System

Abstract

An object of the system according to the embodiment is to allow a user to easily reflect his / her idea in reality.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a projection unit. The receiving unit inputs a design. The analysis unit analyzes the design input by the reception unit. The generation unit generates a 3D model based on the design analyzed by the analysis unit. The projector projects the 3D model generated by the generator onto the reality space.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for users to easily realize their ideas in reality, as creating and projecting VR / AR content is complicated.

[0005] The system according to the embodiment aims to enable users to easily put their own ideas into practice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a projection unit. The reception unit inputs a design. The analysis unit analyzes the design input by the reception unit. The generation unit generates a 3D model based on the design analyzed by the analysis unit. The projection unit projects the 3D model generated by the generation unit into real space. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily put their own ideas into practice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention allows users to easily create and project VR / AR content using a generative AI. In this system, a user inputs a design, and the generative AI analyzes the design and generates a 3D model. The generated 3D model is then projected into real space using AR technology, allowing users to view the actual placement of the object. This system is also available for both creators and general users, allowing creators to efficiently bring their ideas to life. For example, a character designer can use a generative AI to create a 3D model of the character and view it in a VR space. Furthermore, general users can easily create VR / AR content and share it with other users. For example, if a user wants to change the layout of their room, they can use a generative AI to simulate a new furniture arrangement and share the results with their friends. This streamlines the production process and enables creators and general users to share virtual experiences. The system allows users to input a design, and the generative AI analyzes the design, generates a 3D model, and projects it into real space. For example, general users can experience VR content created by creators, allowing the creator's ideas to be widely shared and general users to enjoy new experiences.

[0029] The VR / AR content generation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a projection unit. The reception unit receives a design from a user. Examples of designs input by the user include, but are not limited to, graphic design, product design, and architectural design. The reception unit allows the user to input the design in text format. The reception unit also allows the user to input the design in image format. The reception unit also allows the user to input the design in voice format. For example, the reception unit can convert the user's voice input into text data using voice recognition technology. The analysis unit uses a generation AI to analyze the design input by the reception unit. The analysis can be performed using methods such as, but not limited to, shape analysis, functional analysis, and structural analysis. For example, the analysis unit allows the generation AI to analyze the shape of the design and extract information necessary for generating a 3D model. The analysis unit can also allow the generation AI to analyze the function of the design and extract information related to the operation of the 3D model. The analysis unit can also allow the generation AI to analyze the structure of the design and extract information necessary for constructing the 3D model. The generation unit uses a generation AI to generate a 3D model based on the design analyzed by the analysis unit. The 3D model may be generated in the form of, for example, a polygon model, a NURBS model, a voxel model, or the like, but is not limited to these examples. For example, the generation unit generates a polygon model using the generation AI. The generation unit can also generate a NURBS model using the generation AI. The generation unit can also generate a voxel model using the generation AI. The projection unit projects the 3D model generated by the generation unit into real space. The projection is performed using, for example, augmented reality (AR), virtual reality (VR), projection mapping, or other technologies, but is not limited to these examples. For example, the projection unit projects the 3D model into real space using AR technology. The projection unit can also project the 3D model into virtual space using VR technology. The projection unit can also project the 3D model onto a physical surface using projection mapping technology.As a result, the VR / AR content generation system according to the embodiment can consistently perform everything from design input to analysis, generation of a 3D model, and projection into real space.

[0030] The reception unit can analyze the user's past design input history and provide an optimal input interface. For example, the reception unit can prioritize and display design tools that the user has frequently used in the past. The reception unit can also automatically display frequently used design elements as candidates based on the user's past input history. The reception unit can also analyze the user's past design patterns and suggest optimal templates. This makes it possible to provide an optimal input interface based on the user's past design history. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's past design data into a generation AI and have the generation AI select an optimal input interface.

[0031] When inputting a design, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can prioritize displaying design elements related to the user's current project. The reception unit can also suggest related design templates based on the user's area of ​​interest. The reception unit can also filter optimal design elements by referring to the user's past project history. This makes it possible to provide optimal design elements based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI select optimal design elements.

[0032] When inputting a design, the reception unit can select an input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the design using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide a text editor and input the design. Furthermore, if the user selects image input, the reception unit can input the design using image analysis technology. This makes it possible to provide the optimal design input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0033] When inputting a design, the reception unit can prioritize inputting highly relevant designs by taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize displaying design elements related to that region. The reception unit can also suggest region-specific design templates based on the user's location information. The reception unit can also filter optimal design elements by taking into account the user's location information. This makes it possible to provide optimal design elements based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select optimal design elements.

[0034] When inputting a design, the reception unit can analyze the user's social media activity and input related designs. For example, the reception unit can suggest related design elements based on designs the user has shared on social media. The reception unit can also analyze the user's social media activity and suggest design templates that the user may be interested in. The reception unit can also suggest related designs based on the activity of the user's friends on social media. This makes it possible to provide optimal design elements based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select related design elements.

[0035] The reception unit can customize the input method by reflecting the user's past feedback when inputting a design. The reception unit can provide an optimal input interface based on, for example, feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and optimize the input procedure. The reception unit can also provide customization options for design input by reflecting the user's feedback. This makes it possible to provide an optimal design input method based on the user's past feedback. Some or all of the above-described processing by the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's feedback data to a generation AI and cause the generation AI to select an optimal input interface.

[0036] When analyzing a design, the analysis unit can adjust the level of detail of the analysis based on the importance of the design. For example, the analysis unit performs a detailed analysis on important design elements. The analysis unit can also perform a simplified analysis on less important design elements. The analysis unit can also adjust the level of detail of the analysis based on the importance of the entire design. This makes it possible to provide an optimal analysis method according to the importance of the design. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input design importance data into a generation AI and have the generation AI select the optimal analysis method.

[0037] When analyzing a design, the analysis unit can apply different analysis algorithms depending on the category of the design. For example, the analysis unit can apply a specific analysis algorithm to a character design. The analysis unit can also apply a different analysis algorithm to a furniture design. The analysis unit can also select the optimal analysis algorithm depending on the design category. This makes it possible to provide the optimal analysis algorithm depending on the design category. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input design category data to the generation AI and have the generation AI select the optimal analysis algorithm.

[0038] When analyzing a design, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] When analyzing a design, the analysis unit can determine the analysis priority based on the submission date of the design. For example, the analysis unit can prioritize analysis of designs submitted early. The analysis unit can also prioritize analysis of designs with an approaching deadline. The analysis unit can also adjust the analysis priority based on the submission date. This makes it possible to provide optimal analysis priorities based on the submission date of the design. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input design submission date data into the generation AI and have the generation AI determine the optimal analysis priority.

[0040] When analyzing designs, the analysis unit can adjust the order of analysis based on the relevance of the designs. For example, the analysis unit prioritizes analysis of highly relevant designs. The analysis unit can also postpone analysis of less relevant designs. The analysis unit can also adjust the order of analysis based on the relevance of the designs. This makes it possible to provide an optimal analysis order based on the relevance of the designs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input design relevance data to a generation AI and have the generation AI determine the optimal analysis order.

[0041] When analyzing a design, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to a user with high expertise. The analysis unit can also provide analysis results in simple language to a user with low expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the optimal way of expressing the analysis results.

[0042] When generating a 3D model, the generation unit can adjust the level of detail of the generation based on the importance of the design. For example, the generation unit generates a detailed 3D model for an important design element. The generation unit can also generate a simplified 3D model for a less important design element. The generation unit can also adjust the level of detail of the generation based on the importance of the entire design. This makes it possible to provide an optimal 3D model generation method according to the importance of the design. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design importance data into the generation AI and have the generation AI select the optimal generation method.

[0043] When generating a 3D model, the generation unit can apply different generation algorithms depending on the design category. For example, the generation unit applies a specific generation algorithm for a character design. The generation unit can also apply a different generation algorithm for a furniture design. The generation unit can also select the optimal generation algorithm depending on the design category. This makes it possible to provide the optimal 3D model generation algorithm depending on the design category. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input design category data into the generation AI and cause the generation AI to select the optimal generation algorithm.

[0044] When generating a 3D model, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, optimizes the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. The generation unit can also analyze the user's past generation results and identify areas for improvement in generation. This allows the accuracy of generation to be improved based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0045] When generating a 3D model, the generation unit can determine a generation priority based on the submission date of the design. For example, the generation unit can prioritize the generation of designs submitted early. The generation unit can also prioritize the generation of designs with an approaching deadline. The generation unit can also adjust the generation priority based on the submission date. This allows for optimal 3D model generation priority to be provided based on the submission date of the design. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design submission date data into the generation AI and have the generation AI determine the optimal generation priority.

[0046] When generating a 3D model, the generation unit can adjust the generation order based on the relevance of the designs. For example, the generation unit prioritizes the generation of highly relevant designs. The generation unit can also postpone the generation of less relevant designs. The generation unit can also adjust the generation order based on the relevance of the designs. This makes it possible to provide an optimal generation order based on the relevance of the designs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design relevance data into the generation AI and have the generation AI determine the optimal generation order.

[0047] When generating a 3D model, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit can provide a generated result using detailed technical terminology to a user with high level of expertise. The generation unit can also provide a generated result in simple language to a user with low level of expertise. The generation unit can also adjust the expression method of the generated result according to the user's level of expertise. This makes it possible to provide an optimal 3D model generation result according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the optimal expression method of the generated result.

[0048] The projection unit can adjust the level of detail of the projection based on the importance of the 3D model during projection. For example, the projection unit performs detailed projection on important 3D models. The projection unit can also perform simplified projection on less important 3D models. The projection unit can also adjust the level of detail of the projection based on the importance of the entire 3D model. This makes it possible to provide an optimal projection method according to the importance of the 3D model. Some or all of the above-described processing in the projection unit may be performed using, or without, AI. For example, the projection unit can input importance data of the 3D model to the generation AI and cause the generation AI to select the optimal projection method.

[0049] The projection unit can apply different projection algorithms depending on the category of the 3D model during projection. For example, the projection unit can apply a specific projection algorithm for character design. The projection unit can also apply a different projection algorithm for furniture design. The projection unit can also select the optimal projection algorithm depending on the category of the 3D model. This makes it possible to provide the optimal projection algorithm depending on the category of the 3D model. Some or all of the above-described processing in the projection unit can be performed using AI, for example, or without AI. For example, the projection unit can input category data of the 3D model to the generation AI and cause the generation AI to select the optimal projection algorithm.

[0050] During projection, the projection unit can improve the accuracy of projection by referring to the user's past projection results. The projection unit, for example, optimizes the projection algorithm based on the user's past projection results. The projection unit can also improve the accuracy of projection by referring to the user's past projection results. The projection unit can also analyze the user's past projection results and identify areas for improvement in the projection. This makes it possible to improve the accuracy of projection based on the user's past projection results. Some or all of the above-described processing in the projection unit may be performed using, for example, AI, or may be performed without using AI. For example, the projection unit can input the user's past projection data into the generation AI and cause the generation AI to improve the accuracy of the projection.

[0051] During projection, the projection unit can determine the projection priority based on the submission date of the 3D model. For example, the projection unit can prioritize the projection of 3D models submitted early. The projection unit can also prioritize the projection of 3D models whose deadlines are approaching. The projection unit can also adjust the projection priority based on the submission date. This allows for optimal projection priority to be provided based on the submission date of the 3D model. Some or all of the above-described processing in the projection unit can be performed using, for example, AI, or can be performed without using AI. For example, the projection unit can input data on the submission date of the 3D model to the generation AI and have the generation AI determine the optimal projection priority.

[0052] The projection unit can adjust the order of projection based on the relevance of the 3D models during projection. For example, the projection unit prioritizes the projection of highly relevant 3D models. The projection unit can also postpone the projection of less relevant 3D models. The projection unit can also adjust the order of projection based on the relevance of the 3D models. This makes it possible to provide an optimal projection order based on the relevance of the 3D models. Some or all of the above-described processing in the projection unit may be performed using, or without, AI. For example, the projection unit can input relevance data of the 3D models to the generation AI and cause the generation AI to determine the optimal projection order.

[0053] During projection, the projection unit can adjust the use of technical terminology in the projection according to the user's level of expertise. For example, the projection unit can provide projection results using detailed technical terminology to a user with high expertise. The projection unit can also provide projection results in simple language to a user with low expertise. The projection unit can also adjust the way the projection results are expressed according to the user's level of expertise. This makes it possible to provide optimal projection results according to the user's level of expertise. Some or all of the above-described processing in the projection unit can be performed using, for example, AI, or can be performed without using AI. For example, the projection unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the optimal way of expressing the projection results.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When the user inputs a design, the reception unit can analyze the success rate of the user's past projects and prioritize displaying design elements with a high success rate. For example, design elements used in the user's past successful projects can be automatically displayed as candidates. The reception unit can also analyze the causes of failure in the user's past projects and provide advice on how to avoid similar failures. Furthermore, the reception unit can suggest optimal design templates based on feedback from the user's past projects. This makes it possible to provide the optimal design input method based on the success rate of the user's past projects.

[0056] When the user inputs a design, the reception unit can analyze the user's design style and provide an input interface that matches that style. For example, if the user prefers minimalist designs, a simple interface can be provided. Alternatively, if the user prefers complex designs, detailed input options can be provided. Furthermore, the reception unit can suggest an optimal design template based on the user's design style. This makes it possible to provide an optimal input interface that matches the user's design style.

[0057] When a user inputs a design, the reception unit can customize the input content based on the user's design purpose. For example, if the user is creating a commercial design, the reception unit can provide input options specialized for commercial designs. Also, if the user is creating a personal design, the reception unit can provide input options suitable for personal designs. Furthermore, the reception unit can suggest an optimal design template based on the user's design purpose. This makes it possible to provide optimal input content according to the user's design purpose.

[0058] When analyzing a design, the analysis unit can adjust the level of detail of the analysis based on the user's design purpose. For example, for a commercial design, a detailed analysis can be performed to emphasize commercial elements. For a personal design, a simplified analysis can be performed to emphasize personal elements. Furthermore, the analysis unit can select the optimal analysis algorithm based on the design purpose. This makes it possible to provide the optimal analysis method according to the design purpose.

[0059] When analyzing a design, the analysis unit can select an analysis algorithm based on the complexity of the user's design. For example, for a complex design, a detailed analysis algorithm can be applied. For a simple design, a simplified analysis algorithm can be applied. Furthermore, the analysis unit can select the optimal analysis algorithm based on the complexity of the design. This makes it possible to provide the optimal analysis method according to the complexity of the design.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives a design from the user. The designs input by the user include graphic designs, product designs, architectural designs, etc. The reception unit allows the user to input the design in text format, image format, or voice format. In the case of voice input, the voice is converted into text data using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the design entered by the reception unit. The analysis is performed using methods such as shape analysis, function analysis, and structure analysis. The analysis unit analyzes the shape, function, and structure of the design and extracts the information necessary to generate a 3D model. Step 3: The generator uses the AI ​​to generate a 3D model based on the design analyzed by the analyzer. The 3D model is generated in the form of a polygon model, NURBS model, voxel model, etc. Step 4: The projection unit projects the 3D model generated by the generation unit into real space. The projection is performed using technologies such as AR (augmented reality), VR (virtual reality), and projection mapping.

[0062] (Example 2) A system according to an embodiment of the present invention allows users to easily create and project VR / AR content using a generative AI. In this system, a user inputs a design, and the generative AI analyzes the design and generates a 3D model. The generated 3D model is then projected into real space using AR technology, allowing users to view the actual placement of the object. This system is also available for both creators and general users, allowing creators to efficiently bring their ideas to life. For example, a character designer can use a generative AI to create a 3D model of the character and view it in a VR space. Furthermore, general users can easily create VR / AR content and share it with other users. For example, if a user wants to change the layout of their room, they can use a generative AI to simulate a new furniture arrangement and share the results with their friends. This streamlines the production process and enables creators and general users to share virtual experiences. The system allows users to input a design, and the generative AI analyzes the design, generates a 3D model, and projects it into real space. For example, general users can experience VR content created by creators, allowing the creator's ideas to be widely shared and general users to enjoy new experiences.

[0063] The VR / AR content generation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a projection unit. The reception unit receives a design from a user. Examples of designs input by the user include, but are not limited to, graphic design, product design, and architectural design. The reception unit allows the user to input the design in text format. The reception unit also allows the user to input the design in image format. The reception unit also allows the user to input the design in voice format. For example, the reception unit can convert the user's voice input into text data using voice recognition technology. The analysis unit uses a generation AI to analyze the design input by the reception unit. The analysis can be performed using methods such as, but not limited to, shape analysis, functional analysis, and structural analysis. For example, the analysis unit allows the generation AI to analyze the shape of the design and extract information necessary for generating a 3D model. The analysis unit can also allow the generation AI to analyze the function of the design and extract information related to the operation of the 3D model. The analysis unit can also allow the generation AI to analyze the structure of the design and extract information necessary for constructing the 3D model. The generation unit uses a generation AI to generate a 3D model based on the design analyzed by the analysis unit. The 3D model may be generated in the form of, for example, a polygon model, a NURBS model, a voxel model, or the like, but is not limited to these examples. For example, the generation unit generates a polygon model using the generation AI. The generation unit can also generate a NURBS model using the generation AI. The generation unit can also generate a voxel model using the generation AI. The projection unit projects the 3D model generated by the generation unit into real space. The projection is performed using, for example, augmented reality (AR), virtual reality (VR), projection mapping, or other technologies, but is not limited to these examples. For example, the projection unit projects the 3D model into real space using AR technology. The projection unit can also project the 3D model into virtual space using VR technology. The projection unit can also project the 3D model onto a physical surface using projection mapping technology.As a result, the VR / AR content generation system according to the embodiment can consistently perform everything from design input to analysis, generation of a 3D model, and projection into real space.

[0064] The reception unit can estimate the user's emotions and adjust the design input method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick design input. This allows for the provision of an optimal design input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the reception unit can be performed using AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0065] The reception unit can analyze the user's past design input history and provide an optimal input interface. For example, the reception unit can prioritize and display design tools that the user has frequently used in the past. The reception unit can also automatically display frequently used design elements as candidates based on the user's past input history. The reception unit can also analyze the user's past design patterns and suggest optimal templates. This makes it possible to provide an optimal input interface based on the user's past design history. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's past design data into a generation AI and have the generation AI select an optimal input interface.

[0066] When inputting a design, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can prioritize displaying design elements related to the user's current project. The reception unit can also suggest related design templates based on the user's area of ​​interest. The reception unit can also filter optimal design elements by referring to the user's past project history. This makes it possible to provide optimal design elements based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI select optimal design elements.

[0067] When inputting a design, the reception unit can select an input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the design using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide a text editor and input the design. Furthermore, if the user selects image input, the reception unit can input the design using image analysis technology. This makes it possible to provide the optimal design input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0068] The reception unit can estimate the user's emotions and determine the priority of input designs based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize displaying important design elements. Furthermore, if the user is relaxed, the reception unit can also provide detailed design options. Furthermore, if the user is stressed, the reception unit can prioritize displaying simple design elements. This allows design priorities to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0069] When inputting a design, the reception unit can prioritize inputting highly relevant designs by taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize displaying design elements related to that region. The reception unit can also suggest region-specific design templates based on the user's location information. The reception unit can also filter optimal design elements by taking into account the user's location information. This makes it possible to provide optimal design elements based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select optimal design elements.

[0070] When inputting a design, the reception unit can analyze the user's social media activity and input related designs. For example, the reception unit can suggest related design elements based on designs the user has shared on social media. The reception unit can also analyze the user's social media activity and suggest design templates that the user may be interested in. The reception unit can also suggest related designs based on the activity of the user's friends on social media. This makes it possible to provide optimal design elements based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select related design elements.

[0071] The reception unit can customize the input method by reflecting the user's past feedback when inputting a design. The reception unit can provide an optimal input interface based on, for example, feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and optimize the input procedure. The reception unit can also provide customization options for design input by reflecting the user's feedback. This makes it possible to provide an optimal design input method based on the user's past feedback. Some or all of the above-described processing by the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's feedback data to a generation AI and cause the generation AI to select an optimal input interface.

[0072] The analysis unit can estimate the user's emotions and adjust the design analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis, analyzing every detail of the design. If the user is in a hurry, the analysis unit can analyze only the important parts and provide quick results. If the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for an optimal design analysis method to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0073] When analyzing a design, the analysis unit can adjust the level of detail of the analysis based on the importance of the design. For example, the analysis unit performs a detailed analysis on important design elements. The analysis unit can also perform a simplified analysis on less important design elements. The analysis unit can also adjust the level of detail of the analysis based on the importance of the entire design. This makes it possible to provide an optimal analysis method according to the importance of the design. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input design importance data into a generation AI and have the generation AI select the optimal analysis method.

[0074] When analyzing a design, the analysis unit can apply different analysis algorithms depending on the category of the design. For example, the analysis unit can apply a specific analysis algorithm to a character design. The analysis unit can also apply a different analysis algorithm to a furniture design. The analysis unit can also select the optimal analysis algorithm depending on the design category. This makes it possible to provide the optimal analysis algorithm depending on the design category. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input design category data to the generation AI and have the generation AI select the optimal analysis algorithm.

[0075] When analyzing a design, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide analysis results in a short time. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide results over a long period of time. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for an optimal analysis time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0077] When analyzing a design, the analysis unit can determine the analysis priority based on the submission date of the design. For example, the analysis unit can prioritize analysis of designs submitted early. The analysis unit can also prioritize analysis of designs with an approaching deadline. The analysis unit can also adjust the analysis priority based on the submission date. This makes it possible to provide optimal analysis priorities based on the submission date of the design. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input design submission date data into the generation AI and have the generation AI determine the optimal analysis priority.

[0078] When analyzing designs, the analysis unit can adjust the order of analysis based on the relevance of the designs. For example, the analysis unit prioritizes analysis of highly relevant designs. The analysis unit can also postpone analysis of less relevant designs. The analysis unit can also adjust the order of analysis based on the relevance of the designs. This makes it possible to provide an optimal analysis order based on the relevance of the designs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input design relevance data to a generation AI and have the generation AI determine the optimal analysis order.

[0079] When analyzing a design, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to a user with high expertise. The analysis unit can also provide analysis results in simple language to a user with low expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the optimal way of expressing the analysis results.

[0080] The generation unit can estimate the user's emotions and adjust the 3D model generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a 3D model that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a 3D model that can be generated in the shortest time. Furthermore, if the user is excited, the generation unit can generate a 3D model that adds visually stimulating effects. This makes it possible to provide an optimal 3D model generation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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 processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0081] When generating a 3D model, the generation unit can adjust the level of detail of the generation based on the importance of the design. For example, the generation unit generates a detailed 3D model for an important design element. The generation unit can also generate a simplified 3D model for a less important design element. The generation unit can also adjust the level of detail of the generation based on the importance of the entire design. This makes it possible to provide an optimal 3D model generation method according to the importance of the design. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design importance data into the generation AI and have the generation AI select the optimal generation method.

[0082] When generating a 3D model, the generation unit can apply different generation algorithms depending on the design category. For example, the generation unit applies a specific generation algorithm for a character design. The generation unit can also apply a different generation algorithm for a furniture design. The generation unit can also select the optimal generation algorithm depending on the design category. This makes it possible to provide the optimal 3D model generation algorithm depending on the design category. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input design category data into the generation AI and cause the generation AI to select the optimal generation algorithm.

[0083] When generating a 3D model, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, optimizes the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. The generation unit can also analyze the user's past generation results and identify areas for improvement in generation. This allows the accuracy of generation to be improved based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0084] The generation unit can estimate the user's emotions and determine the priority of 3D models to be generated based on the estimated user emotions. For example, when the user is excited, the generation unit can prioritize generating important 3D models. Furthermore, when the user is relaxed, the generation unit can prioritize generating detailed 3D models. Furthermore, when the user is stressed, the generation unit can prioritize generating simple 3D models. This allows for optimal prioritization of 3D model generation according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0085] When generating a 3D model, the generation unit can determine a generation priority based on the submission date of the design. For example, the generation unit can prioritize the generation of designs submitted early. The generation unit can also prioritize the generation of designs with an approaching deadline. The generation unit can also adjust the generation priority based on the submission date. This allows for optimal 3D model generation priority to be provided based on the submission date of the design. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design submission date data into the generation AI and have the generation AI determine the optimal generation priority.

[0086] When generating a 3D model, the generation unit can adjust the generation order based on the relevance of the designs. For example, the generation unit prioritizes the generation of highly relevant designs. The generation unit can also postpone the generation of less relevant designs. The generation unit can also adjust the generation order based on the relevance of the designs. This makes it possible to provide an optimal generation order based on the relevance of the designs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design relevance data into the generation AI and have the generation AI determine the optimal generation order.

[0087] When generating a 3D model, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit can provide a generated result using detailed technical terminology to a user with high level of expertise. The generation unit can also provide a generated result in simple language to a user with low level of expertise. The generation unit can also adjust the expression method of the generated result according to the user's level of expertise. This makes it possible to provide an optimal 3D model generation result according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the optimal expression method of the generated result.

[0088] The projection unit can estimate the user's emotions and adjust the projection display method based on the estimated user emotions. For example, if the user is nervous, the projection unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the projection unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the projection unit can provide a display method that focuses on the main points. This makes it possible to provide an optimal projection display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 processing in the projection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the projection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0089] The projection unit can adjust the level of detail of the projection based on the importance of the 3D model during projection. For example, the projection unit performs detailed projection on important 3D models. The projection unit can also perform simplified projection on less important 3D models. The projection unit can also adjust the level of detail of the projection based on the importance of the entire 3D model. This makes it possible to provide an optimal projection method according to the importance of the 3D model. Some or all of the above-described processing in the projection unit may be performed using, or without, AI. For example, the projection unit can input importance data of the 3D model to the generation AI and cause the generation AI to select the optimal projection method.

[0090] The projection unit can apply different projection algorithms depending on the category of the 3D model during projection. For example, the projection unit can apply a specific projection algorithm for character design. The projection unit can also apply a different projection algorithm for furniture design. The projection unit can also select the optimal projection algorithm depending on the category of the 3D model. This makes it possible to provide the optimal projection algorithm depending on the category of the 3D model. Some or all of the above-described processing in the projection unit can be performed using AI, for example, or without AI. For example, the projection unit can input category data of the 3D model to the generation AI and cause the generation AI to select the optimal projection algorithm.

[0091] During projection, the projection unit can improve the accuracy of projection by referring to the user's past projection results. The projection unit, for example, optimizes the projection algorithm based on the user's past projection results. The projection unit can also improve the accuracy of projection by referring to the user's past projection results. The projection unit can also analyze the user's past projection results and identify areas for improvement in the projection. This makes it possible to improve the accuracy of projection based on the user's past projection results. Some or all of the above-described processing in the projection unit may be performed using, for example, AI, or may be performed without using AI. For example, the projection unit can input the user's past projection data into the generation AI and cause the generation AI to improve the accuracy of the projection.

[0092] The projection unit can estimate the user's emotions and determine the priority of the 3D models to be projected based on the estimated user's emotions. For example, if the user is excited, the projection unit can prioritize projecting important 3D models. Furthermore, if the user is relaxed, the projection unit can prioritize projecting detailed 3D models. Furthermore, if the user is stressed, the projection unit can prioritize projecting simple 3D models. This allows for optimal prioritization of 3D model projection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the projection unit can be performed using, for example, AI, or without AI. For example, the projection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0093] During projection, the projection unit can determine the projection priority based on the submission date of the 3D model. For example, the projection unit can prioritize the projection of 3D models submitted early. The projection unit can also prioritize the projection of 3D models whose deadlines are approaching. The projection unit can also adjust the projection priority based on the submission date. This allows for optimal projection priority to be provided based on the submission date of the 3D model. Some or all of the above-described processing in the projection unit can be performed using, for example, AI, or can be performed without using AI. For example, the projection unit can input data on the submission date of the 3D model to the generation AI and have the generation AI determine the optimal projection priority.

[0094] The projection unit can adjust the order of projection based on the relevance of the 3D models during projection. For example, the projection unit prioritizes the projection of highly relevant 3D models. The projection unit can also postpone the projection of less relevant 3D models. The projection unit can also adjust the order of projection based on the relevance of the 3D models. This makes it possible to provide an optimal projection order based on the relevance of the 3D models. Some or all of the above-described processing in the projection unit may be performed using, or without, AI. For example, the projection unit can input relevance data of the 3D models to the generation AI and cause the generation AI to determine the optimal projection order.

[0095] During projection, the projection unit can adjust the use of technical terminology in the projection according to the user's level of expertise. For example, the projection unit can provide projection results using detailed technical terminology to a user with high expertise. The projection unit can also provide projection results in simple language to a user with low expertise. The projection unit can also adjust the way the projection results are expressed according to the user's level of expertise. This makes it possible to provide optimal projection results according to the user's level of expertise. Some or all of the above-described processing in the projection unit can be performed using, for example, AI, or can be performed without using AI. For example, the projection unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the optimal way of expressing the projection results. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and projection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive a user's design input using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the design using a generation AI. The generation unit can generate a 3D model using the specific processing unit 290 of the data processing device 12. The projection unit can project the generated 3D model into real space using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and projection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive a user's design input using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the design using a generation AI. The generation unit can generate a 3D model by the specific processing unit 290 of the data processing device 12. The projection unit can project the generated 3D model into real space using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and projection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive a user's design input using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the design using a generation AI. The generation unit can generate a 3D model by the specific processing unit 290 of the data processing device 12. The projection unit can project the generated 3D model into real space using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and projection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive a user's design input using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the design using a generation AI. The generation unit can generate a 3D model using the specific processing unit 290 of the data processing device 12. The projection unit can project the generated 3D model into real space using the display of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] When the user inputs a design, the reception unit can analyze the success rate of the user's past projects and prioritize displaying design elements with a high success rate. For example, design elements used in the user's past successful projects can be automatically displayed as candidates. The reception unit can also analyze the causes of failure in the user's past projects and provide advice on how to avoid similar failures. Furthermore, the reception unit can suggest optimal design templates based on feedback from the user's past projects. This makes it possible to provide the optimal design input method based on the success rate of the user's past projects.

[0098] The reception unit can estimate the user's emotions and play music based on the estimated user emotions when the user is inputting a design. For example, if the user is relaxed, calm music can be played. If the user is stressed, music with a relaxing effect can be played. Furthermore, if the user is excited, energetic music can be played. This allows the optimal music to be provided according to the user's emotions, improving the design input environment.

[0099] When the user inputs a design, the reception unit can analyze the user's design style and provide an input interface that matches that style. For example, if the user prefers minimalist designs, a simple interface can be provided. Alternatively, if the user prefers complex designs, detailed input options can be provided. Furthermore, the reception unit can suggest an optimal design template based on the user's design style. This makes it possible to provide an optimal input interface that matches the user's design style.

[0100] The reception unit can estimate the user's emotions and provide interactive guidance when inputting a design based on the estimated user emotions. For example, if the user feels anxious, a step-by-step guide can be provided. If the user feels confident, flexible input options can be provided. Furthermore, if the user is in a hurry, shortcuts that allow the user to complete input quickly can be suggested. In this way, optimal interactive guidance can be provided according to the user's emotions.

[0101] When a user inputs a design, the reception unit can customize the input content based on the user's design purpose. For example, if the user is creating a commercial design, the reception unit can provide input options specialized for commercial designs. Also, if the user is creating a personal design, the reception unit can provide input options suitable for personal designs. Furthermore, the reception unit can suggest an optimal design template based on the user's design purpose. This makes it possible to provide optimal input content according to the user's design purpose.

[0102] The reception unit can estimate the user's emotions and provide feedback based on the estimated user emotions when the user is entering a design. For example, if the user is confident, positive feedback can be provided. If the user is feeling anxious, encouraging feedback can be provided. Furthermore, if the user is feeling stressed, feedback with a relaxing effect can be provided. This makes it possible to provide optimal feedback according to the user's emotions and improve motivation for entering a design.

[0103] When analyzing a design, the analysis unit can adjust the level of detail of the analysis based on the user's design purpose. For example, for a commercial design, a detailed analysis can be performed to emphasize commercial elements. For a personal design, a simplified analysis can be performed to emphasize personal elements. Furthermore, the analysis unit can select the optimal analysis algorithm based on the design purpose. This makes it possible to provide the optimal analysis method according to the design purpose.

[0104] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, visually stimulating analysis results can be provided. This makes it possible to provide an optimal display method of the analysis results according to the user's emotions.

[0105] When analyzing a design, the analysis unit can select an analysis algorithm based on the complexity of the user's design. For example, for a complex design, a detailed analysis algorithm can be applied. For a simple design, a simplified analysis algorithm can be applied. Furthermore, the analysis unit can select the optimal analysis algorithm based on the complexity of the design. This makes it possible to provide the optimal analysis method according to the complexity of the design.

[0106] The analysis unit can estimate the user's emotions and provide analysis feedback based on the estimated user emotions. For example, if the user is confident, positive feedback can be provided. If the user is feeling anxious, encouraging feedback can be provided. Furthermore, if the user is feeling stressed, feedback with a relaxing effect can be provided. This makes it possible to provide optimal feedback according to the user's emotions and promote understanding of the analysis results.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit receives a design from the user. The designs input by the user include graphic designs, product designs, architectural designs, etc. The reception unit allows the user to input the design in text format, image format, or voice format. In the case of voice input, the voice is converted into text data using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the design entered by the reception unit. The analysis is performed using methods such as shape analysis, function analysis, and structure analysis. The analysis unit analyzes the shape, function, and structure of the design and extracts the information necessary to generate a 3D model. Step 3: The generator uses the AI ​​to generate a 3D model based on the design analyzed by the analyzer. The 3D model is generated in the form of a polygon model, NURBS model, voxel model, etc. Step 4: The projection unit projects the 3D model generated by the generation unit into real space. The projection is performed using technologies such as AR (augmented reality), VR (virtual reality), and projection mapping.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where designs are entered; an analysis unit that analyzes the design input by the reception unit; a generation unit that generates a 3D model based on the design analyzed by the analysis unit; a projection unit that projects the 3D model generated by the generation unit into real space; Equipped with A system characterized by:

2. The reception unit Estimate user emotions and adjust design input methods based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyzes the user's past design input history and provides an input interface 2. The system of claim 1.

4. The reception unit As you enter your design, filter your input based on your current project or area of ​​interest 2. The system of claim 1.

5. The reception unit When entering a design, select the input method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate user emotions and prioritize input designs based on the estimated user emotions 2. The system of claim 1.

7. The reception unit When entering designs, the app takes into account the user's geographic location to prioritize relevant designs.

2. The system of claim 1.

8. The reception unit When entering a design, analyze the user's social media activity and enter relevant designs.

2. The system of claim 1.

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