user's request, thereby enabling the user to easily create AR content.

[0029] The AR generation system according to the embodiment includes a request understanding unit, an image analysis unit, and a generation unit. The request understanding unit understands a user's request. For example, when a user inputs a request such as "I want to make a princess-like dress," the request understanding unit analyzes the request. The request understanding unit can also analyze voice input to understand the user's request. For example, when a user inputs by voice "I want to make a tourist guide," the request is analyzed. The request understanding unit can also analyze gesture input to understand the user's request. For example, when a user makes a gesture of drawing a "heart" with their hands, the request is analyzed. The image analysis unit analyzes illustrations and images based on the request understood by the request understanding unit. For example, the image analysis unit analyzes an illustration of a dress input by the user to understand its content. The image analysis unit can also analyze a photo of a tourist spot to understand its content. For example, the image analysis unit can analyze a photo of a tourist spot input by the user to identify the location. The image analysis unit can also analyze a hand-drawn illustration to understand its content. For example, the image analysis unit can analyze an illustration of an animal drawn by the user to identify the animal. The generation unit generates AR content based on illustrations and images analyzed by the image analysis unit. For example, the generation unit generates a dress to be displayed in AR based on the analyzed illustration of a dress. The generation unit can also generate AR content for a tourist guide based on analyzed photos of tourist spots. For example, AR content including a map and route guidance is generated based on photos of tourist spots. The generation unit can also generate animals to be displayed in AR based on analyzed hand-drawn illustrations. For example, AR content for animals is generated based on hand-drawn illustrations of animals. This allows the AR generation system according to the embodiment to automatically generate AR content based on user requests. For example, if a user simply says, "I want to make a princess-like dress," the generation unit automatically generates AR content for a dress. Furthermore, when creating a tourist guide, the user simply inputs information about the tourist spots, and the generation unit automatically generates AR content for the tourist guide.

[0030] The request understanding unit can generate more personalized AR content by referencing the user's past request history. For example, the generation AI stores the user's past request history in a database, and when a new request is entered, the request understanding unit generates personalized AR content by referencing the past history. For example, if a user who previously created a "princess dress" requests to create another dress, the request understanding unit can suggest a new dress based on the previous design. The request understanding unit also analyzes the user's past request history to extract common patterns and preferences. For example, if a user frequently creates AR content with an "animal" theme, the request understanding unit can suggest new requests that incorporate animals. Furthermore, the generation AI learns request trends based on the user's past request history and makes more appropriate suggestions for future requests. For example, if a user frequently requests "fantasy," the request understanding unit can generate AR content with enhanced fantasy elements. This allows the generation AI to generate more personalized AR content by referencing the user's past request history.

[0031] The request understanding unit can generate globally compatible AR content by taking into account different languages ​​and cultural backgrounds. For example, the generation AI automatically translates requests entered in different languages ​​and generates AR content appropriate for those languages ​​and cultural backgrounds. For example, if a user enters "I want to make an AR of cherry blossoms" in Japanese, the generation AI generates AR content with a cherry blossom theme. The request understanding unit also analyzes requests by taking into account different cultural backgrounds. For example, if an American user says, "I want to make an AR of Halloween," the generation AI generates AR content incorporating Halloween traditions and symbols. Furthermore, the request understanding unit allows the generation AI to reference a multilingual database to generate globally compatible AR content. For example, if a user enters "I want to make an AR of the Eiffel Tower" in French, the generation AI generates AR content with an Eiffel Tower theme. This allows the generation AI to generate global AR content by taking into account different languages ​​and cultural backgrounds.

[0032] The request understanding unit analyzes the user's gestures and movements and can generate AR content with intuitive operations. For example, the generation AI captures the user's gestures with a camera and analyzes those movements to understand the request. For example, if the user makes a gesture of drawing a "heart" with their hand, the generation AI generates heart-themed AR content. The request understanding unit also analyzes the user's movements and generates AR content with intuitive operations. For example, if the user makes a gesture of drawing a "star" with their finger, the generation AI generates star-themed AR content. The request understanding unit also uses gesture recognition technology to analyze the user's movements and understand the request. For example, if the user waves their hand, the generation AI interprets that movement as "goodbye" and generates related AR content. This allows the generation AI to analyze the user's gestures and movements and generate AR content with intuitive operations.

[0033] The generation AI can recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content. For example, the generation AI can recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content based on those illustrations. For example, it can recognize a "princess dress" drawn by a user and generate a dress to be displayed in AR. The generation AI can also scan hand-drawn illustrations and analyze them to generate AR content. For example, it can scan an "animal illustration" drawn by a user and display that animal in AR. The generation AI can also recognize hand-drawn illustrations in real time and generate AR content on the spot. For example, it can recognize a "landscape painting" drawn by a user on a tablet in real time and generate a landscape to be displayed in AR. This allows the generation AI to recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content.

[0034] The generative AI can automatically identify objects in an image and apply individual AR effects to each object. For example, the generative AI can automatically identify objects in an image and apply individual AR effects to each object. For example, for a "landscape photo" entered by a user, it can identify objects such as trees and flowers and apply animation effects to each. The generative AI can also use image analysis technology to identify objects in an image and apply appropriate AR effects to each object. For example, for a "photo of animals" entered by a user, it can apply different effects to each animal. Furthermore, the generative AI can identify objects in an image in real time and apply individual AR effects to each object. For example, for a "cityscape" captured by a user with a camera, it can identify objects such as buildings and cars and apply effects to each. This allows the system to automatically identify objects in an image and apply individual AR effects to each object.

[0035] Generative AI can analyze illustrations and images, automatically generate 3D models, and provide more three-dimensional AR content. For example, generative AI can analyze illustrations and images input by users and automatically generate 3D models. For example, it can generate three-dimensional AR content for cars based on a "car illustration" drawn by the user. Generative AI can also use image analysis technology to generate 3D models from input images and display them as AR content. For example, it can generate three-dimensional AR content for buildings based on a "photo of a building" taken by the user. Generative AI can also analyze illustrations and images in real time and automatically generate 3D models. For example, it can analyze an "animal illustration" drawn by a user on a tablet in real time and generate three-dimensional AR content for animals. This allows it to analyze illustrations and images, automatically generate 3D models, and provide three-dimensional AR content.

[0036] The generation AI can generate panoramic view AR content by combining multiple images. For example, the generation AI analyzes multiple images and combines them to generate panoramic view AR content. For example, it generates a 360-degree panoramic view based on multiple landscape photos taken by the user. The generation AI also uses image analysis technology to automatically combine multiple images to generate a seamless panoramic view. For example, it generates AR content that overlooks the entire room based on a photo of a room taken by the user. Furthermore, the generation AI analyzes multiple images in real time to generate panoramic view AR content. For example, it generates a panoramic view that overlooks the entire city based on cityscape photos taken continuously by the user. This allows the generation of panoramic view AR content by combining multiple images.

[0037] The generation AI can learn the user's operation history and make suggestions to make operations easier in the future. For example, the generation AI can store the user's operation history in a database and make suggestions to make operations easier in the future. For example, it can prioritize the display of functions that the user uses frequently. The generation AI can also analyze the user's operation history and extract common patterns. For example, it can automatically suggest tools and settings that the user uses frequently. Furthermore, the generation AI can provide customization options to make operations easier in the future based on the user's operation history. For example, it can set functions that the user uses frequently as shortcuts. This allows the generation AI to learn the user's operation history and make suggestions to make operations easier in the future.

[0038] Generative AI can recognize the user's voice and gestures, and provide functions that allow operation by voice or motion. Generative AI can, for example, recognize the user's voice and provide functions that allow operation by voice. For example, if a user simply says, "I want to make a dress," the generative AI automatically generates AR content for the dress. Generative AI can also use gesture recognition technology to analyze the user's movements, enabling intuitive operation. For example, the user can switch AR content by waving their hand. Furthermore, generative AI recognizes both voice and gestures to support user operation. For example, if the user moves their hand while saying "next page," it will move to the next page. This makes it possible to provide functions that recognize the user's voice and gestures, and allow operation by voice or motion.

[0039] The generative AI can provide customization options according to the user's age and skill level. For example, the generative AI can analyze the user's age and provide customization options according to that age. For example, it can provide a simple operation interface for young children. The generative AI can also analyze the user's skill level and provide customization options according to that skill level. For example, it can display a detailed guide for beginners and provide advanced functions for advanced users. The generative AI can also analyze both age and skill level to provide the optimal customization options for the user. For example, it can provide an interface that incorporates educational elements for elementary school students. This makes it possible to provide customization options according to the user's age and skill level.

[0040] The generation AI can gamify the user's operations, providing a function that allows the user to create AR content while having fun. The generation AI, for example, gamifies the user's operations, providing a function that allows the user to create AR content while having fun. For example, when the user creates a dress, a mini-game is provided in which the user collects points. The generation AI also provides an operation interface that incorporates game elements, allowing the user to operate the system while having fun. For example, when the user creates a tourist guide, the information is entered in the form of a quiz. Furthermore, the generation AI analyzes the user's operations in real time and gamifies them, making the operation more fun. For example, when the user creates a character, the avatar is customized in a game format. This makes it possible to gamify the user's operations, providing a function that allows the user to create AR content while having fun.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The request understanding unit can generate more personalized AR content by referencing the user's past request history. For example, the generation AI stores the user's past request history in a database, and when a new request is entered, it references the past history to generate personalized AR content. If a user who previously created a "princess dress" requests to create another dress, the request understanding unit will suggest a new dress based on the previous design. The request understanding unit also analyzes the user's past request history to extract common patterns and preferences. If the user frequently creates AR content with an "animal" theme, the request understanding unit will suggest new requests that incorporate animals. Furthermore, the generation AI learns request trends based on the user's past request history and makes more appropriate suggestions for future requests. If the user frequently requests "fantasy," the generation AI will generate AR content with enhanced fantasy elements. This allows the generation AI to generate more personalized AR content by referencing the user's past request history.

[0043] The request understanding unit can generate globally compatible AR content by taking into account different languages ​​and cultural backgrounds. For example, the generation AI can automatically translate requests entered in different languages ​​and generate AR content appropriate for those languages ​​and cultural backgrounds. For example, if a user enters "I want to make an AR of cherry blossoms" in Japanese, the generation AI will generate AR content with a cherry blossom theme. The request understanding unit also analyzes requests by taking into account different cultural backgrounds. For example, if an American user says "I want to make an AR of Halloween," the generation AI will generate AR content incorporating Halloween traditions and symbols. Furthermore, the request understanding unit allows the generation AI to reference a multilingual database to generate globally compatible AR content. For example, if a user enters "I want to make an AR of the Eiffel Tower" in French, the generation AI will generate AR content with an Eiffel Tower theme. This allows the generation of global AR content that takes into account different languages ​​and cultural backgrounds.

[0044] The request understanding unit analyzes the user's gestures and movements and can generate AR content with intuitive operations. For example, the generation AI captures the user's gestures with a camera, analyzes those movements, and understands the user's requests. If the user makes a gesture of drawing a "heart" with their hand, the generation AI generates heart-themed AR content. The request understanding unit also analyzes the user's movements and generates AR content with intuitive operations. If the user makes a gesture of drawing a "star" with their finger, the generation AI generates star-themed AR content. The request understanding unit also uses gesture recognition technology to analyze the user's movements and understand the user's requests. If the user waves their hand, the generation AI interprets that movement as "goodbye" and generates related AR content. This allows the user's gestures and movements to be analyzed and AR content to be generated with intuitive operations.

[0045] The generation AI can recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content. For example, it can recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content based on those illustrations. It can recognize a "princess dress" drawn by the user by hand and generate a dress to be displayed in AR. The generation AI can also scan hand-drawn illustrations, analyze them, and generate AR content. It can scan an "animal illustration" drawn by the user, and the generation AI can display that animal in AR. The generation AI can also recognize hand-drawn illustrations in real time and generate AR content on the spot. It can recognize a "landscape painting" drawn by the user on a tablet in real time and generate a landscape to be displayed in AR. This allows the generation AI to recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content.

[0046] The generative AI can automatically identify objects in an image and apply individual AR effects to each object. For example, it can automatically identify objects in an image and apply individual AR effects to each object. For a "landscape photo" entered by the user, it can identify objects such as trees and flowers and apply animation effects to each. The generative AI can also use image analysis technology to identify objects in an image and apply appropriate AR effects to each object. For an "animal photo" entered by the user, it can apply different effects to each animal. Furthermore, the generative AI can identify objects in an image in real time and apply individual AR effects to each object. For a "cityscape" captured by the user with a camera, it can identify objects such as buildings and cars and apply effects to each. This allows it to automatically identify objects in an image and apply individual AR effects to each object.

[0047] Generative AI can analyze illustrations and images, automatically generate 3D models, and provide more three-dimensional AR content. For example, it can analyze illustrations and images input by the user and automatically generate 3D models. It can generate three-dimensional AR content for cars based on a "car illustration" drawn by the user. Generative AI can also use image analysis technology to generate 3D models from input images and display them as AR content. It can generate three-dimensional AR content for buildings based on "photos of buildings" taken by the user. Generative AI can also analyze illustrations and images in real time and automatically generate 3D models. It can analyze "animal illustrations" drawn by the user on a tablet in real time and generate three-dimensional AR content for animals. This allows it to analyze illustrations and images, automatically generate 3D models, and provide three-dimensional AR content.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The request understanding unit understands the user's request. For example, if the user inputs a request such as "I want to make a princess-like dress," the request is analyzed. It can also analyze voice input and gesture input to understand the user's request. For example, if the user inputs by voice, "I want to make a tourist guide," the request is analyzed. Furthermore, if the user makes a gesture of drawing a "heart" with their hand, the request is analyzed. Step 2: The image analysis unit analyzes illustrations and images based on the requests understood by the request understanding unit. For example, it analyzes illustrations of dresses, photos of tourist spots, and hand-drawn illustrations entered by the user and understands their content. Step 3: The generation unit generates AR content based on the illustrations and images analyzed by the image analysis unit. For example, it can generate a dress to be displayed in AR based on the analyzed illustration of a dress, or generate AR content for a tourist guide based on photos of tourist spots. It can also generate AR content for animals based on hand-drawn illustrations of animals.

[0050] (Example 2) The AR generation system according to the embodiment of the present invention is a system that automatically generates AR content based on a user's request, thereby enabling the user to easily create AR content.

[0051] The AR generation system according to the embodiment includes a request understanding unit, an image analysis unit, and a generation unit. The request understanding unit understands a user's request. For example, when a user inputs a request such as "I want to make a princess-like dress," the request understanding unit analyzes the request. The request understanding unit can also analyze voice input to understand the user's request. For example, when a user inputs by voice "I want to make a tourist guide," the request is analyzed. The request understanding unit can also analyze gesture input to understand the user's request. For example, when a user makes a gesture of drawing a "heart" with their hands, the request is analyzed. The image analysis unit analyzes illustrations and images based on the request understood by the request understanding unit. For example, the image analysis unit analyzes an illustration of a dress input by the user to understand its content. The image analysis unit can also analyze a photo of a tourist spot to understand its content. For example, the image analysis unit can analyze a photo of a tourist spot input by the user to identify the location. The image analysis unit can also analyze a hand-drawn illustration to understand its content. For example, the image analysis unit can analyze an illustration of an animal drawn by the user to identify the animal. The generation unit generates AR content based on illustrations and images analyzed by the image analysis unit. For example, the generation unit generates a dress to be displayed in AR based on the analyzed illustration of a dress. The generation unit can also generate AR content for a tourist guide based on analyzed photos of tourist spots. For example, AR content including a map and route guidance is generated based on photos of tourist spots. The generation unit can also generate animals to be displayed in AR based on analyzed hand-drawn illustrations. For example, AR content for animals is generated based on hand-drawn illustrations of animals. This allows the AR generation system according to the embodiment to automatically generate AR content based on user requests. For example, if a user simply says, "I want to make a princess-like dress," the generation unit automatically generates AR content for a dress. Furthermore, when creating a tourist guide, the user simply inputs information about the tourist spots, and the generation unit automatically generates AR content for the tourist guide.

[0052] The request understanding unit can generate more personalized AR content by referencing the user's past request history. For example, the generation AI stores the user's past request history in a database, and when a new request is entered, the request understanding unit generates personalized AR content by referencing the past history. For example, if a user who previously created a "princess dress" requests to create another dress, the request understanding unit can suggest a new dress based on the previous design. The request understanding unit also analyzes the user's past request history to extract common patterns and preferences. For example, if a user frequently creates AR content with an "animal" theme, the request understanding unit can suggest new requests that incorporate animals. Furthermore, the generation AI learns request trends based on the user's past request history and makes more appropriate suggestions for future requests. For example, if a user frequently requests "fantasy," the request understanding unit can generate AR content with enhanced fantasy elements. This allows the generation AI to generate more personalized AR content by referencing the user's past request history.

[0053] The request understanding unit can analyze the user's tone of voice and facial expression to suggest AR content based on their emotions. For example, the generation AI analyzes the user's tone of voice to infer their emotions. For example, if a user says in an excited voice, "I want to create an AR adventure," the generation AI will suggest dynamic AR content that reflects their excitement. The request understanding unit also captures the user's facial expression with a camera, and the generation AI analyzes their emotions from their facial expression. For example, if a user says with a smile, "I want to make a princess dress," the generation AI will suggest a gorgeous dress that reflects their positive emotions. Furthermore, the request understanding unit analyzes both the tone of voice and facial expression to generate AR content based on their emotions. For example, if a user says in a calm voice, "I want to create a relaxing AR," the generation AI will suggest AR content with a calm landscape that reflects their relaxed emotion. This allows AR content to be suggested based on the user's emotions.

[0054] The request understanding unit can use the emotion estimation function to analyze the user's emotions in real time when entering their request and generate AR content that elicits positive emotions. For example, the request understanding unit can use the emotion estimation function to analyze the user's emotions in real time when entering their request and make suggestions to elicit positive emotions. For example, if a user says, "I want to create a new AR experience" with a slightly anxious tone, the generation AI displays an encouraging message to elicit positive emotions. The request understanding unit can also analyze the user's emotions in real time and generate AR content that elicits positive emotions. For example, if a user says, "I want to create a relaxing AR experience" in a tired voice, the generation AI suggests scenery or music that has a relaxing effect. Furthermore, the request understanding unit can use the emotion estimation function to analyze the user's emotions when entering their request and provide an interface that elicits positive emotions. For example, if the user is nervous, the generation AI displays a relaxing interface to elicit positive emotions. This allows the generation of AR content that elicits positive emotions from the user.

[0055] The request understanding unit can generate globally compatible AR content by taking into account different languages ​​and cultural backgrounds. For example, the generation AI automatically translates requests entered in different languages ​​and generates AR content appropriate for those languages ​​and cultural backgrounds. For example, if a user enters "I want to make an AR of cherry blossoms" in Japanese, the generation AI generates AR content with a cherry blossom theme. The request understanding unit also analyzes requests by taking into account different cultural backgrounds. For example, if an American user says, "I want to make an AR of Halloween," the generation AI generates AR content incorporating Halloween traditions and symbols. Furthermore, the request understanding unit allows the generation AI to reference a multilingual database to generate globally compatible AR content. For example, if a user enters "I want to make an AR of the Eiffel Tower" in French, the generation AI generates AR content with an Eiffel Tower theme. This allows the generation AI to generate global AR content by taking into account different languages ​​and cultural backgrounds.

[0056] The request understanding unit analyzes the user's gestures and movements and can generate AR content with intuitive operations. For example, the generation AI captures the user's gestures with a camera and analyzes those movements to understand the request. For example, if the user makes a gesture of drawing a "heart" with their hand, the generation AI generates heart-themed AR content. The request understanding unit also analyzes the user's movements and generates AR content with intuitive operations. For example, if the user makes a gesture of drawing a "star" with their finger, the generation AI generates star-themed AR content. The request understanding unit also uses gesture recognition technology to analyze the user's movements and understand the request. For example, if the user waves their hand, the generation AI interprets that movement as "goodbye" and generates related AR content. This allows the generation AI to analyze the user's gestures and movements and generate AR content with intuitive operations.

[0057] The request understanding unit can use the emotion estimation function to analyze the emotion a user expresses when entering a request and suggest AR content to alleviate negative emotions. For example, the request understanding unit can use the emotion estimation function to analyze the emotion a user expresses when entering a request and suggest AR content to alleviate negative emotions. For example, if a user sadly says, "I want to create an AR that will cheer me up," the generation AI can suggest cheerful AR content that will cheer them up. The request understanding unit can also analyze the user's emotion in real time and suggest ways to alleviate negative emotions. For example, if a user is feeling stressed, the generation AI can suggest AR content that has a relaxing effect. Furthermore, the request understanding unit can use the emotion estimation function to analyze the emotion a user expresses when entering a request and provide an interface to alleviate negative emotions. For example, if a user is feeling anxious, the generation AI can display an interface that provides a sense of security, thereby alleviating negative emotions. This allows AR content to be suggested to alleviate the user's negative emotions.

[0058] The generation AI can recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content. For example, the generation AI can recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content based on those illustrations. For example, it can recognize a "princess dress" drawn by a user and generate a dress to be displayed in AR. The generation AI can also scan hand-drawn illustrations and analyze them to generate AR content. For example, it can scan an "animal illustration" drawn by a user and display that animal in AR. The generation AI can also recognize hand-drawn illustrations in real time and generate AR content on the spot. For example, it can recognize a "landscape painting" drawn by a user on a tablet in real time and generate a landscape to be displayed in AR. This allows the generation AI to recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content.

[0059] The generative AI can automatically identify objects in an image and apply individual AR effects to each object. For example, the generative AI can automatically identify objects in an image and apply individual AR effects to each object. For example, for a "landscape photo" entered by a user, it can identify objects such as trees and flowers and apply animation effects to each. The generative AI can also use image analysis technology to identify objects in an image and apply appropriate AR effects to each object. For example, for a "photo of animals" entered by a user, it can apply different effects to each animal. Furthermore, the generative AI can identify objects in an image in real time and apply individual AR effects to each object. For example, for a "cityscape" captured by a user with a camera, it can identify objects such as buildings and cars and apply effects to each. This allows the system to automatically identify objects in an image and apply individual AR effects to each object.

[0060] The generation AI can use the emotion estimation function to analyze emotions toward illustrations and images input by the user and generate AR content based on those emotions. For example, the generation AI can use the emotion estimation function to analyze emotions toward illustrations and images input by the user and generate AR content based on those emotions. For example, it can generate AR content that reflects positive emotions toward a "smiling character" drawn by the user. The generation AI can also analyze emotions toward images input by the user and apply AR effects based on those emotions. For example, it can apply a relaxing effect to a "landscape photo" taken by the user. Furthermore, the generation AI can use the emotion estimation function to analyze emotions toward illustrations and images input by the user in real time and generate AR content based on those emotions. For example, it can generate AR content that soothes the emotions toward a "sad-looking character" drawn by the user. This allows the generation AI to analyze emotions toward illustrations and images input by the user and generate AR content based on those emotions.

[0061] Generative AI can analyze illustrations and images, automatically generate 3D models, and provide more three-dimensional AR content. For example, generative AI can analyze illustrations and images input by users and automatically generate 3D models. For example, it can generate three-dimensional AR content for cars based on a "car illustration" drawn by the user. Generative AI can also use image analysis technology to generate 3D models from input images and display them as AR content. For example, it can generate three-dimensional AR content for buildings based on a "photo of a building" taken by the user. Generative AI can also analyze illustrations and images in real time and automatically generate 3D models. For example, it can analyze an "animal illustration" drawn by a user on a tablet in real time and generate three-dimensional AR content for animals. This allows it to analyze illustrations and images, automatically generate 3D models, and provide three-dimensional AR content.

[0062] The generation AI can generate panoramic view AR content by combining multiple images. For example, the generation AI analyzes multiple images and combines them to generate panoramic view AR content. For example, it generates a 360-degree panoramic view based on multiple landscape photos taken by the user. The generation AI also uses image analysis technology to automatically combine multiple images to generate a seamless panoramic view. For example, it generates AR content that overlooks the entire room based on a photo of a room taken by the user. Furthermore, the generation AI analyzes multiple images in real time to generate panoramic view AR content. For example, it generates a panoramic view that overlooks the entire city based on cityscape photos taken continuously by the user. This allows the generation of panoramic view AR content by combining multiple images.

[0063] The generative AI can use its emotion estimation function to analyze the emotions associated with illustrations and images entered by users and provide customization options based on those emotions. For example, the generative AI can use its emotion estimation function to analyze the emotions associated with illustrations and images entered by users and provide customization options based on those emotions. For example, for a "happy landscape painting" drawn by a user, it can suggest customization options that reflect positive emotions. The generative AI can also analyze the emotions associated with images entered by users and provide customization options based on those emotions. For example, for a "relaxing landscape photo" taken by a user, it can suggest customization options that enhance the relaxing effect. Furthermore, the generative AI can use its emotion estimation function to analyze the emotions associated with illustrations and images entered by users in real time and provide customization options based on those emotions. For example, for a "sad character" drawn by a user, it can suggest customization options that soften the emotions. This allows the generative AI to analyze the emotions associated with illustrations and images entered by users and provide customization options based on those emotions.

[0064] The generation AI can learn the user's operation history and make suggestions to make operations easier in the future. For example, the generation AI can store the user's operation history in a database and make suggestions to make operations easier in the future. For example, it can prioritize the display of functions that the user uses frequently. The generation AI can also analyze the user's operation history and extract common patterns. For example, it can automatically suggest tools and settings that the user uses frequently. Furthermore, the generation AI can provide customization options to make operations easier in the future based on the user's operation history. For example, it can set functions that the user uses frequently as shortcuts. This allows the generation AI to learn the user's operation history and make suggestions to make operations easier in the future.

[0065] Generative AI can recognize the user's voice and gestures, and provide functions that allow operation by voice or motion. Generative AI can, for example, recognize the user's voice and provide functions that allow operation by voice. For example, if a user simply says, "I want to make a dress," the generative AI automatically generates AR content for the dress. Generative AI can also use gesture recognition technology to analyze the user's movements, enabling intuitive operation. For example, the user can switch AR content by waving their hand. Furthermore, generative AI recognizes both voice and gestures to support user operation. For example, if the user moves their hand while saying "next page," it will move to the next page. This makes it possible to provide functions that recognize the user's voice and gestures, and allow operation by voice or motion.

[0066] The generative AI can use its emotion estimation function to analyze in real time the stress and difficulty the user feels while operating the device and provide an assistance function to simplify the operation. For example, the generative AI can use its emotion estimation function to analyze in real time the stress the user feels while operating the device and provide an assistance function to simplify the operation. For example, it can display a help message when the user is having trouble. The generative AI can also analyze the user's emotions in real time and make suggestions to simplify the operation. For example, if the user is feeling stressed, it can display a simple operation guide. Furthermore, the generative AI can use its emotion estimation function to analyze the difficulty the user feels while operating the device and provide an interface to simplify the operation. For example, if the user is confused, it can switch to a simpler interface. This allows the generative AI to analyze in real time the stress and difficulty the user feels while operating the device and provide an assistance function to simplify the operation.

[0067] The generative AI can provide customization options according to the user's age and skill level. For example, the generative AI can analyze the user's age and provide customization options according to that age. For example, it can provide a simple operation interface for young children. The generative AI can also analyze the user's skill level and provide customization options according to that skill level. For example, it can display a detailed guide for beginners and provide advanced functions for advanced users. The generative AI can also analyze both age and skill level to provide the optimal customization options for the user. For example, it can provide an interface that incorporates educational elements for elementary school students. This makes it possible to provide customization options according to the user's age and skill level.

[0068] The generation AI can gamify the user's operations, providing a function that allows the user to create AR content while having fun. The generation AI, for example, gamifies the user's operations, providing a function that allows the user to create AR content while having fun. For example, when the user creates a dress, a mini-game is provided in which the user collects points. The generation AI also provides an operation interface that incorporates game elements, allowing the user to operate the system while having fun. For example, when the user creates a tourist guide, the information is entered in the form of a quiz. Furthermore, the generation AI analyzes the user's operations in real time and gamifies them, making the operation more fun. For example, when the user creates a character, the avatar is customized in a game format. This makes it possible to gamify the user's operations, providing a function that allows the user to create AR content while having fun.

[0069] Generative AI can use its emotion estimation function to analyze the joy and excitement a user feels while operating a device and provide an interface design that elicits positive emotions. For example, generative AI can use its emotion estimation function to analyze the joy and excitement a user feels while operating a device and provide an interface design that elicits positive emotions. For example, it can add animations or music when the user is having fun. Generative AI can also analyze the user's emotions in real time and make suggestions to elicit positive emotions. For example, if the user is excited, it can add effects to the interface. Furthermore, generative AI can use its emotion estimation function to analyze the joy and excitement a user feels while operating a device and provide an interface design that reinforces those emotions. For example, it can display a congratulatory message when the user succeeds. This allows generative AI to analyze the joy and excitement a user feels while operating a device and provide an interface design that elicits positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The request understanding unit can analyze the user's tone of voice and facial expression to suggest AR content based on their emotions. For example, the generation AI analyzes the user's tone of voice to infer their emotions. If the user says in an excited voice, "I want to create an AR adventure," the generation AI will suggest dynamic AR content that reflects that excitement. The request understanding unit also captures the user's facial expression with a camera, and the generation AI analyzes their emotions from that expression. If the user says with a smile, "I want to make a princess dress," the generation AI will suggest a gorgeous dress that reflects that positive emotion. Furthermore, the request understanding unit analyzes both the tone of voice and facial expression to generate AR content based on emotions. If the user says in a calm voice, "I want to create a relaxing AR," the generation AI will suggest AR content with a calm landscape that reflects that relaxed emotion. This allows AR content to be suggested based on the user's emotions.

[0072] The request understanding unit can generate more personalized AR content by referencing the user's past request history. For example, the generation AI stores the user's past request history in a database, and when a new request is entered, it references the past history to generate personalized AR content. If a user who previously created a "princess dress" requests to create another dress, the request understanding unit will suggest a new dress based on the previous design. The request understanding unit also analyzes the user's past request history to extract common patterns and preferences. If the user frequently creates AR content with an "animal" theme, the request understanding unit will suggest new requests that incorporate animals. Furthermore, the generation AI learns request trends based on the user's past request history and makes more appropriate suggestions for future requests. If the user frequently requests "fantasy," the generation AI will generate AR content with enhanced fantasy elements. This allows the generation AI to generate more personalized AR content by referencing the user's past request history.

[0073] The request understanding unit can use the emotion estimation function to analyze the user's emotions in real time when entering their request and generate AR content that elicits positive emotions. For example, the emotion estimation function can be used to analyze the user's emotions in real time when entering their request and make suggestions to elicit positive emotions. If a user says, "I want to create a new AR experience" in a slightly anxious voice, the generation AI displays an encouraging message to elicit positive emotions. The request understanding unit can also analyze the user's emotions in real time and generate AR content that elicits positive emotions. If a user says, "I want to create a relaxing AR experience" in a tired voice, the generation AI can suggest scenery or music that has a relaxing effect. The request understanding unit can also use the emotion estimation function to analyze the user's emotions when entering their request and provide an interface that elicits positive emotions. If the user is nervous, the generation AI can display a relaxing interface to elicit positive emotions. This makes it possible to generate AR content that elicits positive emotions from the user.

[0074] The request understanding unit can generate globally compatible AR content by taking into account different languages ​​and cultural backgrounds. For example, the generation AI can automatically translate requests entered in different languages ​​and generate AR content appropriate for those languages ​​and cultural backgrounds. For example, if a user enters "I want to make an AR of cherry blossoms" in Japanese, the generation AI will generate AR content with a cherry blossom theme. The request understanding unit also analyzes requests by taking into account different cultural backgrounds. For example, if an American user says "I want to make an AR of Halloween," the generation AI will generate AR content incorporating Halloween traditions and symbols. Furthermore, the request understanding unit allows the generation AI to reference a multilingual database to generate globally compatible AR content. For example, if a user enters "I want to make an AR of the Eiffel Tower" in French, the generation AI will generate AR content with an Eiffel Tower theme. This allows the generation of global AR content that takes into account different languages ​​and cultural backgrounds.

[0075] The request understanding unit analyzes the user's gestures and movements and can generate AR content with intuitive operations. For example, the generation AI captures the user's gestures with a camera, analyzes those movements, and understands the user's requests. If the user makes a gesture of drawing a "heart" with their hand, the generation AI generates heart-themed AR content. The request understanding unit also analyzes the user's movements and generates AR content with intuitive operations. If the user makes a gesture of drawing a "star" with their finger, the generation AI generates star-themed AR content. The request understanding unit also uses gesture recognition technology to analyze the user's movements and understand the user's requests. If the user waves their hand, the generation AI interprets that movement as "goodbye" and generates related AR content. This allows the user's gestures and movements to be analyzed and AR content to be generated with intuitive operations.

[0076] The request understanding unit can use the emotion estimation function to analyze the emotions expressed when a user inputs a request and suggest AR content to alleviate negative emotions. For example, the emotion estimation function can be used to analyze the emotions expressed when a user inputs a request and suggest AR content to alleviate negative emotions. If a user sadly says, "I want to create an AR that will cheer me up," the generation AI will suggest cheerful AR content that will cheer them up. The request understanding unit also analyzes the user's emotions in real time and makes suggestions to alleviate negative emotions. If the user is feeling stressed, the generation AI will suggest AR content that has a relaxing effect. Furthermore, the request understanding unit can use the emotion estimation function to analyze the emotions expressed when a user inputs a request and provide an interface to alleviate negative emotions. If the user is feeling anxious, the generation AI will display an interface that provides a sense of security, thereby alleviating negative emotions. This makes it possible to suggest AR content to alleviate the user's negative emotions.

[0077] The generation AI can recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content. For example, it can recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content based on those illustrations. It can recognize a "princess dress" drawn by the user by hand and generate a dress to be displayed in AR. The generation AI can also scan hand-drawn illustrations, analyze them, and generate AR content. It can scan an "animal illustration" drawn by the user, and the generation AI can display that animal in AR. The generation AI can also recognize hand-drawn illustrations in real time and generate AR content on the spot. It can recognize a "landscape painting" drawn by the user on a tablet in real time and generate a landscape to be displayed in AR. This allows the generation AI to recognize hand-drawn illustrations drawn by users with high accuracy and generate AR content.

[0078] The generative AI can automatically identify objects in an image and apply individual AR effects to each object. For example, it can automatically identify objects in an image and apply individual AR effects to each object. For a "landscape photo" entered by the user, it can identify objects such as trees and flowers and apply animation effects to each. The generative AI can also use image analysis technology to identify objects in an image and apply appropriate AR effects to each object. For an "animal photo" entered by the user, it can apply different effects to each animal. Furthermore, the generative AI can identify objects in an image in real time and apply individual AR effects to each object. For a "cityscape" captured by the user with a camera, it can identify objects such as buildings and cars and apply effects to each. This allows it to automatically identify objects in an image and apply individual AR effects to each object.

[0079] The generation AI can use its emotion estimation function to analyze emotions toward illustrations and images input by the user and generate AR content based on those emotions. For example, the emotion estimation function can be used to analyze emotions toward illustrations and images input by the user and generate AR content based on those emotions. For a "smiling character" drawn by the user, AR content reflecting a positive emotion can be generated. The generation AI can also analyze emotions toward images input by the user and apply AR effects based on those emotions. For a "landscape photo" taken by the user, an effect with a relaxing effect can be applied. Furthermore, the generation AI can use its emotion estimation function to analyze emotions toward illustrations and images input by the user in real time and generate AR content based on those emotions. For a "sad-looking character" drawn by the user, AR content that soothes the emotion can be generated. This allows the generation of AR content based on emotions to be generated by analyzing emotions toward illustrations and images input by the user.

[0080] Generative AI can analyze illustrations and images, automatically generate 3D models, and provide more three-dimensional AR content. For example, it can analyze illustrations and images input by the user and automatically generate 3D models. It can generate three-dimensional AR content for cars based on a "car illustration" drawn by the user. Generative AI can also use image analysis technology to generate 3D models from input images and display them as AR content. It can generate three-dimensional AR content for buildings based on "photos of buildings" taken by the user. Generative AI can also analyze illustrations and images in real time and automatically generate 3D models. It can analyze "animal illustrations" drawn by the user on a tablet in real time and generate three-dimensional AR content for animals. This allows it to analyze illustrations and images, automatically generate 3D models, and provide three-dimensional AR content.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The request understanding unit understands the user's request. For example, if the user inputs a request such as "I want to make a princess-like dress," the request is analyzed. It can also analyze voice input and gesture input to understand the user's request. For example, if the user inputs by voice, "I want to make a tourist guide," the request is analyzed. Furthermore, if the user makes a gesture of drawing a "heart" with their hand, the request is analyzed. Step 2: The image analysis unit analyzes illustrations and images based on the requests understood by the request understanding unit. For example, it analyzes illustrations of dresses, photos of tourist spots, and hand-drawn illustrations entered by the user and understands their content. Step 3: The generation unit generates AR content based on the illustrations and images analyzed by the image analysis unit. For example, it can generate a dress to be displayed in AR based on the analyzed illustration of a dress, or generate AR content for a tourist guide based on photos of tourist spots. It can also generate AR content for animals based on hand-drawn illustrations of animals.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 request understanding unit that understands a user's request; an image analysis unit that analyzes illustrations and images based on the request understood by the request understanding unit; a generation unit that generates AR content based on the illustrations and images analyzed by the image analysis unit. A system characterized by:

2. The request understanding unit Refer to the user's past request history to generate more personalized AR content.

2. The system of claim 1.

3. The request understanding unit Analyzes the user's tone of voice and facial expressions to suggest AR content based on their emotions 2. The system of claim 1.

4. The request understanding unit Analyze the user's emotions in real time when inputting their requests, and generate AR content that elicits positive emotions.

2. The system of claim 1.

5. The request understanding unit Generate globally compatible AR content taking into account different languages ​​and cultural backgrounds 2. The system of claim 1.

6. The request understanding unit Analyzes the user's gestures and movements and generates the AR content through intuitive operations.

2. The system of claim 1.

7. The request understanding unit Analyze the emotions of users when they input their requests and suggest AR content to alleviate negative emotions.

2. The system of claim 1.

8. The generated AI is The system recognizes hand-drawn illustrations drawn by the user with high accuracy and generates the AR content.

2. The system of claim 1.

Images