system

Abstract

The system according to this embodiment aims to generate background images that are closer to reality, based on actual 3D data and map data. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a synthesis unit. The reception unit receives background information from the user. The generation unit analyzes the information received by the reception unit and generates a background image based on actual 3D data and map data. The synthesis unit synthesizes objects commanded by the user onto the background image generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the background image generated by the image generation AI is unrealistic and it is difficult to meet the needs of users who seek reality.

[0005] The system according to the embodiment aims to generate a more realistic background image based on actual 3D data or map data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a synthesis unit. The reception unit receives background information from the user. The generation unit analyzes the information received by the reception unit and generates a background image based on actual 3D data and map data. The synthesis unit synthesizes objects commanded by the user onto the background image generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate background images that are closer to reality based on actual 3D data and map data. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The image generation system according to an embodiment of the present invention aims to make the background of images created by image generation AI more realistic by utilizing actual 3D data (terrain + buildings) and map data (land use, etc.) in order to solve the problem that images generated by current image generation AI tend to be unrealistic. The image generation system takes information about the background of the image that the user wants to generate as input. For example, if the user wants to generate an image of a specific city landscape or a specific terrain, they input information about that city or terrain. This information is input to the generation AI. Next, the generation AI analyzes the input information and generates a background image that is close to reality based on actual 3D data and map data. The generation AI analyzes terrain data and building data and outputs preliminary background outline data based on that. For example, when generating a landscape of a specific city, it reproduces the arrangement of buildings and roads based on the 3D data and map data of that city. The generated background image is then composited with an object commanded by the user via prompts. For example, if the user commands "a person sitting on a park bench," the generation AI will composite that object with the background image. This generates a background image that is closer to reality. This system can meet the needs of users seeking realism and can be used in various fields, including game developers, film and animation production teams, architecture and urban planning experts, and tourism-related companies and organizations. Furthermore, the automated process allows for the rapid and low-cost generation of realistic backgrounds, and the use of high-precision algorithms ensures consistent backgrounds. For example, game developers can use this technology to realistically recreate game backgrounds and environments. Film and animation production teams can use this technology when they need realistic landscapes for their works. Architecture and urban planning experts can use this technology when they need realistic backgrounds for simulations of buildings and urban planning. Tourism-related companies and organizations can use this technology when promoting and simulating tourist destinations. In this way, the image generation system realizes realistic background generation by fusing image generation AI with 3D data / map data, and is expected to be used in a variety of fields.This allows the image generation system to generate realistic background images based on the user's background information and to composite objects according to the user's commands.

[0029] The image generation system according to the embodiment comprises a reception unit, a generation unit, and a synthesis unit. The reception unit receives background information from the user. Background information from the user includes, but is not limited to, information about the scenery of a specific city or specific terrain. The reception unit receives, for example, text information and image information entered by the user. The reception unit can also receive location information. For example, if a user wants to generate a scenery of a specific city, they can input the name and location information of that city. The generation unit analyzes the information received by the reception unit and generates a background image based on actual 3D data and map data. The generation unit analyzes, for example, terrain data and building data and outputs preliminary background outline data based on that. For example, when generating a scenery of a specific city, it reproduces the arrangement of buildings and roads based on the 3D data and map data of that city. The generation unit can also improve the quality of the background image by having the generation AI learn based on user feedback. For example, the user can provide evaluations and comments on the generated background image, and the generation AI learns based on that feedback. The compositing unit combines the user-commanded object with the background image generated by the generation unit. For example, the compositing unit combines the object commanded by the user via prompts with the background image. For example, if the user commands "a person sitting on a park bench," the compositing unit will combine that object with the background image. The compositing unit can also estimate the user's emotions and adjust the object compositing method based on the estimated user emotions. For example, if the user is relaxed, the unit will combine the objects in a natural arrangement. As a result, the image generation system according to this embodiment can generate a background image that is close to reality based on the user's background information and combine objects according to the user's commands.

[0030] The reception desk receives background information from users. This background information may include, but is not limited to, information about the scenery of a specific city or the topography of a specific place. The reception desk also receives text and image information entered by users. Specifically, users can enter text information such as "scenery of the Eiffel Tower in Paris" or "topography of the Grand Canyon." Users can also upload photos they have taken or existing images. Furthermore, the reception desk can also accept location information. For example, if a user wants to generate scenery of a specific city, they can enter the name of that city and its location. Location information is often entered as GPS data or address information. This allows the reception desk to accurately understand the user's intent and provide appropriate data to the generation desk. The reception desk is designed to allow users to easily input information through a user interface. For example, text boxes, file upload buttons, and a map interface for inputting location information are provided. This allows users to operate intuitively and quickly provide the necessary information. Furthermore, the reception desk also has a function to check the consistency of the entered information and verify that there is no missing or incorrect information. For example, if location information is inaccurate or image files are corrupted, the system can display an appropriate error message to the user and prompt them to re-enter the information. This allows the receiving unit to provide accurate and complete information to the generating unit, improving the overall accuracy and reliability of the system.

[0031] The generation unit analyzes the information received by the reception unit and generates background images based on actual 3D data and map data. For example, the generation unit analyzes terrain data and building data and outputs preliminary background outline data based on that. Specifically, when generating a landscape of a specific city, it reproduces the arrangement of buildings and roads based on the city's 3D data and map data. For example, when generating a landscape around the Eiffel Tower in Paris, it reproduces a realistic landscape using a 3D model of the Eiffel Tower and data of surrounding buildings, roads, parks, etc. The generation unit can also improve the quality of background images by having the generation AI learn from user feedback. For example, users can provide evaluations and comments on the generated background images, and the generation AI learns from that feedback. The generation AI uses deep learning technology to improve the accuracy of image generation according to user preferences and requests. Specifically, the generation AI analyzes user feedback, identifies which parts need improvement, and reflects this in the next generation. As a result, the generation unit can provide high-quality background images that meet user requirements. Furthermore, the generation unit supports real-time image generation, instantly generating background images based on user input. This allows users to immediately review the generated results and make corrections or additional instructions as needed. By utilizing cloud-based processing, the generation unit can rapidly process large amounts of data, achieving high-speed image generation. As a result, the generation unit can generate background images efficiently and with high accuracy, increasing user satisfaction.

[0032] The compositing unit combines user-specified objects with a background image generated by the generation unit. For example, if the user prompts for an object, the compositing unit will combine that object with the background image. Specifically, if the user specifies "a person sitting on a park bench," the compositing unit will combine that object with the background image. The compositing unit adjusts the object's position, size, and angle to integrate it naturally into the background image. For example, it considers the shadow and light reflection of the person sitting on the bench to achieve a realistic composite. The compositing unit can also estimate the user's emotions and adjust the object compositing method based on the estimated emotions. For example, if the user is relaxed, it will composite objects in a natural arrangement. Specifically, it will adjust the placement and color tone of objects to create a relaxed atmosphere, considering the overall balance. The compositing unit uses AI technology to analyze the user's emotions and select the optimal compositing method. For example, if the user is stressed, the compositing unit will select objects and arrangements that have a relaxing effect to soothe the user's mood. Furthermore, the compositing unit can composite multiple objects simultaneously and generate complex scenes based on multiple user-specified commands. For example, if the user commands the creation of "a person sitting on a park bench" and "a bird flying in the sky" simultaneously, the compositing unit will appropriately position these objects to create a natural scene. The compositing unit can flexibly respond to user requests and provide realistic and appealing images. As a result, the image generation system according to this embodiment can generate realistic background images based on the user's background information and composite objects according to the user's commands.

[0033] The generation unit can analyze terrain data and building data and output preliminary background outline data based on that analysis. For example, the generation unit can analyze terrain data and generate background outline data based on elevation data and terrain models. The generation unit can also analyze building data and generate background outline data based on 3D models and location information of buildings. For example, the generation unit can recreate the layout of buildings and roads based on 3D data and map data of a specific city. By analyzing terrain data and building data, it is possible to generate background outline data that closely resembles reality. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input terrain data and building data into a generation AI and have the generation AI perform the generation of background outline data.

[0034] The generation unit can reproduce the layout of buildings and roads based on 3D data or map data of a specific city. For example, the generation unit can reproduce the layout of buildings and roads based on 3D scan data or 3D model data of a specific city. Furthermore, the generation unit can also reproduce the layout of buildings and roads based on Geographic Information System (GIS) data or map image data. For example, the generation unit can analyze 3D data of a specific city and reproduce the layout of buildings and the structure of roads. This allows for the reproduction of a realistic layout of buildings and roads based on 3D data or map data of a specific city. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input 3D data or map data of a specific city into a generation AI and have the generation AI perform the reproduction of the layout of buildings and roads.

[0035] The generation unit can improve the quality of background images by having the generating AI learn from user feedback. For example, the generation unit can receive evaluations and comments from users on the generated background images, and the generating AI can learn from this feedback. The generation unit can also analyze the user's usage history and operation logs, and the generating AI can learn from this. For example, the generation unit can improve the quality of background images by having the generating AI learn from data of background images previously generated by the user. In this way, the quality of background images can be improved by having the generating AI learn from user feedback. Some or all of the above processing in the generation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the generation unit can input user feedback data into the generating AI and have the generating AI perform learning to improve the quality of background images.

[0036] The reception desk can analyze the user's past background information input history and select the optimal input method. For example, the reception desk can automatically display background information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest background information to be used during a specific time period based on the user's past input history. In this way, the reception desk can provide the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0037] The reception unit can filter background information upon receipt based on the user's current projects and areas of interest. For example, the reception unit can prioritize displaying background information related to the user's current ongoing projects. The reception unit can also suggest highly relevant background information based on the user's areas of interest. For example, the reception unit can filter background information based on areas the user has previously shown interest in. This allows the reception unit to provide highly relevant information by filtering background information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the background information filtering.

[0038] The reception unit can prioritize receiving highly relevant information when receiving background information, taking into account the user's geographical location. For example, the reception unit can prioritize displaying background information related to the user's current location. The reception unit can also suggest highly relevant background information based on the user's geographical location. For example, the reception unit can filter background information based on places the user has visited in the past. This allows the reception unit to provide highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's location data into a generating AI and have the generating AI select highly relevant background information.

[0039] The reception unit can analyze the user's social media activity and receive relevant information when background information is received. For example, the reception unit can suggest relevant background information based on information the user has shared on social media. The reception unit can also prioritize displaying background information of interest based on the user's social media activity. For example, the reception unit can filter background information based on the accounts the user follows on social media. This allows the reception unit to provide highly relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant background information.

[0040] The generation unit can adjust the level of detail of terrain and building data when generating background images. For example, if the user requests a detailed background image, the generation unit will increase the level of detail of the terrain and building data. Conversely, if the user requests a simpler background image, the generation unit can decrease the level of detail of the terrain and building data. For example, the generation unit can dynamically adjust the level of detail of the terrain and building data according to the user's request. This allows for the generation of appropriate background images by adjusting the level of detail of the terrain and building data according to the user's request. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input terrain and building data into a generation AI and have the generation AI perform the level of detail adjustment.

[0041] The generation unit can apply different generation algorithms depending on the specific city or terrain category when generating background images. For example, when generating urban landscapes, the generation unit applies an algorithm that reproduces the building layout and road structure unique to cities. Similarly, when generating mountainous landscapes, the generation unit can apply an algorithm that reproduces the shape of mountains and vegetation. For example, when generating coastal landscapes, the generation unit applies an algorithm that reproduces wave movement and beach shape. This allows for the generation of realistic background images by applying generation algorithms tailored to specific city or terrain categories. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input specific city or terrain category data into a generation AI and have the generation AI apply an appropriate generation algorithm.

[0042] The generation unit can prioritize the use of highly relevant data when generating background images, taking into account the user's geographical location. For example, the generation unit can prioritize the use of terrain data and building data related to the user's current location. The generation unit can also suggest highly relevant data based on the user's geographical location. For example, the generation unit can generate background images based on places the user has visited in the past. This allows for the generation of realistic background images by using highly relevant data based on the user's geographical location. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's location data into a generation AI and have the generation AI select highly relevant data.

[0043] The generation unit can analyze the user's social media activity and use relevant data when generating background images. For example, the generation unit can generate relevant background images based on information shared by the user on social media. The generation unit can also prioritize the generation of background images of interest based on the user's social media activity. For example, the generation unit can generate background images based on accounts followed by the user on social media. This allows for the generation of background images that meet the user's needs by using highly relevant data based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI select the relevant data.

[0044] The compositing unit can adjust the accuracy of object compositing based on the detail level of the background image. For example, if the background image is detailed, the compositing unit will perform object compositing with high accuracy. Conversely, if the background image is simple, the compositing unit can also perform object compositing in a simplified manner. For example, the compositing unit can dynamically adjust the object compositing accuracy according to the detail level of the background image. This allows for appropriate compositing by adjusting the object compositing accuracy according to the detail level of the background image. Some or all of the above processing in the compositing unit may be performed using AI or not. For example, the compositing unit can input background image detail level data into a generating AI and have the generating AI perform the adjustment of the compositing accuracy.

[0045] The synthesis unit can apply different synthesis algorithms depending on the user's instructions during object synthesis. For example, the synthesis unit applies the optimal synthesis algorithm according to the type of object specified by the user. The synthesis unit can also select a different synthesis algorithm based on the user's instructions. For example, if the user commands multiple objects, the synthesis unit applies a synthesis algorithm suitable for each. This allows for appropriate object synthesis by applying the optimal synthesis algorithm according to the user's instructions. Some or all of the above processing in the synthesis unit may be performed using AI, or it may be performed without AI. For example, the synthesis unit can input user instruction data into a generation AI and have the generation AI select an appropriate synthesis algorithm.

[0046] The compositing unit can prioritize the compositing of highly relevant objects by considering the user's geographical location information during object compositing. For example, the compositing unit can prioritize the compositing of objects related to the user's current location. The compositing unit can also suggest highly relevant objects based on the user's geographical location information. For example, the compositing unit can composite objects based on places the user has visited in the past. This allows for the generation of realistic background images by compositing highly relevant objects based on the user's geographical location information. Some or all of the above processing in the compositing unit may be performed using AI or not. For example, the compositing unit can input the user's location information data into a generation AI and have the generation AI select highly relevant objects.

[0047] The compositing unit can analyze the user's social media activity and composite relevant objects during object compositing. For example, the compositing unit can composite relevant objects based on information shared by the user on social media. The compositing unit can also prioritize compositing objects of interest based on the user's social media activity. For example, the compositing unit can composite objects based on accounts followed by the user on social media. This allows for the generation of background images that meet the user's needs by compositing highly relevant objects based on the user's social media activity. Some or all of the above processing in the compositing unit may be performed using AI or not. For example, the compositing unit can input the user's social media data into a generation AI and have the generation AI select relevant objects.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The image generation system can further analyze the user's past generation history, and the generation unit can adjust the background image generation method based on that history. For example, it can analyze the style and theme of background images the user has generated in the past and prioritize generating background images with similar styles and themes. The generation unit can also learn the characteristics of background images the user has previously rated highly and generate background images that reflect those characteristics. For example, it can generate new background images by referring to the color tone and composition of background images that the user has previously given high ratings to. This allows the system to provide more personalized background images by utilizing the user's past generation history.

[0050] The image generation system can also receive real-time user feedback and dynamically adjust the background image generation method based on that feedback. For example, the user can provide comments and evaluations of the background image being generated in real time, and the generation unit can adjust the background image based on that feedback. The generation unit can also analyze the user feedback, and the generation AI can learn from that feedback to improve the quality of the background image. For example, if the user comments, "I want a brighter color tone," the generation unit will adjust the color tone of the background image according to that instruction. This allows the system to generate background images that reflect the user's real-time feedback.

[0051] Image generation systems can further integrate information from different data sources to generate background images. For example, the generation unit can integrate Geographic Information System (GIS) data, satellite image data, and meteorological data to produce a more realistic background image. The generation unit can also analyze information from different data sources and generate background images that reflect the characteristics of each data source. For instance, it can reproduce topographic details based on GIS data and the arrangement of vegetation and water bodies based on satellite image data. This allows for the generation of more detailed and realistic background images by utilizing multiple data sources.

[0052] The image generation system can further generate background images by considering the user's current activity status. For example, if the user is working, the generation unit can generate a background image that enhances concentration. Similarly, if the user is relaxing, the generation unit can generate a background image that promotes relaxation. For instance, if the user is using the system at night, the generation unit can generate a background image of a night view or starry sky. This allows the system to provide background images tailored to the user's current activity status.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The reception desk receives background information from the user. This background information may include information about the scenery of a specific city or specific terrain. The reception desk can accept text information, image information, and location information entered by the user. For example, if a user wants to generate a scenery of a specific city, they can enter the name and location information of that city. Step 2: The generation unit analyzes the information received by the reception unit and generates a background image based on actual 3D data and map data. The generation unit analyzes terrain data and building data and outputs a preliminary outline of the background based on that. For example, when generating a landscape of a specific city, it recreates the layout of buildings and roads based on the city's 3D data and map data. The generation unit can also improve the quality of the background image by having its generation AI learn from user feedback. Step 3: The compositing unit composites the user-specified object onto the background image generated by the generation unit. The compositing unit composites the object specified by the user in the prompt onto the background image. For example, if the user specifies "a person sitting on a park bench," the compositing unit will composite that object onto the background image. The compositing unit can also estimate the user's emotions and adjust the object compositing method based on the estimated emotions.

[0055] (Example of form 2) The image generation system according to an embodiment of the present invention aims to make the background of images created by image generation AI more realistic by utilizing actual 3D data (terrain + buildings) and map data (land use, etc.) in order to solve the problem that images generated by current image generation AI tend to be unrealistic. The image generation system takes information about the background of the image that the user wants to generate as input. For example, if the user wants to generate an image of a specific city landscape or a specific terrain, they input information about that city or terrain. This information is input to the generation AI. Next, the generation AI analyzes the input information and generates a background image that is close to reality based on actual 3D data and map data. The generation AI analyzes terrain data and building data and outputs preliminary background outline data based on that. For example, when generating a landscape of a specific city, it reproduces the arrangement of buildings and roads based on the 3D data and map data of that city. The generated background image is then composited with an object commanded by the user via prompts. For example, if the user commands "a person sitting on a park bench," the generation AI will composite that object with the background image. This generates a background image that is closer to reality. This system can meet the needs of users seeking realism and can be used in various fields, including game developers, film and animation production teams, architecture and urban planning experts, and tourism-related companies and organizations. Furthermore, the automated process allows for the rapid and low-cost generation of realistic backgrounds, and the use of high-precision algorithms ensures consistent backgrounds. For example, game developers can use this technology to realistically recreate game backgrounds and environments. Film and animation production teams can use this technology when they need realistic landscapes for their works. Architecture and urban planning experts can use this technology when they need realistic backgrounds for simulations of buildings and urban planning. Tourism-related companies and organizations can use this technology when promoting and simulating tourist destinations. In this way, the image generation system realizes realistic background generation by fusing image generation AI with 3D data / map data, and is expected to be used in a variety of fields.This allows the image generation system to generate realistic background images based on the user's background information and to composite objects according to the user's commands.

[0056] The image generation system according to the embodiment comprises a reception unit, a generation unit, and a synthesis unit. The reception unit receives background information from the user. Background information from the user includes, but is not limited to, information about the scenery of a specific city or specific terrain. The reception unit receives, for example, text information and image information entered by the user. The reception unit can also receive location information. For example, if a user wants to generate a scenery of a specific city, they can input the name and location information of that city. The generation unit analyzes the information received by the reception unit and generates a background image based on actual 3D data and map data. The generation unit analyzes, for example, terrain data and building data and outputs preliminary background outline data based on that. For example, when generating a scenery of a specific city, it reproduces the arrangement of buildings and roads based on the 3D data and map data of that city. The generation unit can also improve the quality of the background image by having the generation AI learn based on user feedback. For example, the user can provide evaluations and comments on the generated background image, and the generation AI learns based on that feedback. The compositing unit combines the user-commanded object with the background image generated by the generation unit. For example, the compositing unit combines the object commanded by the user via prompts with the background image. For example, if the user commands "a person sitting on a park bench," the compositing unit will combine that object with the background image. The compositing unit can also estimate the user's emotions and adjust the object compositing method based on the estimated user emotions. For example, if the user is relaxed, the unit will combine the objects in a natural arrangement. As a result, the image generation system according to this embodiment can generate a background image that is close to reality based on the user's background information and combine objects according to the user's commands.

[0057] The reception desk receives background information from users. This background information may include, but is not limited to, information about the scenery of a specific city or the topography of a specific place. The reception desk also receives text and image information entered by users. Specifically, users can enter text information such as "scenery of the Eiffel Tower in Paris" or "topography of the Grand Canyon." Users can also upload photos they have taken or existing images. Furthermore, the reception desk can also accept location information. For example, if a user wants to generate scenery of a specific city, they can enter the name of that city and its location. Location information is often entered as GPS data or address information. This allows the reception desk to accurately understand the user's intent and provide appropriate data to the generation desk. The reception desk is designed to allow users to easily input information through a user interface. For example, text boxes, file upload buttons, and a map interface for inputting location information are provided. This allows users to operate intuitively and quickly provide the necessary information. Furthermore, the reception desk also has a function to check the consistency of the entered information and verify that there is no missing or incorrect information. For example, if location information is inaccurate or image files are corrupted, the system can display an appropriate error message to the user and prompt them to re-enter the information. This allows the receiving unit to provide accurate and complete information to the generating unit, improving the overall accuracy and reliability of the system.

[0058] The generation unit analyzes the information received by the reception unit and generates background images based on actual 3D data and map data. For example, the generation unit analyzes terrain data and building data and outputs preliminary background outline data based on that. Specifically, when generating a landscape of a specific city, it reproduces the arrangement of buildings and roads based on the city's 3D data and map data. For example, when generating a landscape around the Eiffel Tower in Paris, it reproduces a realistic landscape using a 3D model of the Eiffel Tower and data of surrounding buildings, roads, parks, etc. The generation unit can also improve the quality of background images by having the generation AI learn from user feedback. For example, users can provide evaluations and comments on the generated background images, and the generation AI learns from that feedback. The generation AI uses deep learning technology to improve the accuracy of image generation according to user preferences and requests. Specifically, the generation AI analyzes user feedback, identifies which parts need improvement, and reflects this in the next generation. As a result, the generation unit can provide high-quality background images that meet user requirements. Furthermore, the generation unit supports real-time image generation, instantly generating background images based on user input. This allows users to immediately review the generated results and make corrections or additional instructions as needed. By utilizing cloud-based processing, the generation unit can rapidly process large amounts of data, achieving high-speed image generation. As a result, the generation unit can generate background images efficiently and with high accuracy, increasing user satisfaction.

[0059] The compositing unit combines user-specified objects with a background image generated by the generation unit. For example, if the user prompts for an object, the compositing unit will combine that object with the background image. Specifically, if the user specifies "a person sitting on a park bench," the compositing unit will combine that object with the background image. The compositing unit adjusts the object's position, size, and angle to integrate it naturally into the background image. For example, it considers the shadow and light reflection of the person sitting on the bench to achieve a realistic composite. The compositing unit can also estimate the user's emotions and adjust the object compositing method based on the estimated emotions. For example, if the user is relaxed, it will composite objects in a natural arrangement. Specifically, it will adjust the placement and color tone of objects to create a relaxed atmosphere, considering the overall balance. The compositing unit uses AI technology to analyze the user's emotions and select the optimal compositing method. For example, if the user is stressed, the compositing unit will select objects and arrangements that have a relaxing effect to soothe the user's mood. Furthermore, the compositing unit can composite multiple objects simultaneously and generate complex scenes based on multiple user-specified commands. For example, if the user commands the creation of "a person sitting on a park bench" and "a bird flying in the sky" simultaneously, the compositing unit will appropriately position these objects to create a natural scene. The compositing unit can flexibly respond to user requests and provide realistic and appealing images. As a result, the image generation system according to this embodiment can generate realistic background images based on the user's background information and composite objects according to the user's commands.

[0060] The generation unit can analyze terrain data and building data and output preliminary background outline data based on that analysis. For example, the generation unit can analyze terrain data and generate background outline data based on elevation data and terrain models. The generation unit can also analyze building data and generate background outline data based on 3D models and location information of buildings. For example, the generation unit can recreate the layout of buildings and roads based on 3D data and map data of a specific city. By analyzing terrain data and building data, it is possible to generate background outline data that closely resembles reality. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input terrain data and building data into a generation AI and have the generation AI perform the generation of background outline data.

[0061] The generation unit can reproduce the layout of buildings and roads based on 3D data or map data of a specific city. For example, the generation unit can reproduce the layout of buildings and roads based on 3D scan data or 3D model data of a specific city. Furthermore, the generation unit can also reproduce the layout of buildings and roads based on Geographic Information System (GIS) data or map image data. For example, the generation unit can analyze 3D data of a specific city and reproduce the layout of buildings and the structure of roads. This allows for the reproduction of a realistic layout of buildings and roads based on 3D data or map data of a specific city. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input 3D data or map data of a specific city into a generation AI and have the generation AI perform the reproduction of the layout of buildings and roads.

[0062] The generation unit can improve the quality of background images by having the generating AI learn from user feedback. For example, the generation unit can receive evaluations and comments from users on the generated background images, and the generating AI can learn from this feedback. The generation unit can also analyze the user's usage history and operation logs, and the generating AI can learn from this. For example, the generation unit can improve the quality of background images by having the generating AI learn from data of background images previously generated by the user. In this way, the quality of background images can be improved by having the generating AI learn from user feedback. Some or all of the above processing in the generation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the generation unit can input user feedback data into the generating AI and have the generating AI perform learning to improve the quality of background images.

[0063] The reception desk can estimate the user's emotions and adjust the input method for background information based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of background information. This improves user convenience by adjusting the input method for background information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0064] The reception desk can analyze the user's past background information input history and select the optimal input method. For example, the reception desk can automatically display background information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest background information to be used during a specific time period based on the user's past input history. In this way, the reception desk can provide the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0065] The reception unit can filter background information upon receipt based on the user's current projects and areas of interest. For example, the reception unit can prioritize displaying background information related to the user's current ongoing projects. The reception unit can also suggest highly relevant background information based on the user's areas of interest. For example, the reception unit can filter background information based on areas the user has previously shown interest in. This allows the reception unit to provide highly relevant information by filtering background information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the background information filtering.

[0066] The reception desk can estimate the user's emotions and determine the priority of background information to display based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize displaying important background information. It can also prioritize displaying detailed background information if the user is relaxed. For example, if the user is in a hurry, the reception desk will prioritize displaying background information that can be quickly reviewed. This allows for the priority of important information to be provided by prioritizing background information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The reception unit can prioritize receiving highly relevant information when receiving background information, taking into account the user's geographical location. For example, the reception unit can prioritize displaying background information related to the user's current location. The reception unit can also suggest highly relevant background information based on the user's geographical location. For example, the reception unit can filter background information based on places the user has visited in the past. This allows the reception unit to provide highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's location data into a generating AI and have the generating AI select highly relevant background information.

[0068] The reception unit can analyze the user's social media activity and receive relevant information when background information is received. For example, the reception unit can suggest relevant background information based on information the user has shared on social media. The reception unit can also prioritize displaying background information of interest based on the user's social media activity. For example, the reception unit can filter background information based on the accounts the user follows on social media. This allows the reception unit to provide highly relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant background information.

[0069] The generation unit can estimate the user's emotions and adjust the background image generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a background image that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a background image that emphasizes the shortest route. For example, if the user is excited, the generation unit can generate a background image with visually stimulating effects. This allows for the generation of background images that meet the user's needs by adjusting the background image generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the background image generation method.

[0070] The generation unit can adjust the level of detail of terrain and building data when generating background images. For example, if the user requests a detailed background image, the generation unit will increase the level of detail of the terrain and building data. Conversely, if the user requests a simpler background image, the generation unit can decrease the level of detail of the terrain and building data. For example, the generation unit can dynamically adjust the level of detail of the terrain and building data according to the user's request. This allows for the generation of appropriate background images by adjusting the level of detail of the terrain and building data according to the user's request. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input terrain and building data into a generation AI and have the generation AI perform the level of detail adjustment.

[0071] The generation unit can apply different generation algorithms depending on the specific city or terrain category when generating background images. For example, when generating urban landscapes, the generation unit applies an algorithm that reproduces the building layout and road structure unique to cities. Similarly, when generating mountainous landscapes, the generation unit can apply an algorithm that reproduces the shape of mountains and vegetation. For example, when generating coastal landscapes, the generation unit applies an algorithm that reproduces wave movement and beach shape. This allows for the generation of realistic background images by applying generation algorithms tailored to specific city or terrain categories. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input specific city or terrain category data into a generation AI and have the generation AI apply an appropriate generation algorithm.

[0072] The generation unit can estimate the user's emotions and determine the priority of background images to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating relaxing scenery. It can also prioritize generating visually stimulating scenery if the user is excited. For example, if the user is in a hurry, the generation unit will prioritize generating scenery that can be generated quickly. This allows for the prioritization of background images according to the user's emotions, thereby prioritizing the generation of background images that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of background images.

[0073] The generation unit can prioritize the use of highly relevant data when generating background images, taking into account the user's geographical location. For example, the generation unit can prioritize the use of terrain data and building data related to the user's current location. The generation unit can also suggest highly relevant data based on the user's geographical location. For example, the generation unit can generate background images based on places the user has visited in the past. This allows for the generation of realistic background images by using highly relevant data based on the user's geographical location. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's location data into a generation AI and have the generation AI select highly relevant data.

[0074] The generation unit can analyze the user's social media activity and use relevant data when generating background images. For example, the generation unit can generate relevant background images based on information shared by the user on social media. The generation unit can also prioritize the generation of background images of interest based on the user's social media activity. For example, the generation unit can generate background images based on accounts followed by the user on social media. This allows for the generation of background images that meet the user's needs by using highly relevant data based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI select the relevant data.

[0075] The synthesis unit can estimate the user's emotions and adjust the object synthesis method based on the estimated emotions. For example, if the user is relaxed, the synthesis unit will synthesize objects in a natural arrangement. Conversely, if the user is in a hurry, the synthesis unit can apply a method that allows for rapid synthesis. For example, if the user is excited, the synthesis unit will synthesize objects in a visually stimulating arrangement. This allows for the synthesis of objects that meet the user's needs by adjusting the object synthesis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the synthesis unit may be performed using AI or not. For example, the synthesis unit can input user emotion data into a generative AI and have the generative AI adjust the object synthesis method.

[0076] The compositing unit can adjust the accuracy of object compositing based on the detail level of the background image. For example, if the background image is detailed, the compositing unit will perform object compositing with high accuracy. Conversely, if the background image is simple, the compositing unit can also perform object compositing in a simplified manner. For example, the compositing unit can dynamically adjust the object compositing accuracy according to the detail level of the background image. This allows for appropriate compositing by adjusting the object compositing accuracy according to the detail level of the background image. Some or all of the above processing in the compositing unit may be performed using AI or not. For example, the compositing unit can input background image detail level data into a generating AI and have the generating AI perform the adjustment of the compositing accuracy.

[0077] The synthesis unit can apply different synthesis algorithms depending on the user's instructions during object synthesis. For example, the synthesis unit applies the optimal synthesis algorithm according to the type of object specified by the user. The synthesis unit can also select a different synthesis algorithm based on the user's instructions. For example, if the user commands multiple objects, the synthesis unit applies a synthesis algorithm suitable for each. This allows for appropriate object synthesis by applying the optimal synthesis algorithm according to the user's instructions. Some or all of the above processing in the synthesis unit may be performed using AI, or it may be performed without AI. For example, the synthesis unit can input user instruction data into a generation AI and have the generation AI select an appropriate synthesis algorithm.

[0078] The synthesis unit can estimate the user's emotions and determine the priority of objects to synthesize based on the estimated emotions. For example, if the user is stressed, the synthesis unit will prioritize synthesizing important objects. Conversely, if the user is relaxed, the synthesis unit can prioritize synthesizing detailed objects. For example, if the user is in a hurry, the synthesis unit will prioritize synthesizing objects that can be synthesized quickly. This allows for the prioritization of important objects by determining object priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the synthesis unit may be performed using AI or not. For example, the synthesis unit can input user emotion data into a generative AI and have the generative AI determine object priorities.

[0079] The compositing unit can prioritize the compositing of highly relevant objects by considering the user's geographical location information during object compositing. For example, the compositing unit can prioritize the compositing of objects related to the user's current location. The compositing unit can also suggest highly relevant objects based on the user's geographical location information. For example, the compositing unit can composite objects based on places the user has visited in the past. This allows for the generation of realistic background images by compositing highly relevant objects based on the user's geographical location information. Some or all of the above processing in the compositing unit may be performed using AI or not. For example, the compositing unit can input the user's location information data into a generation AI and have the generation AI select highly relevant objects.

[0080] The compositing unit can analyze the user's social media activity and composite relevant objects during object compositing. For example, the compositing unit can composite relevant objects based on information shared by the user on social media. The compositing unit can also prioritize compositing objects of interest based on the user's social media activity. For example, the compositing unit can composite objects based on accounts followed by the user on social media. This allows for the generation of background images that meet the user's needs by compositing highly relevant objects based on the user's social media activity. Some or all of the above processing in the compositing unit may be performed using AI or not. For example, the compositing unit can input the user's social media data into a generation AI and have the generation AI select relevant objects.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The image generation system can further analyze the user's past generation history, and the generation unit can adjust the background image generation method based on that history. For example, it can analyze the style and theme of background images the user has generated in the past and prioritize generating background images with similar styles and themes. The generation unit can also learn the characteristics of background images the user has previously rated highly and generate background images that reflect those characteristics. For example, it can generate new background images by referring to the color tone and composition of background images that the user has previously given high ratings to. This allows the system to provide more personalized background images by utilizing the user's past generation history.

[0083] The image generation system can also receive real-time user feedback and dynamically adjust the background image generation method based on that feedback. For example, the user can provide comments and evaluations of the background image being generated in real time, and the generation unit can adjust the background image based on that feedback. The generation unit can also analyze the user feedback, and the generation AI can learn from that feedback to improve the quality of the background image. For example, if the user comments, "I want a brighter color tone," the generation unit will adjust the color tone of the background image according to that instruction. This allows the system to generate background images that reflect the user's real-time feedback.

[0084] Image generation systems can further integrate information from different data sources to generate background images. For example, the generation unit can integrate Geographic Information System (GIS) data, satellite image data, and meteorological data to produce a more realistic background image. The generation unit can also analyze information from different data sources and generate background images that reflect the characteristics of each data source. For instance, it can reproduce topographic details based on GIS data and the arrangement of vegetation and water bodies based on satellite image data. This allows for the generation of more detailed and realistic background images by utilizing multiple data sources.

[0085] The image generation system can further estimate the user's emotions and adjust the color tone and atmosphere of the background image based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a background image with calm colors and soft lighting effects. Conversely, if the user is excited, the generation unit can generate a background image with vibrant colors and dynamic effects. For example, if the user is stressed, the generation unit will prioritize generating relaxing natural landscapes. This allows the system to provide background images that match the user's emotions.

[0086] The image generation system can further generate background images by considering the user's current activity status. For example, if the user is working, the generation unit can generate a background image that enhances concentration. Similarly, if the user is relaxing, the generation unit can generate a background image that promotes relaxation. For instance, if the user is using the system at night, the generation unit can generate a background image of a night view or starry sky. This allows the system to provide background images tailored to the user's current activity status.

[0087] The image generation system can further estimate the user's emotions and adjust the background image generation speed based on the estimated emotions. For example, if the user is in a hurry, the generation unit will set a lower level of detail to generate a background image quickly. Conversely, if the user is relaxed, the generation unit can take its time to generate a highly detailed background image. For example, if the user is stressed, the generation unit will provide a simple background image that can be generated quickly. This allows the system to provide background images at a generation speed that matches the user's emotions.

[0088] The image generation system can further estimate the user's emotions and adjust the composition of the background image based on those emotions. For example, if the user is relaxed, the generation unit can generate a background image with a broad, open composition. If the user is focused, the generation unit can also generate a background image with a simple, focused composition. For example, if the user is excited, the generation unit can generate a background image with a dynamic composition. This allows the system to provide background images with compositions that match the user's emotions.

[0089] The image generation system can further estimate the user's emotions and adjust the background image effects based on those emotions. For example, if the user is relaxed, the generation unit can generate a background image with soft lighting effects and calming colors. If the user is excited, the generation unit can also generate a background image with vibrant colors and dynamic effects. For example, if the user is stressed, the generation unit can generate a background image with relaxing effects. This allows the system to provide background images with effects that match the user's emotions.

[0090] The image generation system can further estimate the user's emotions and select a background image theme based on those emotions. For example, if the user is relaxed, the generation unit can generate background images with themes of natural landscapes or calm seas. If the user is excited, the generation unit can generate background images with themes of city nightscapes or sporting events. For example, if the user is stressed, the generation unit can generate background images with relaxing themes. This allows the system to provide background images with themes that match the user's emotions.

[0091] The image generation system can further estimate the user's emotions and adjust the detail of the background image based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a background image with meticulous detail. Conversely, if the user is in a hurry, the generation unit can generate a simplified background image. For example, if the user is stressed, the generation unit will generate a simple background image that is less visually burdensome. This allows the system to provide background images with details that match the user's emotions.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The reception desk receives background information from the user. This background information may include information about the scenery of a specific city or specific terrain. The reception desk can accept text information, image information, and location information entered by the user. For example, if a user wants to generate a scenery of a specific city, they can enter the name and location information of that city. Step 2: The generation unit analyzes the information received by the reception unit and generates a background image based on actual 3D data and map data. The generation unit analyzes terrain data and building data and outputs a preliminary outline of the background based on that. For example, when generating a landscape of a specific city, it recreates the layout of buildings and roads based on the city's 3D data and map data. The generation unit can also improve the quality of the background image by having its generation AI learn from user feedback. Step 3: The compositing unit composites the user-specified object onto the background image generated by the generation unit. The compositing unit composites the object specified by the user in the prompt onto the background image. For example, if the user specifies "a person sitting on a park bench," the compositing unit will composite that object onto the background image. The compositing unit can also estimate the user's emotions and adjust the object compositing method based on the estimated emotions.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0096] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] Each of the multiple elements, including the reception unit, generation unit, and synthesis unit described above, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives background information from the user. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a background image based on actual 3D data or map data. The synthesis unit is implemented by the control unit 46A of the smart device 14 and synthesizes objects commanded by the user onto the generated background image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0104] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0105] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0106] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0107] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0108] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements, including the reception unit, generation unit, and synthesis unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives background information from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a background image based on actual 3D data or map data. The synthesis unit is implemented by the control unit 46A of the smart glasses 214 and synthesizes objects commanded by the user onto the generated background image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements, including the reception unit, generation unit, and synthesis unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives background information from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a background image based on actual 3D data or map data. The synthesis unit is implemented by the control unit 46A of the headset terminal 314 and synthesizes objects commanded by the user onto the generated background image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0138] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements, including the reception unit, generation unit, and synthesis unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives background information from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a background image based on actual 3D data or map data. The synthesis unit is implemented by the control unit 46A of the robot 414 and synthesizes objects commanded by the user onto the generated background image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0149] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0150] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0151] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0155] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0156] 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.

[0157] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0158] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0159] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0160] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0162] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0163] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0164] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0165] (Note 1) A reception desk that receives background information from users, A generation unit analyzes the information received by the reception unit and generates a background image based on actual 3D data and map data, The system includes a compositing unit that combines objects commanded by the user with the background image generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Analyze terrain and building data, and then output a preliminary background outline based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Recreate the layout of buildings and roads based on 3D data and map data of a specific city. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The generation AI learns from user feedback and improves the quality of background images. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for background information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past background information input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When background information is received, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of background information to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving background information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When background information is received, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the background image generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is Adjust the level of detail in terrain and building data when generating background images. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating background images, different generation algorithms are applied depending on the specific city or terrain category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and determines the priority of background images to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating background images, the system prioritizes the use of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating background images, we analyze the user's social media activity and use relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned synthesis section is It estimates the user's emotions and adjusts how objects are combined based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned synthesis section is When compositing objects, adjust the compositing accuracy based on the detail level of the background image. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned synthesis section is When composing objects, different composing algorithms are applied depending on the user's instructions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned synthesis section is It estimates the user's emotions and determines the priority of objects to synthesize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned synthesis section is When compositing objects, the system prioritizes compositing highly relevant objects by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned synthesis section is During object compositing, the system analyzes the user's social media activity and composites relevant objects. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives background information from users, A generation unit analyzes the information received by the reception unit and generates a background image based on actual 3D data and map data, The system includes a compositing unit that combines objects commanded by the user with the background image generated by the generation unit. A system characterized by the following features.

2. The generating unit is Analyze terrain and building data, and then output a preliminary background outline based on that analysis. The system according to feature 1.

3. The generating unit is Recreate the layout of buildings and roads based on 3D data and map data of a specific city. The system according to feature 1.

4. The generating unit is The generation AI learns from user feedback and improves the quality of background images. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for background information based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past background information input history and select the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When background information is received, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of background information to accept based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving background information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When background information is received, the system analyzes the user's social media activity and collects relevant information. The system according to feature 1.

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