system

Abstract

An object of the system according to the embodiment is to generate a 3D model or a VR image in response to a natural-language instruction even if a user is not knowledgeable about design or CAD.SOLUTION: A system includes an instruction input unit, an analysis unit, a generation unit, and a presentation unit. The instruction input unit receives an instruction in a natural language from a user. The analysis unit analyzes the instruction received by the instruction input unit. The generation unit generates a 3D model based on the design elements analyzed by the analysis unit. The presenting unit generates and presents a 3D image based on the 3DVR model generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for users without knowledge of design or CAD to generate 3D models and VR images.

[0005] The system according to the embodiment aims to generate 3D models and VR images by instructions in natural language, even if the user has no knowledge of design or CAD. [Means for solving the problem]

[0006] The system according to the embodiment includes an instruction input unit, an analysis unit, a generation unit, and a presentation unit. The instruction input unit accepts instructions in a user's natural language. The analysis unit analyzes the instructions accepted by the instruction input unit. The generation unit generates a 3D model based on the design elements analyzed by the analysis unit. The presentation unit generates and presents a 3D VR image based on the 3D model generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate 3D models and VR images by instructions in natural language, even if the user has no knowledge of design or CAD. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The interior design simulation system according to an embodiment of the present invention is a system in which a user simply gives verbal instructions to simulate a visual image, and a generation AI generates and displays a 3D VR image. This allows anyone to easily simulate an interior design and provides a tool for realizing their ideal home.

[0029] An interior design simulation system according to an embodiment includes an instruction input unit, an analysis unit, a generation unit, and a presentation unit. The instruction input unit accepts instructions in natural language from a user. For example, the user may input an instruction such as "The wallpaper should be light blue and the floor should be wood-grain flooring." The analysis unit analyzes the instructions accepted by the instruction input unit. For example, the analysis unit may use natural language processing technology to analyze the user's instructions and extract design elements. The generation unit generates a 3D model based on the design elements analyzed by the analysis unit. For example, the generation unit may use modeling software to generate a 3D model that reflects the wallpaper color and floor material. The presentation unit generates and presents a 3D VR image based on the 3D model generated by the generation unit. For example, the presentation unit may use rendering technology to generate a 3D VR image with realistic textures and lighting effects and present the image to the user via a VR device. This allows the interior design simulation system according to an embodiment to generate and present a 3D VR image simply by the user inputting instructions in natural language.

[0030] The instruction input unit can learn the user's past instruction history and predict and suggest a preferred design. The instruction input unit, for example, learns the user's past instruction history and predicts a preferred design. For example, if the user previously instructed "white wallpaper and wood-grain flooring," the same design will be suggested the next time. The instruction input unit also predicts and suggests a preferred design based on the user's past instruction history. For example, if the user previously instructed "blue wallpaper and carpet flooring," the next time it will also suggest blue-toned wallpaper and carpet. The instruction input unit also analyzes the user's past instruction history and predicts a preferred design. For example, if the user previously instructed "green wallpaper and tile flooring," the next time it will also suggest green-toned wallpaper and tile. This makes it possible to suggest designs based on the user's preferences.

[0031] The instruction input unit can enable instruction input using not only voice input but also gesture input and eye tracking. The instruction input unit, for example, allows a user to input instructions using not only voice input but also gesture input. For example, selecting the wallpaper color or floor material with hand movements. The instruction input unit also uses eye tracking technology to allow a user to input instructions with their gaze. For example, selecting the wallpaper color or floor material with their gaze. The instruction input unit also combines voice input, gesture input, and eye tracking to allow a user to input instructions intuitively. For example, specifying the wallpaper color with voice and selecting the floor material with gestures. This allows support for a variety of input methods, thereby improving user convenience.

[0032] The instruction input unit supports input of instructions in different languages, making it possible to accommodate international users. The instruction input unit, for example, allows users to input instructions in different languages. For example, multilingual support is realized, such as English, French, and Chinese. The instruction input unit also supports input of instructions in different languages, making it possible to accommodate international users. For example, it allows users to input instructions in their native language. The instruction input unit also supports input of instructions in different languages ​​using multilingual natural language processing technology. For example, a user inputs instructions in Japanese, and the generation AI analyzes the instructions. This multilingual support makes it possible to use the system for international users.

[0033] The analysis unit can perform customization taking into consideration the user's lifestyle and hobbies. The analysis unit, for example, analyzes and customizes design elements taking into consideration the user's lifestyle and hobbies. For example, for a user who likes the outdoors, it would suggest a design that makes extensive use of natural materials. The analysis unit also customizes design elements based on the user's hobbies and interests. For example, for a user who likes music, it would suggest a design that takes acoustic effects into consideration. The analysis unit also analyzes the user's lifestyle and customizes design elements based on that. For example, for a household with children, it would suggest a design that emphasizes safety. This makes it possible to customize according to the user's lifestyle and hobbies.

[0034] The analysis unit can reflect real-time user feedback on the design elements proposed by the generation AI. For example, the analysis unit reflects real-time user feedback on the design elements proposed by the generation AI. For example, if a user provides feedback such as "This wallpaper color is a little dark," the generation AI immediately adjusts the color. The analysis unit also allows the generation AI to adjust the design elements based on real-time user feedback. For example, if a user indicates that they want to change the floor material, the generation AI immediately proposes a new material. The analysis unit also builds a system that reflects user feedback in real time on the design elements proposed by the generation AI. For example, if a user provides feedback such as "I don't like this design," the generation AI proposes a new design. This allows user feedback to be reflected in real time, enabling more accurate design proposals.

[0035] The analysis unit can incorporate external design databases and trend information into the analysis of design elements. For example, the analysis unit incorporates an external design database into the analysis of design elements. For example, the analysis unit analyzes design elements based on the latest interior design trend information. The analysis unit also incorporates external design databases and trend information to analyze design elements. For example, the analysis unit suggests design elements based on popular design styles and color combinations. The analysis unit also builds a system that incorporates external trend information into the analysis of design elements. For example, the analysis unit collects information from the latest design magazines and websites and analyzes design elements based on that information. In this way, by incorporating external design databases and trend information, it is possible to reflect the latest design elements.

[0036] The analysis unit can provide an interface that allows the user to manually fine-tune the design elements suggested by the generative AI. The analysis unit, for example, provides an interface that allows the user to manually fine-tune the design elements suggested by the generative AI. For example, it may allow the user to adjust the wallpaper color or floor material using a slider. The analysis unit also provides an interface that allows the user to manually fine-tune the design elements based on the suggestions of the generative AI. For example, it may allow the user to adjust the color intensity or the texture of the material. The analysis unit also builds an interface that allows the user to manually fine-tune the design elements suggested by the generative AI. For example, it may allow the user to place design elements using drag and drop. By providing an interface that allows the user to manually fine-tune design elements, more detailed customization is possible.

[0037] The generation unit can add a function that allows the user to arrange the user's furniture and decorations in real time to the generated 3D VR image. For example, the generation unit adds a function that allows the user to arrange the user's own furniture and decorations in real time to the generated 3D VR image. For example, the user can change the furniture arrangement to experiment with room layouts. The generation unit also provides a function that allows the user to upload images of the user's own furniture and decorations and arrange them in the 3D VR image in real time. For example, the user can arrange their own sofa or table to check the atmosphere of the room. The generation unit also provides an interface that allows the user to arrange the furniture and decorations they have selected in the generated 3D VR image by dragging and dropping. For example, the user can freely move the furniture to find the optimal arrangement. This allows the user to arrange the user's own furniture and decorations in real time, enabling more realistic simulations.

[0038] The generation unit can reflect lighting effects according to the season and time of day in the 3D VR images generated by the generation AI. For example, the generation unit reflects lighting effects according to the season and time of day in the generated 3D VR images. For example, it simulates lighting effects in different situations, such as summer daytime and winter nighttime. The generation unit also automatically adjusts the lighting effects of the 3D VR images according to the season and time of day specified by the user. For example, it realistically reproduces morning light and evening shadows. The generation unit also reflects lighting effects according to the season and time of day, allowing the user to check interior designs in different situations. For example, it simulates bright spring sunlight and soft autumn light. This allows for more realistic simulations by reflecting lighting effects according to the season and time of day.

[0039] The generation unit can make the generated 3D VR image multi-user compatible, allowing multiple users to experience it simultaneously. For example, the generation unit makes the generated 3D VR image multi-user compatible, allowing multiple users to experience it simultaneously. For example, checking an interior design in a VR space with family and friends. The generation unit also provides multi-user compatible 3D VR images, allowing multiple users to interact with each other simultaneously. For example, multiple users can simultaneously rearrange furniture or exchange opinions. The generation unit also builds a system that allows multiple users to experience the generated 3D VR image simultaneously. For example, remotely connected users can check a design in the same VR space. This allows multiple users to experience the 3D VR image simultaneously, enabling collaborative design review.

[0040] The generation unit can make the generated 3D VR image available for experience on an AR device. The generation unit can make the generated 3D VR image available for experience on an AR device, for example. For example, a 3D model can be overlaid on a real room using a smartphone or tablet. The generation unit can also use an AR device to overlay the generated 3D VR image on real space. For example, a user can place a 3D model in their own room to check the interior design. The generation unit can also build a system that allows the generated 3D VR image to be experienced on an AR device. For example, a user can wear AR goggles and display the 3D model on real space. This allows the 3D model to be overlaid on real space using the AR device.

[0041] The presentation unit can add a function that allows a user to share a design customized by the user with other users and receive feedback. The presentation unit adds a function that allows a user to share a design customized by the user with other users and receive feedback. For example, the design is published on a social networking site or a dedicated platform. The presentation unit also provides a function that allows a user to share a customized design and receive feedback from other users. For example, the design is shared with friends and family and opinions are exchanged. The presentation unit also builds a system that allows a user to share a design customized by the user with other users and receive feedback. For example, the design is evaluated in a design contest or community. This allows a user to share a customized design with other users and receive feedback, thereby creating better designs.

[0042] The presentation unit can take into account the user's budget and time constraints when the generation AI makes customization suggestions. For example, the presentation unit takes into account the user's budget when the generation AI makes customization suggestions. For example, it proposes the optimal design within the budget. The presentation unit also takes into account the user's time constraints when the generation AI makes customization suggestions. For example, it proposes a design that can be realized in a short period of time. The presentation unit also builds a system that takes into account the user's budget and time constraints when the generation AI makes customization suggestions. For example, it proposes the optimal design based on the budget and time. This makes it possible to make customization suggestions that take into account the user's budget and time constraints.

[0043] The presentation unit can add a function to send the customized design directly to the actual construction company and obtain an estimate. The presentation unit adds, for example, a function to send the customized design directly to the actual construction company and obtain an estimate. For example, the presentation unit sends design data to the construction company and receives an estimate. The presentation unit also provides a function to send the design customized by the user to the construction company and obtain an estimate. For example, the construction company creates an estimate based on the design data. The presentation unit also builds a system to send the customized design directly to the actual construction company and obtain an estimate. For example, the presentation unit sends design data to the construction company and receives an estimate in real time. This allows the customized design to be sent directly to the construction company and an estimate to be obtained, thereby smoothly preparing for actual construction.

[0044] The presentation unit can share the customized design on social media and collect opinions from other users. For example, the presentation unit adds a function for sharing the customized design on social media and collecting opinions from other users. For example, the design is published on Facebook (registered trademark) or Instagram (registered trademark). The presentation unit also provides a function for a user to share the customized design on social media and collect opinions from other users. For example, the design is shared on Twitter (registered trademark) and comments are received. The presentation unit also builds a system for sharing the customized design on social media and collecting opinions from other users. For example, the design is published on Pinterest and feedback is received. This makes it possible to improve the design by sharing the customized design on social media and collecting opinions from other users.

[0045] The presentation unit can incorporate tutorials and guides for beginners into the user interface. The presentation unit, for example, incorporates a tutorial for beginners into the user interface. For example, it provides a guide that explains basic operation methods to a user using the system for the first time. The presentation unit also incorporates a guide for beginners into the user interface to clearly explain operation methods. For example, it provides a step-by-step tutorial. The presentation unit also builds a system that incorporates tutorials and guides for beginners into the user interface. For example, it provides an interactive guide or video tutorial. In this way, by incorporating tutorials and guides for beginners, even first-time users can easily operate the system.

[0046] The presentation unit can learn the user's operation history and add a personalization function that simplifies operations from the next time onwards. The presentation unit, for example, learns the user's operation history and adds a personalization function that simplifies operations from the next time onwards. For example, it automatically displays frequently used functions and settings. The presentation unit also learns the user's preferences and habits based on the operation history and provides a personalized interface. For example, it prioritizes displaying functions that the user uses frequently. The presentation unit also analyzes the user's operation history and builds a personalization function that simplifies operations from the next time onwards. For example, it suggests optimal operation procedures based on past operation history. In this way, the presentation unit learns the user's operation history and simplifies operations from the next time onwards, thereby improving user convenience.

[0047] The presentation unit can also optimize the interface for mobile devices such as tablets and smartphones. The presentation unit, for example, optimizes the interface for mobile devices such as tablets and smartphones. For example, the layout is automatically adjusted according to the screen size. The presentation unit also provides an interface optimized for mobile devices, allowing users to easily operate it anywhere. For example, the presentation unit provides an interface that supports touch operations. The presentation unit also builds a system that optimizes the interface for mobile devices. For example, the system employs a responsive design and adjusts the display according to the device. By optimizing the interface for mobile devices, the system allows users to easily operate it anywhere.

[0048] The presentation unit can add a voice assistant function to the user interface, enabling operation by voice. The presentation unit, for example, adds a voice assistant function to the user interface, enabling operation by voice. For example, the user gives voice instructions to change the design. The presentation unit also uses the voice assistant function to provide an interface that the user can operate by voice. For example, changing the color of wallpaper or the material of the floor with a voice command. The presentation unit also adds a voice assistant function to the user interface, building a system that enables operation by voice. For example, it uses voice recognition technology to analyze the user's instructions. In this way, adding the voice assistant function enables operation by voice, improving user convenience.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The instruction input unit can enable user input of instructions using gestures or eye tracking. For example, a user can select a wallpaper color or floor material by moving their hand. Eye tracking technology can also be used to enable a user to input instructions using their gaze. For example, a wallpaper color or floor material can be selected using their gaze. Furthermore, voice input, gesture input, and eye tracking can be combined to enable a user to input instructions intuitively. For example, a wallpaper color can be specified by voice and a floor material can be selected by gesture. This allows for support of a variety of input methods, thereby improving user convenience.

[0051] The instruction input unit can learn the user's past instruction history and predict and suggest a preferred design. For example, the instruction input unit learns the user's past instruction history and predicts a preferred design. For example, if the user previously instructed "white wallpaper and wood-grain flooring," the same design will be suggested the next time. The instruction input unit also predicts and suggests a preferred design based on the user's past instruction history. For example, if the user previously instructed "blue wallpaper and carpet flooring," the unit will suggest blue wallpaper and carpet the next time as well. The instruction input unit also analyzes the user's past instruction history and predicts a preferred design. For example, if the user previously instructed "green wallpaper and tile flooring," the unit will suggest green wallpaper and tiles the next time as well. This makes it possible to suggest designs based on the user's preferences.

[0052] The instruction input unit can enable instruction input using not only voice input but also gesture input and eye tracking. For example, the user can input instructions using not only voice input but also gesture input. For example, the user can select the wallpaper color or floor material by moving his or her hand. The instruction input unit can also use eye tracking technology to enable the user to input instructions using their gaze. For example, the user can select the wallpaper color or floor material by using their gaze. The instruction input unit can also combine voice input, gesture input, and eye tracking to enable the user to input instructions intuitively. For example, the user can specify the wallpaper color by voice and select the floor material by gesture. This allows for support for a variety of input methods, thereby improving user convenience.

[0053] The instruction input unit supports input of instructions in different languages, making it possible to accommodate international users. For example, it allows users to input instructions in different languages. For example, it realizes multilingual support such as English, French, and Chinese. The instruction input unit also supports input of instructions in different languages, making it possible to accommodate international users. For example, it allows users to input instructions in their native language. The instruction input unit also supports input of instructions in different languages ​​using multilingual natural language processing technology. For example, a user inputs instructions in Japanese, and the generation AI analyzes the instructions. This multilingual support makes it possible to use the system for international users.

[0054] The analysis unit can perform customization taking into account the user's lifestyle and hobbies. For example, it analyzes and customizes design elements taking into account the user's lifestyle and hobbies. For example, for a user who likes the outdoors, it proposes a design that makes extensive use of natural materials. The analysis unit also customizes design elements based on the user's hobbies and interests. For example, for a user who likes music, it proposes a design that takes acoustic effects into consideration. The analysis unit also analyzes the user's lifestyle and customizes design elements based on that. For example, it proposes a design that emphasizes safety for a household with children. This makes it possible to customize according to the user's lifestyle and hobbies.

[0055] The analysis unit can reflect real-time user feedback on the design elements proposed by the generation AI. For example, if a user provides feedback such as "This wallpaper color is a little dark," the generation AI will instantly adjust the color. The analysis unit also allows the generation AI to adjust the design elements based on real-time user feedback. For example, if a user indicates that they would like to change the floor material, the generation AI will instantly suggest a new material. The analysis unit also builds a system that reflects user feedback in real time on the design elements proposed by the generation AI. For example, if a user provides feedback such as "I don't like this design," the generation AI will suggest a new design. This allows user feedback to be reflected in real time, enabling more accurate design suggestions.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The instruction input unit accepts instructions in natural language from the user. For example, the user inputs an instruction such as "The wallpaper should be light blue and the floor should have wood-grain flooring." Step 2: The analysis unit analyzes the instruction received by the instruction input unit. For example, the analysis unit uses natural language processing technology to analyze the user's instruction and extract design elements. Step 3: The generation unit generates a 3D model based on the design elements analyzed by the analysis unit. For example, the generation unit uses modeling software to generate a 3D model that reflects the color of wallpaper and the material of the floor. Step 4: The presentation unit generates and presents a 3D VR image based on the 3D model generated by the generation unit. For example, the presentation unit may use rendering technology to generate a 3D VR image with realistic textures and lighting effects, and present the image to the user through a VR device.

[0058] (Example 2) The interior design simulation system according to an embodiment of the present invention is a system in which a user simply gives verbal instructions to simulate a visual image, and a generation AI generates and displays a 3D VR image. This allows anyone to easily simulate an interior design and provides a tool for realizing their ideal home.

[0059] An interior design simulation system according to an embodiment includes an instruction input unit, an analysis unit, a generation unit, and a presentation unit. The instruction input unit accepts instructions in natural language from a user. For example, the user may input an instruction such as "The wallpaper should be light blue and the floor should be wood-grain flooring." The analysis unit analyzes the instructions accepted by the instruction input unit. For example, the analysis unit may use natural language processing technology to analyze the user's instructions and extract design elements. The generation unit generates a 3D model based on the design elements analyzed by the analysis unit. For example, the generation unit may use modeling software to generate a 3D model that reflects the wallpaper color and floor material. The presentation unit generates and presents a 3D VR image based on the 3D model generated by the generation unit. For example, the presentation unit may use rendering technology to generate a 3D VR image with realistic textures and lighting effects and present the image to the user via a VR device. This allows the interior design simulation system according to an embodiment to generate and present a 3D VR image simply by the user inputting instructions in natural language.

[0060] The instruction input unit can analyze the tone and speed of the user's voice to estimate the user's emotional state and adjust the design proposal accordingly. For example, when the user inputs an instruction such as "light blue wallpaper and wood-grain flooring," the instruction input unit analyzes the tone and speed of the user's voice to estimate whether the user is relaxed or in a hurry. If the user is relaxed, the unit proposes a more subdued design, and if the user is in a hurry, the unit quickly displays the results. The instruction input unit also analyzes the tone of the user's voice when inputting instructions to estimate the user's emotional state. For example, if the user is excited, the unit proposes a bright color and a bold design, and if the user is calm, the unit proposes a simple and subdued design. The instruction input unit also analyzes the speed of the user's voice to estimate the user's emotional state. For example, if the user is speaking slowly, the unit proposes a detailed design, and if the user is speaking quickly, the unit proposes a concise and intuitive design. This enables design proposals to be made according to the user's emotional state.

[0061] The instruction input unit can learn the user's past instruction history and predict and suggest a preferred design. The instruction input unit, for example, learns the user's past instruction history and predicts a preferred design. For example, if the user previously instructed "white wallpaper and wood-grain flooring," the same design will be suggested the next time. The instruction input unit also predicts and suggests a preferred design based on the user's past instruction history. For example, if the user previously instructed "blue wallpaper and carpet flooring," the next time it will also suggest blue-toned wallpaper and carpet. The instruction input unit also analyzes the user's past instruction history and predicts a preferred design. For example, if the user previously instructed "green wallpaper and tile flooring," the next time it will also suggest green-toned wallpaper and tile. This makes it possible to suggest designs based on the user's preferences.

[0062] The instruction input unit can use the emotion estimation function to automatically suggest design elements according to the user's emotions. For example, when the user inputs an instruction such as "light blue wallpaper and wood-grain flooring," the instruction input unit uses the emotion estimation function to analyze the user's emotions and suggest design elements according to the emotions. For example, if the user is relaxed, a calm design is suggested. The instruction input unit also uses the emotion estimation function to automatically suggest design elements according to the user's emotions. For example, if the user is excited, a bright color or a bold design is suggested. The instruction input unit also analyzes the user's emotions in real time and suggests design elements according to the emotions. For example, if the user is calm, a simple and calm design is suggested. This makes it possible to suggest design elements according to the user's emotions.

[0063] The instruction input unit can enable instruction input using not only voice input but also gesture input and eye tracking. The instruction input unit, for example, allows a user to input instructions using not only voice input but also gesture input. For example, selecting the wallpaper color or floor material with hand movements. The instruction input unit also uses eye tracking technology to allow a user to input instructions with their gaze. For example, selecting the wallpaper color or floor material with their gaze. The instruction input unit also combines voice input, gesture input, and eye tracking to allow a user to input instructions intuitively. For example, specifying the wallpaper color with voice and selecting the floor material with gestures. This allows support for a variety of input methods, thereby improving user convenience.

[0064] The instruction input unit supports input of instructions in different languages, making it possible to accommodate international users. The instruction input unit, for example, allows users to input instructions in different languages. For example, multilingual support is realized, such as English, French, and Chinese. The instruction input unit also supports input of instructions in different languages, making it possible to accommodate international users. For example, it allows users to input instructions in their native language. The instruction input unit also supports input of instructions in different languages ​​using multilingual natural language processing technology. For example, a user inputs instructions in Japanese, and the generation AI analyzes the instructions. This multilingual support makes it possible to use the system for international users.

[0065] The instruction input unit can use the emotion estimation function to analyze the emotion of the user when inputting an instruction in real time, and provide feedback that elicits positive emotions. The instruction input unit, for example, analyzes the emotion of the user when inputting an instruction in real time, and provides feedback that elicits positive emotions. For example, if the user is relaxed, an encouraging message is displayed. The instruction input unit also uses the emotion estimation function to analyze the emotion of the user when inputting an instruction, and provides feedback that elicits positive emotions. For example, if the user is excited, a calm message is displayed. The instruction input unit also analyzes the emotion of the user in real time, and provides feedback that elicits positive emotions. For example, if the user is calm, simple and intuitive feedback is provided. In this way, feedback that corresponds to the user's emotions is provided, thereby promoting a positive experience.

[0066] The analysis unit can perform customization taking into consideration the user's lifestyle and hobbies. The analysis unit, for example, analyzes and customizes design elements taking into consideration the user's lifestyle and hobbies. For example, for a user who likes the outdoors, it would suggest a design that makes extensive use of natural materials. The analysis unit also customizes design elements based on the user's hobbies and interests. For example, for a user who likes music, it would suggest a design that takes acoustic effects into consideration. The analysis unit also analyzes the user's lifestyle and customizes design elements based on that. For example, for a household with children, it would suggest a design that emphasizes safety. This makes it possible to customize according to the user's lifestyle and hobbies.

[0067] The analysis unit can reflect real-time user feedback on the design elements proposed by the generation AI. For example, the analysis unit reflects real-time user feedback on the design elements proposed by the generation AI. For example, if a user provides feedback such as "This wallpaper color is a little dark," the generation AI immediately adjusts the color. The analysis unit also allows the generation AI to adjust the design elements based on real-time user feedback. For example, if a user indicates that they want to change the floor material, the generation AI immediately proposes a new material. The analysis unit also builds a system that reflects user feedback in real time on the design elements proposed by the generation AI. For example, if a user provides feedback such as "I don't like this design," the generation AI proposes a new design. This allows user feedback to be reflected in real time, enabling more accurate design proposals.

[0068] The analysis unit can use the emotion estimation function to prioritize analysis of design elements based on the user's emotion. The analysis unit, for example, uses the emotion estimation function to prioritize analysis of design elements based on the user's emotion. For example, if the user is relaxed, calm design elements are prioritized for analysis. The analysis unit also analyzes the user's emotion in real time and prioritizes analysis of design elements based on the emotion. For example, if the user is excited, bright colors and bold design elements are prioritized for analysis. The analysis unit also uses the emotion estimation function to build a system that prioritizes analysis of design elements based on the user's emotion. For example, if the user is calm, simple and calm design elements are prioritized for analysis. This makes it possible to analyze design elements based on the user's emotion.

[0069] The analysis unit can incorporate external design databases and trend information into the analysis of design elements. For example, the analysis unit incorporates an external design database into the analysis of design elements. For example, the analysis unit analyzes design elements based on the latest interior design trend information. The analysis unit also incorporates external design databases and trend information to analyze design elements. For example, the analysis unit suggests design elements based on popular design styles and color combinations. The analysis unit also builds a system that incorporates external trend information into the analysis of design elements. For example, the analysis unit collects information from the latest design magazines and websites and analyzes design elements based on that information. In this way, by incorporating external design databases and trend information, it is possible to reflect the latest design elements.

[0070] The analysis unit can provide an interface that allows the user to manually fine-tune the design elements suggested by the generative AI. The analysis unit, for example, provides an interface that allows the user to manually fine-tune the design elements suggested by the generative AI. For example, it may allow the user to adjust the wallpaper color or floor material using a slider. The analysis unit also provides an interface that allows the user to manually fine-tune the design elements based on the suggestions of the generative AI. For example, it may allow the user to adjust the color intensity or the texture of the material. The analysis unit also builds an interface that allows the user to manually fine-tune the design elements suggested by the generative AI. For example, it may allow the user to place design elements using drag and drop. By providing an interface that allows the user to manually fine-tune design elements, more detailed customization is possible.

[0071] The analysis unit can use the emotion estimation function to automatically select design elements that evoke the most positive emotion in the user. For example, the analysis unit uses the emotion estimation function to automatically select design elements that evoke the most positive emotion in the user. For example, if the user is relaxed, calm colors and materials are selected. The analysis unit also analyzes the user's emotions in real time and selects design elements that evoke the most positive emotion. For example, if the user is excited, bright colors and bold designs are selected. The analysis unit also uses the emotion estimation function to build a system that automatically selects design elements that evoke the most positive emotion in the user. For example, if the user is calm, simple and calm design elements are selected. This makes it possible to improve user satisfaction by automatically selecting design elements that evoke the most positive emotion in the user.

[0072] The generation unit can add a function that allows the user to arrange the user's furniture and decorations in real time to the generated 3D VR image. For example, the generation unit adds a function that allows the user to arrange the user's own furniture and decorations in real time to the generated 3D VR image. For example, the user can change the furniture arrangement to experiment with room layouts. The generation unit also provides a function that allows the user to upload images of the user's own furniture and decorations and arrange them in the 3D VR image in real time. For example, the user can arrange their own sofa or table to check the atmosphere of the room. The generation unit also provides an interface that allows the user to arrange the furniture and decorations they have selected in the generated 3D VR image by dragging and dropping. For example, the user can freely move the furniture to find the optimal arrangement. This allows the user to arrange the user's own furniture and decorations in real time, enabling more realistic simulations.

[0073] The generation unit can reflect lighting effects according to the season and time of day in the 3D VR images generated by the generation AI. For example, the generation unit reflects lighting effects according to the season and time of day in the generated 3D VR images. For example, it simulates lighting effects in different situations, such as summer daytime and winter nighttime. The generation unit also automatically adjusts the lighting effects of the 3D VR images according to the season and time of day specified by the user. For example, it realistically reproduces morning light and evening shadows. The generation unit also reflects lighting effects according to the season and time of day, allowing the user to check interior designs in different situations. For example, it simulates bright spring sunlight and soft autumn light. This allows for more realistic simulations by reflecting lighting effects according to the season and time of day.

[0074] The generation unit can use the emotion estimation function to automatically adjust lighting and color tones according to the user's emotions. The generation unit, for example, uses the emotion estimation function to automatically adjust lighting and color tones according to the user's emotions. For example, if the user is relaxed, soft lighting and subdued color tones are set. The generation unit also analyzes the user's emotions in real time and automatically adjusts lighting and color tones according to the emotions. For example, if the user is excited, bright lighting and vivid color tones are set. The generation unit also uses the emotion estimation function to build a system that automatically adjusts lighting and color tones based on the user's emotions. For example, if the user is calm, simple and subdued lighting and color tones are set. This makes it possible to adjust lighting and color tones according to the user's emotions.

[0075] The generation unit can make the generated 3D VR image multi-user compatible, allowing multiple users to experience it simultaneously. For example, the generation unit makes the generated 3D VR image multi-user compatible, allowing multiple users to experience it simultaneously. For example, checking an interior design in a VR space with family and friends. The generation unit also provides multi-user compatible 3D VR images, allowing multiple users to interact with each other simultaneously. For example, multiple users can simultaneously rearrange furniture or exchange opinions. The generation unit also builds a system that allows multiple users to experience the generated 3D VR image simultaneously. For example, remotely connected users can check a design in the same VR space. This allows multiple users to experience the 3D VR image simultaneously, enabling collaborative design review.

[0076] The generation unit can make the generated 3D VR image available for experience on an AR device. The generation unit can make the generated 3D VR image available for experience on an AR device, for example. For example, a 3D model can be overlaid on a real room using a smartphone or tablet. The generation unit can also use an AR device to overlay the generated 3D VR image on real space. For example, a user can place a 3D model in their own room to check the interior design. The generation unit can also build a system that allows the generated 3D VR image to be experienced on an AR device. For example, a user can wear AR goggles and display the 3D model on real space. This allows the 3D model to be overlaid on real space using the AR device.

[0077] The generation unit can use the emotion estimation function to automatically highlight scenes that the user most emotionally empathizes with. For example, the generation unit uses the emotion estimation function to automatically highlight scenes that the user most emotionally empathizes with. For example, it highlights scenes in which the user is relaxed. The generation unit also analyzes the user's emotions in real time and automatically highlights scenes that the user most emotionally empathizes with. For example, it highlights scenes in which the user is excited. The generation unit also uses the emotion estimation function to build a system that automatically highlights scenes that the user most emotionally empathizes with. For example, it highlights scenes in which the user is calm. This makes it possible to provide a more emotional experience by highlighting scenes that the user most emotionally empathizes with.

[0078] The presentation unit can add a function that allows a user to share a design customized by the user with other users and receive feedback. The presentation unit adds a function that allows a user to share a design customized by the user with other users and receive feedback. For example, the design is published on a social networking site or a dedicated platform. The presentation unit also provides a function that allows a user to share a customized design and receive feedback from other users. For example, the design is shared with friends and family and opinions are exchanged. The presentation unit also builds a system that allows a user to share a design customized by the user with other users and receive feedback. For example, the design is evaluated in a design contest or community. This allows a user to share a customized design with other users and receive feedback, thereby creating better designs.

[0079] The presentation unit can take into account the user's budget and time constraints when the generation AI makes customization suggestions. For example, the presentation unit takes into account the user's budget when the generation AI makes customization suggestions. For example, it proposes the optimal design within the budget. The presentation unit also takes into account the user's time constraints when the generation AI makes customization suggestions. For example, it proposes a design that can be realized in a short period of time. The presentation unit also builds a system that takes into account the user's budget and time constraints when the generation AI makes customization suggestions. For example, it proposes the optimal design based on the budget and time. This makes it possible to make customization suggestions that take into account the user's budget and time constraints.

[0080] The presentation unit can use the emotion estimation function to make customization suggestions based on the user's emotions. The presentation unit, for example, uses the emotion estimation function to make customization suggestions based on the user's emotions. For example, if the user is relaxed, a calm design is suggested. The presentation unit also analyzes the user's emotions in real time and makes customization suggestions based on the emotions. For example, if the user is excited, a bright color or a bold design is suggested. The presentation unit also uses the emotion estimation function to build a system that makes customization suggestions based on the user's emotions. For example, if the user is calm, a simple and calm design is suggested. This makes it possible to make customization suggestions based on the user's emotions.

[0081] The presentation unit can add a function to send the customized design directly to the actual construction company and obtain an estimate. The presentation unit adds, for example, a function to send the customized design directly to the actual construction company and obtain an estimate. For example, the presentation unit sends design data to the construction company and receives an estimate. The presentation unit also provides a function to send the design customized by the user to the construction company and obtain an estimate. For example, the construction company creates an estimate based on the design data. The presentation unit also builds a system to send the customized design directly to the actual construction company and obtain an estimate. For example, the presentation unit sends design data to the construction company and receives an estimate in real time. This allows the customized design to be sent directly to the construction company and an estimate to be obtained, thereby smoothly preparing for actual construction.

[0082] The presentation unit can share the customized design on social media and collect opinions from other users. For example, the presentation unit adds a function for sharing the customized design on social media and collecting opinions from other users. For example, the design is published on Facebook (registered trademark) or Instagram (registered trademark). The presentation unit also provides a function for a user to share the customized design on social media and collect opinions from other users. For example, the design is shared on Twitter (registered trademark) and comments are received. The presentation unit also builds a system for sharing the customized design on social media and collecting opinions from other users. For example, the design is published on Pinterest and feedback is received. This makes it possible to improve the design by sharing the customized design on social media and collecting opinions from other users.

[0083] The presentation unit can use the emotion estimation function to automatically suggest customization options that evoke the most positive emotions in the user. For example, the presentation unit uses the emotion estimation function to automatically suggest customization options that evoke the most positive emotions in the user. For example, if the user is relaxed, a calm design option is suggested. The presentation unit also analyzes the user's emotions in real time and suggests customization options that evoke the most positive emotions. For example, if the user is excited, a bright color or bold design option is suggested. The presentation unit also uses the emotion estimation function to build a system that automatically suggests customization options that evoke the most positive emotions in the user. For example, if the user is calm, a simple and calm design option is suggested. This makes it possible to improve user satisfaction by suggesting customization options that evoke the most positive emotions in the user.

[0084] The presentation unit can incorporate tutorials and guides for beginners into the user interface. The presentation unit, for example, incorporates a tutorial for beginners into the user interface. For example, it provides a guide that explains basic operation methods to a user using the system for the first time. The presentation unit also incorporates a guide for beginners into the user interface to clearly explain operation methods. For example, it provides a step-by-step tutorial. The presentation unit also builds a system that incorporates tutorials and guides for beginners into the user interface. For example, it provides an interactive guide or video tutorial. In this way, by incorporating tutorials and guides for beginners, even first-time users can easily operate the system.

[0085] The presentation unit can learn the user's operation history and add a personalization function that simplifies operations from the next time onwards. The presentation unit, for example, learns the user's operation history and adds a personalization function that simplifies operations from the next time onwards. For example, it automatically displays frequently used functions and settings. The presentation unit also learns the user's preferences and habits based on the operation history and provides a personalized interface. For example, it prioritizes displaying functions that the user uses frequently. The presentation unit also analyzes the user's operation history and builds a personalization function that simplifies operations from the next time onwards. For example, it suggests optimal operation procedures based on past operation history. In this way, the presentation unit learns the user's operation history and simplifies operations from the next time onwards, thereby improving user convenience.

[0086] The presentation unit can use the emotion estimation function to improve the interface to reduce stress felt by the user during operation. The presentation unit, for example, uses the emotion estimation function to improve the interface to reduce stress felt by the user during operation. For example, if the user is feeling stressed, the operation procedure is simplified. The presentation unit also analyzes the user's emotion in real time and improves the interface to reduce stress. For example, if the user is irritated, a message guiding the operation is displayed. The presentation unit also uses the emotion estimation function to build a system that improves the interface to reduce stress felt by the user during operation. For example, a design or color scheme that helps the user relax is adopted. This makes it possible to improve the interface to reduce stress felt by the user during operation.

[0087] The presentation unit can also optimize the interface for mobile devices such as tablets and smartphones. The presentation unit, for example, optimizes the interface for mobile devices such as tablets and smartphones. For example, the layout is automatically adjusted according to the screen size. The presentation unit also provides an interface optimized for mobile devices, allowing users to easily operate it anywhere. For example, the presentation unit provides an interface that supports touch operations. The presentation unit also builds a system that optimizes the interface for mobile devices. For example, the system employs a responsive design and adjusts the display according to the device. By optimizing the interface for mobile devices, the system allows users to easily operate it anywhere.

[0088] The presentation unit can add a voice assistant function to the user interface, enabling operation by voice. The presentation unit, for example, adds a voice assistant function to the user interface, enabling operation by voice. For example, the user gives voice instructions to change the design. The presentation unit also uses the voice assistant function to provide an interface that the user can operate by voice. For example, changing the color of wallpaper or the material of the floor with a voice command. The presentation unit also adds a voice assistant function to the user interface, building a system that enables operation by voice. For example, it uses voice recognition technology to analyze the user's instructions. In this way, adding the voice assistant function enables operation by voice, improving user convenience.

[0089] The presentation unit can use the emotion estimation function to automatically suggest an interface design that allows the user to operate most comfortably. The presentation unit, for example, uses the emotion estimation function to automatically suggest an interface design that allows the user to operate most comfortably. For example, if the user is relaxed, it suggests a calm design. The presentation unit also analyzes the user's emotions in real time and suggests an interface design that allows the user to operate most comfortably. For example, if the user is excited, it suggests a simple and intuitive design. The presentation unit also uses the emotion estimation function to build a system that automatically suggests an interface design that allows the user to operate most comfortably. For example, if the user is calm, it suggests a simple and calm design. This suggests an interface design that allows the user to operate most comfortably, thereby improving the user's operation experience.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The instruction input unit can enable user input of instructions using gestures or eye tracking. For example, a user can select a wallpaper color or floor material by moving their hand. Eye tracking technology can also be used to enable a user to input instructions using their gaze. For example, a wallpaper color or floor material can be selected using their gaze. Furthermore, voice input, gesture input, and eye tracking can be combined to enable a user to input instructions intuitively. For example, a wallpaper color can be specified by voice and a floor material can be selected by gesture. This allows for support of a variety of input methods, thereby improving user convenience.

[0092] The instruction input unit can analyze the tone and speed of the user's voice to estimate the user's emotional state and adjust the design suggestions accordingly. For example, when a user inputs an instruction such as "light blue wallpaper and wood-grain flooring," the tone and speed of the voice can be analyzed to estimate whether the user is relaxed or in a hurry. If the user is relaxed, a more subdued design can be suggested, and if the user is in a hurry, the results can be displayed quickly. The instruction input unit also analyzes the tone of the user's voice when inputting instructions to estimate the user's emotional state. For example, if the user is excited, a bright color or bold design can be suggested, and if the user is calm, a simple and subdued design can be suggested. The instruction input unit also analyzes the speed of the user's voice to estimate the user's emotional state. For example, if the user speaks slowly, detailed design suggestions can be made, and if the user speaks quickly, concise and intuitive design suggestions can be made. This enables design suggestions to be made according to the user's emotional state.

[0093] The instruction input unit can learn the user's past instruction history and predict and suggest a preferred design. For example, the instruction input unit learns the user's past instruction history and predicts a preferred design. For example, if the user previously instructed "white wallpaper and wood-grain flooring," the same design will be suggested the next time. The instruction input unit also predicts and suggests a preferred design based on the user's past instruction history. For example, if the user previously instructed "blue wallpaper and carpet flooring," the unit will suggest blue wallpaper and carpet the next time as well. The instruction input unit also analyzes the user's past instruction history and predicts a preferred design. For example, if the user previously instructed "green wallpaper and tile flooring," the unit will suggest green wallpaper and tiles the next time as well. This makes it possible to suggest designs based on the user's preferences.

[0094] The instruction input unit can use the emotion estimation function to automatically suggest design elements according to the user's emotions. For example, when a user inputs an instruction such as "light blue wallpaper and wood-grain flooring," the emotion estimation function is used to analyze the user's emotions and suggest design elements according to the emotions. For example, if the user is relaxed, a calm design is suggested. The instruction input unit also uses the emotion estimation function to automatically suggest design elements according to the user's emotions. For example, if the user is excited, a bright color or a bold design is suggested. The instruction input unit also analyzes the user's emotions in real time and suggests design elements according to the emotions. For example, if the user is calm, a simple and calm design is suggested. This makes it possible to suggest design elements according to the user's emotions.

[0095] The instruction input unit can enable instruction input using not only voice input but also gesture input and eye tracking. For example, the user can input instructions using not only voice input but also gesture input. For example, the user can select the wallpaper color or floor material by moving his or her hand. The instruction input unit can also use eye tracking technology to enable the user to input instructions using their gaze. For example, the user can select the wallpaper color or floor material by using their gaze. The instruction input unit can also combine voice input, gesture input, and eye tracking to enable the user to input instructions intuitively. For example, the user can specify the wallpaper color by voice and select the floor material by gesture. This allows for support for a variety of input methods, thereby improving user convenience.

[0096] The instruction input unit supports input of instructions in different languages, making it possible to accommodate international users. For example, it allows users to input instructions in different languages. For example, it realizes multilingual support such as English, French, and Chinese. The instruction input unit also supports input of instructions in different languages, making it possible to accommodate international users. For example, it allows users to input instructions in their native language. The instruction input unit also supports input of instructions in different languages ​​using multilingual natural language processing technology. For example, a user inputs instructions in Japanese, and the generation AI analyzes the instructions. This multilingual support makes it possible to use the system for international users.

[0097] The instruction input unit can use the emotion estimation function to analyze the emotion of the user when inputting instructions in real time, and provide feedback that elicits positive emotions. For example, the emotion of the user when inputting instructions can be analyzed in real time, and feedback that elicits positive emotions can be provided. For example, if the user is relaxed, an encouraging message can be displayed. The instruction input unit can also use the emotion estimation function to analyze the emotion of the user when inputting instructions, and provide feedback that elicits positive emotions. For example, if the user is excited, a calm message can be displayed. The instruction input unit can also analyze the emotion of the user in real time, and provide feedback that elicits positive emotions. For example, if the user is calm, simple and intuitive feedback can be provided. In this way, feedback that corresponds to the user's emotions can be provided, promoting a positive experience.

[0098] The analysis unit can perform customization taking into account the user's lifestyle and hobbies. For example, it analyzes and customizes design elements taking into account the user's lifestyle and hobbies. For example, for a user who likes the outdoors, it proposes a design that makes extensive use of natural materials. The analysis unit also customizes design elements based on the user's hobbies and interests. For example, for a user who likes music, it proposes a design that takes acoustic effects into consideration. The analysis unit also analyzes the user's lifestyle and customizes design elements based on that. For example, it proposes a design that emphasizes safety for a household with children. This makes it possible to customize according to the user's lifestyle and hobbies.

[0099] The analysis unit can reflect real-time user feedback on the design elements proposed by the generation AI. For example, if a user provides feedback such as "This wallpaper color is a little dark," the generation AI will instantly adjust the color. The analysis unit also allows the generation AI to adjust the design elements based on real-time user feedback. For example, if a user indicates that they would like to change the floor material, the generation AI will instantly suggest a new material. The analysis unit also builds a system that reflects user feedback in real time on the design elements proposed by the generation AI. For example, if a user provides feedback such as "I don't like this design," the generation AI will suggest a new design. This allows user feedback to be reflected in real time, enabling more accurate design suggestions.

[0100] The analysis unit can use the emotion estimation function to prioritize analysis of design elements based on the user's emotion. For example, the emotion estimation function is used to prioritize analysis of design elements based on the user's emotion. For example, if the user is relaxed, calm design elements are prioritized for analysis. The analysis unit also analyzes the user's emotion in real time and prioritizes analysis of design elements based on the emotion. For example, if the user is excited, bright colors and bold design elements are prioritized for analysis. The analysis unit also uses the emotion estimation function to build a system that prioritizes analysis of design elements based on the user's emotion. For example, if the user is calm, simple and calm design elements are prioritized for analysis. This makes it possible to analyze design elements based on the user's emotion.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The instruction input unit accepts instructions in natural language from the user. For example, the user inputs an instruction such as "The wallpaper should be light blue and the floor should have wood-grain flooring." Step 2: The analysis unit analyzes the instruction received by the instruction input unit. For example, the analysis unit uses natural language processing technology to analyze the user's instruction and extract design elements. Step 3: The generation unit generates a 3D model based on the design elements analyzed by the analysis unit. For example, the generation unit uses modeling software to generate a 3D model that reflects the color of wallpaper and the material of the floor. Step 4: The presentation unit generates and presents a 3D VR image based on the 3D model generated by the generation unit. For example, the presentation unit may use rendering technology to generate a 3D VR image with realistic textures and lighting effects, and present the image to the user through a VR device.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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. an instruction input unit that accepts instructions in a user's natural language; an analysis unit that analyzes the instruction received by the instruction input unit; a generation unit that generates a 3D model based on the design elements analyzed by the analysis unit; a presentation unit that generates and presents a 3D VR image based on the 3D model generated by the generation unit. A system characterized by:

2. The instruction input unit Analyzing the tone and rate of the user's voice to estimate their emotional state and adjust design suggestions 2. The system of claim 1.

3. The instruction input unit Learns the user's past instruction history, predicts the user's preferred designs, and makes suggestions 2. The system of claim 1.

4. The instruction input unit Automatically suggesting the design elements according to the user's emotions 2. The system of claim 1.

5. The instruction input unit Enables command input using gestures and eye tracking in addition to voice input 2. The system of claim 1.

6. The instruction input unit Supports input of commands in different languages ​​to accommodate international users 2. The system of claim 1.

Images