system

Abstract

The system according to the embodiment aims to enable a user to get a concrete idea of ​​the interior layout. [Solution] A system according to an embodiment includes a reception unit, a generation unit, an arrangement unit, and a customization unit. The reception unit allows a user to upload photos of their home. The generation unit analyzes the photos received by the reception unit and generates a virtual space. The arrangement unit arranges interior items within the virtual space generated by the generation unit. The customization unit allows a user to customize the interior items arranged by the arrangement unit.

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] With conventional technology, it was difficult to get a concrete idea of ​​the interior layout, making it difficult to make an appropriate decision before purchasing.

[0005] The system according to the embodiment aims to enable a user to get a concrete idea of ​​the interior layout. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an arrangement unit, and a customization unit. The reception unit allows a user to upload photos of their home. The generation unit analyzes the photos received by the reception unit and generates a virtual space. The arrangement unit arranges interior items within the virtual space generated by the generation unit. The customization unit allows a user to customize the interior items arranged by the arrangement unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable the user to get a concrete idea of ​​the interior layout. [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) An interior coordination system according to an embodiment of the present invention is an interior coordination service that combines AI and 3D modeling technology. This interior coordination system allows users to upload photos of their home and generate a virtual space, allowing them to interactively suggest and customize interior designs. First, the user uploads a photo of their home. Next, AI analyzes the photo and generates a virtual space using 3D modeling technology. Within this virtual space, the user can interact with the AI ​​and receive interior design suggestions. For example, if a user says, "I want to put a new sofa in my living room," the AI ​​can arrange various sofas in the virtual space and show the user the layout. Furthermore, the user can customize the color and shape of the sofa. This allows users to specifically see the interior layout before purchasing. For example, a system may be provided with a reception unit where users upload photos of their home. Next, a generation unit is provided that analyzes the photos received by the reception unit and generates a virtual space. The generation unit generates the virtual space using 3D modeling technology. Furthermore, a placement unit is provided that analyzes the user's requests and arranges interior items within the virtual space. The placement unit uses AI to analyze the user's requests and suggest interior items based on those requests. Finally, a customization unit is provided that accepts operations performed by the user when customizing the interior. The customization unit accepts user operations and customizes the interior in the virtual space based on those operations. This allows the interior coordination system to generate a virtual space and propose and customize interior designs simply by the user uploading a photo of their home.

[0029] An interior coordination system according to an embodiment includes a reception unit, a generation unit, an arrangement unit, and a customization unit. The reception unit allows a user to upload photos of their home. For example, the reception unit allows a user to upload photos of their home from a smartphone or PC. The reception unit can also save the uploaded photos and store them in a database for later analysis. The generation unit analyzes the uploaded photos and generates a virtual space. For example, the generation unit may identify objects in the photos using image recognition technology and generate a virtual space using 3D modeling technology. The generation unit may generate a realistic virtual space using CAD software or rendering technology. The arrangement unit uses AI to analyze the user's requests and propose interior designs based on the analysis. For example, the arrangement unit may understand the user's requests using natural language processing technology and arrange the interior designs in the virtual space. For example, if a user says, "I want to put a new sofa in my living room," the arrangement unit can arrange various sofas in the virtual space and show the arrangement to the user. The customization unit accepts user operations and customizes the interior design in the virtual space based on the operations. For example, the customization unit can accept an operation by the user to change the color or shape of a sofa and reflect the change in the virtual space. This allows the interior coordination system according to the embodiment to generate a virtual space and propose and customize interior designs simply by the user uploading a photo of their home.

[0030] The reception unit can analyze the user's past photo upload history and select the optimal upload method. For example, the reception unit can prioritize and suggest upload methods (e.g., drag and drop) that the user has frequently used in the past. For example, the reception unit can store the user's past upload history in a database and analyze that history. The reception unit can also analyze the resolution and file format of photos previously uploaded by the user and automatically apply optimal settings. For example, the reception unit can send a notification prompting the user to upload at a specific time period based on the user's past upload history. This makes it possible to provide the optimal upload method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history into a generation AI and have the generation AI select the optimal upload method.

[0031] The reception unit can filter photos based on the user's current interior style and preferences when uploading photos. The reception unit can filter uploaded photos, for example, based on interior styles previously selected by the user (e.g., modern, classic). For example, the reception unit can store the user's past interior style selection history in a database and analyze that history. The reception unit can also preferentially display photos containing specific colors or design elements based on the user's preferences. For example, the reception unit can filter related photos based on interior items previously rated by the user. This enables more appropriate interior suggestions by filtering photos based on the user's preferences. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past interior style selection history into a generation AI and have the generation AI perform photo filtering.

[0032] The generation unit can generate a virtual space using 3D modeling technology. The generation unit can generate the virtual space using, for example, CAD software. For example, the generation unit can analyze uploaded photos, identify objects in the photos, and create 3D models. The generation unit can also generate a realistic virtual space using rendering technology. For example, the generation unit can use rendering technology to realistically reproduce light reflections and shadows. Furthermore, the generation unit can adjust the level of detail of the virtual space according to the user's request. For example, if the user desires a detailed virtual space, the generation unit can generate the virtual space using high-resolution textures. This allows for the generation of a realistic virtual space using 3D modeling technology. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input uploaded photos into a generation AI and have the generation AI generate the virtual space.

[0033] When generating a virtual space, the generation unit can adjust the level of detail of the generated virtual space based on the resolution and quality of the photo. For example, when using high-resolution photos, the generation unit generates detailed textures and highly accurate models. For example, the generation unit can analyze the resolution of the uploaded photo and apply detailed textures to high-resolution photos. Furthermore, when using low-resolution photos, the generation unit can generate simple textures and basic models. For example, the generation unit can generate simplified models for low-resolution photos, thereby improving processing speed. Furthermore, the generation unit can automatically adjust the level of detail of the generated virtual space according to the quality of the photo. For example, the generation unit can evaluate the quality of the photo and generate a highly detailed virtual space for high-quality photos and a less detailed virtual space for low-quality photos. This allows the generation of an optimal virtual space according to the resolution and quality of the photo. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the resolution and quality of the uploaded photo into the generation AI and have the generation AI generate the virtual space.

[0034] The generation unit can apply different generation algorithms depending on the user's interior style when generating a virtual space. For example, the generation unit can apply a generation algorithm with a simple, linear design to a user who prefers a modern-style interior. For example, the generation unit can store the user's interior style preferences in a database and select a generation algorithm based on that information. The generation unit can also apply a generation algorithm with a decorative, curved design to a user who prefers a classic-style interior. For example, the generation unit can analyze the user's past interior style selection history and select an optimal generation algorithm. Furthermore, the generation unit can apply a generation algorithm with a simple, streamlined design to a user who prefers a minimalist-style interior. For example, the generation unit can automatically adjust the generation algorithm based on the user's interior style preferences. This allows the generation of an optimal virtual space according to the user's interior style. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's interior style preferences into the generation AI and have the generation AI select a generation algorithm.

[0035] The arrangement unit can use AI to analyze the user's requests and suggest interior design based on them. The arrangement unit can understand the user's requests using, for example, natural language processing technology and arrange the interior design in the virtual space. For example, if the user says, "I want to put a new sofa in my living room," the arrangement unit can arrange various sofas in the virtual space and show the arrangement to the user. The arrangement unit can also suggest interior design patterns based on the user's requests. For example, if the user says, "I want to put a new sofa in my living room," the arrangement unit can suggest multiple sofa design patterns and allow the user to select one. Furthermore, the arrangement unit can automatically adjust the interior design based on the user's requests. For example, if the user says, "I want to put a new sofa in my living room," the arrangement unit can automatically adjust the sofa design and suggest the optimal design. This makes it possible to suggest optimal interior design based on the user's requests by using AI. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the placement unit can input the user's requests into the generation AI and have the generation AI execute interior design suggestions.

[0036] When arranging the interior, the arrangement unit can select the optimal arrangement method by referring to the user's past interior selection history. The arrangement unit can, for example, propose the optimal arrangement method based on the arrangement patterns of interior items selected by the user in the past. For example, the arrangement unit can store the user's past interior selection history in a database and analyze that history. The arrangement unit can also preferentially propose a specific arrangement style based on the user's past selection history. For example, the arrangement unit can propose the optimal arrangement by referring to the arrangement methods of interior items that the user has previously evaluated. This makes it possible to propose the optimal interior arrangement based on the user's past selection history. Some or all of the above-described processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can input the user's past interior selection history into a generation AI and cause the generation AI to select the optimal arrangement method.

[0037] The arrangement unit can customize the arrangement method based on the user's current living situation when arranging the interior. For example, if the user has children, the arrangement unit can propose an arrangement that takes safety into consideration. For example, the arrangement unit can store the user's family composition and lifestyle habits in a database and customize the arrangement method based on that information. The arrangement unit can also propose a pet-friendly arrangement if the user has a pet. For example, the arrangement unit can propose an arrangement that allows the pet to live safely based on the user's living situation. Furthermore, the arrangement unit can propose a simple and functional arrangement if the user lives alone. For example, the arrangement unit can automatically adjust the optimal arrangement based on the user's living situation. This makes it possible to propose an optimal interior arrangement according to the user's living situation. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without AI. For example, the arrangement unit can input the user's living situation into the generation AI and cause the generation AI to customize the arrangement method.

[0038] The customization unit can accept user operations and customize the interior of the virtual space based on the operations. The customization unit can, for example, accept a user operation to change the color or shape of a sofa and reflect the change in the virtual space. For example, the customization unit can allow a user to select a sofa color through an interface and change the color of the sofa in the virtual space based on the selection. The customization unit can also accept a user operation to change the shape of the sofa and reflect the change in the virtual space. For example, the customization unit can allow a user to select a sofa shape and change the shape of the sofa in the virtual space based on the selection. The customization unit can also accept a user operation to change the layout of the interior and reflect the change in the virtual space. For example, the customization unit can accept a user operation to change the layout of the sofa and change the layout of the sofa in the virtual space based on the change. This makes it possible to customize the interior of the virtual space based on the user operation. Some or all of the above-described processing in the customization unit can be performed using, for example, AI, or without AI. For example, the customization unit can input user operations to a generation AI and cause the generation AI to customize the interior.

[0039] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. The customization unit can, for example, propose the optimal customization method based on customization options previously selected by the user. For example, the customization unit can store the user's past customization history in a database and analyze that history. The customization unit can also preferentially propose a specific design style based on the user's past customization history. For example, the customization unit can propose the optimal customization method by referring to customization options previously evaluated by the user. This makes it possible to provide the optimal customization method based on the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past customization history into a generation AI and cause the generation AI to select the optimal customization method.

[0040] During customization, the customization unit can customize the customization means based on the user's current living situation. For example, if the user is a family with children, the customization unit can provide customization options that take safety into consideration. For example, the customization unit can store the user's family composition and lifestyle habits in a database and customize the customization means based on that information. The customization unit can also provide pet-friendly customization options if the user has a pet. For example, the customization unit can provide customization options that ensure the safety of the pet based on the user's living situation. Furthermore, the customization unit can provide simple and functional customization options if the user lives alone. For example, the customization unit can automatically adjust the optimal customization based on the user's living situation. This makes it possible to provide an optimal customization method according to the user's living situation. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit can input the user's living situation into a generation AI and cause the generation AI to customize the customization means.

[0041] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, if the user lives in a cold region, the customization unit can provide customization options that emphasize warmth. For example, the customization unit can acquire the user's geographical location information from GPS data and select a customization method based on that information. Furthermore, if the user lives in a tropical region, the customization unit can provide customization options that emphasize coolness. For example, the customization unit can select a customization method that suits the local climatic conditions based on the user's geographical location information. Furthermore, if the user lives in an urban area, the customization unit can provide customization options that emphasize space efficiency. For example, the customization unit can select a customization method that makes effective use of limited space in urban areas based on the user's geographical location information. This makes it possible to provide the optimal customization method based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's geographical location information to a generation AI and cause the generation AI to select a customization method.

[0042] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can reflect interior ideas that the user has received many likes and comments on social media in the customization. For example, the customization unit can store the user's social media activity in a database and analyze that activity. The customization unit can also suggest customization options inspired by interior design accounts that the user follows. For example, the customization unit can suggest customization options related to specific events or seasons based on the user's social media activity. This makes it possible to provide an optimal customization method based on the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the user's social media activity into a generation AI and cause the generation AI to suggest customization methods.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The reception unit can analyze the user's past interior purchase history and suggest optimal interior items. For example, the reception unit can store data on interior items previously purchased by the user in a database and make new suggestions based on that data. The reception unit can also analyze the style and color of items previously purchased by the user and suggest new items based on that analysis. Furthermore, the reception unit can take into account the ratings of items previously purchased by the user and suggest new items similar to highly rated items. This makes it possible to suggest optimal interior items based on the user's past purchase history.

[0045] The layout unit can suggest interior layouts based on the user's lifestyle. For example, if the user works from home, the layout unit can suggest an efficient workspace layout. Also, if the user frequently invites friends over, the layout unit can suggest a sociable space layout. Furthermore, if the user spends a lot of time on a hobby, the layout unit can suggest a layout suitable for that hobby. This makes it possible to provide an optimal interior layout that suits the user's lifestyle.

[0046] The generation unit can adjust the appearance of the virtual space taking into account the user's geographical location information. For example, if the user lives in an urban area, the generation unit can reflect an urban landscape in the virtual space. Also, if the user lives in an area rich in nature, the generation unit can incorporate natural scenery into the virtual space. Furthermore, if the user lives by the sea, the generation unit can reflect an ocean view in the virtual space. This makes it possible to provide an optimal virtual space based on the user's geographical location information.

[0047] The customization unit can analyze the user's past customization history and suggest optimal customization options. For example, the customization unit can store customization options previously selected by the user in a database and make new suggestions based on that data. The customization unit can also take into account customization options previously rated by the user and suggest new options similar to highly rated options. Furthermore, the customization unit can analyze the style and color of customization options previously selected by the user and suggest new options based on that. This makes it possible to provide optimal customization options based on the user's past customization history.

[0048] The layout unit can analyze the user's social media activity and suggest interior layouts based on trends. For example, the layout unit can collect trend information from interior design accounts that the user follows and suggest layouts based on that information. The layout unit can also reflect interior ideas that have received many likes and comments on social media by the user. Furthermore, the layout unit can consider feedback from interior design communities in which the user participates and suggest layouts based on that feedback. This makes it possible to provide optimal interior layouts based on the user's social media activity.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The reception unit allows the user to upload photos of their home. For example, the user can upload photos of their home from a smartphone or computer. The reception unit also saves the uploaded photos and stores them in a database for later analysis. Step 2: The generation unit analyzes the photo received by the reception unit and generates a virtual space. For example, the generation unit may use image recognition technology to identify objects in the photo and 3D modeling technology to generate the virtual space. The generation unit may use CAD software or rendering technology to generate a realistic virtual space. Step 3: The placement unit places the interior design in the virtual space created by the generation unit. For example, the placement unit uses AI to analyze the user's requests and propose interior design ideas based on those requests. It uses natural language processing technology to understand the user's requests and place the interior design in the virtual space. Step 4: The customization unit allows the user to customize the interior space arranged by the arrangement unit. For example, the customization unit accepts user operations and customizes the interior space in the virtual space based on those operations. For example, the customization unit accepts operations by the user to change the color or shape of the sofa, and reflects those changes in the virtual space.

[0051] (Example 2) An interior coordination system according to an embodiment of the present invention is an interior coordination service that combines AI and 3D modeling technology. This interior coordination system allows users to upload photos of their home and generate a virtual space, allowing them to interactively suggest and customize interior designs. First, the user uploads a photo of their home. Next, AI analyzes the photo and generates a virtual space using 3D modeling technology. Within this virtual space, the user can interact with the AI ​​and receive interior design suggestions. For example, if a user says, "I want to put a new sofa in my living room," the AI ​​can arrange various sofas in the virtual space and show the user the layout. Furthermore, the user can customize the color and shape of the sofa. This allows users to specifically see the interior layout before purchasing. For example, a system may be provided with a reception unit where users upload photos of their home. Next, a generation unit is provided that analyzes the photos received by the reception unit and generates a virtual space. The generation unit generates the virtual space using 3D modeling technology. Furthermore, a placement unit is provided that analyzes the user's requests and arranges interior items within the virtual space. The placement unit uses AI to analyze the user's requests and suggest interior items based on those requests. Finally, a customization unit is provided that accepts operations performed by the user when customizing the interior. The customization unit accepts user operations and customizes the interior in the virtual space based on those operations. This allows the interior coordination system to generate a virtual space and propose and customize interior designs simply by the user uploading a photo of their home.

[0052] An interior coordination system according to an embodiment includes a reception unit, a generation unit, an arrangement unit, and a customization unit. The reception unit allows a user to upload photos of their home. For example, the reception unit allows a user to upload photos of their home from a smartphone or PC. The reception unit can also save the uploaded photos and store them in a database for later analysis. The generation unit analyzes the uploaded photos and generates a virtual space. For example, the generation unit may identify objects in the photos using image recognition technology and generate a virtual space using 3D modeling technology. The generation unit may generate a realistic virtual space using CAD software or rendering technology. The arrangement unit uses AI to analyze the user's requests and propose interior designs based on the analysis. For example, the arrangement unit may understand the user's requests using natural language processing technology and arrange the interior designs in the virtual space. For example, if a user says, "I want to put a new sofa in my living room," the arrangement unit can arrange various sofas in the virtual space and show the arrangement to the user. The customization unit accepts user operations and customizes the interior design in the virtual space based on the operations. For example, the customization unit can accept an operation by the user to change the color or shape of a sofa and reflect the change in the virtual space. This allows the interior coordination system according to the embodiment to generate a virtual space and propose and customize interior designs simply by the user uploading a photo of their home.

[0053] The reception unit can estimate the user's emotions and adjust the timing of photo uploads based on the estimated user emotions. For example, if the user is relaxed, the reception unit can send a notification prompting the user to upload a photo. For example, the reception unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. Furthermore, if the user is feeling stressed, the reception unit can simplify the upload procedure and enable it to be completed quickly. For example, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the reception unit can add interactive elements to make the upload process more enjoyable. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the user to be prompted to upload a photo at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0054] The reception unit can analyze the user's past photo upload history and select the optimal upload method. For example, the reception unit can prioritize and suggest upload methods (e.g., drag and drop) that the user has frequently used in the past. For example, the reception unit can store the user's past upload history in a database and analyze that history. The reception unit can also analyze the resolution and file format of photos previously uploaded by the user and automatically apply optimal settings. For example, the reception unit can send a notification prompting the user to upload at a specific time period based on the user's past upload history. This makes it possible to provide the optimal upload method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history into a generation AI and have the generation AI select the optimal upload method.

[0055] The reception unit can filter photos based on the user's current interior style and preferences when uploading photos. The reception unit can filter uploaded photos, for example, based on interior styles previously selected by the user (e.g., modern, classic). For example, the reception unit can store the user's past interior style selection history in a database and analyze that history. The reception unit can also preferentially display photos containing specific colors or design elements based on the user's preferences. For example, the reception unit can filter related photos based on interior items previously rated by the user. This enables more appropriate interior suggestions by filtering photos based on the user's preferences. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past interior style selection history into a generation AI and have the generation AI perform photo filtering.

[0056] The generation unit can generate a virtual space using 3D modeling technology. The generation unit can generate the virtual space using, for example, CAD software. For example, the generation unit can analyze uploaded photos, identify objects in the photos, and create 3D models. The generation unit can also generate a realistic virtual space using rendering technology. For example, the generation unit can use rendering technology to realistically reproduce light reflections and shadows. Furthermore, the generation unit can adjust the level of detail of the virtual space according to the user's request. For example, if the user desires a detailed virtual space, the generation unit can generate the virtual space using high-resolution textures. This allows for the generation of a realistic virtual space using 3D modeling technology. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input uploaded photos into a generation AI and have the generation AI generate the virtual space.

[0057] The generation unit can estimate the user's emotions and adjust the method for generating the virtual space based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a virtual space with soft colors and a calm design. For example, the generation unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. Furthermore, if the user is stressed, the generation unit can generate a virtual space with a simple and uncluttered design. For example, the generation unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the generation unit can generate a virtual space with vivid colors and a dynamic design. For example, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the generation of an optimal virtual space based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0058] When generating a virtual space, the generation unit can adjust the level of detail of the generated virtual space based on the resolution and quality of the photo. For example, when using high-resolution photos, the generation unit generates detailed textures and highly accurate models. For example, the generation unit can analyze the resolution of the uploaded photo and apply detailed textures to high-resolution photos. Furthermore, when using low-resolution photos, the generation unit can generate simple textures and basic models. For example, the generation unit can generate simplified models for low-resolution photos, thereby improving processing speed. Furthermore, the generation unit can automatically adjust the level of detail of the generated virtual space according to the quality of the photo. For example, the generation unit can evaluate the quality of the photo and generate a highly detailed virtual space for high-quality photos and a less detailed virtual space for low-quality photos. This allows the generation of an optimal virtual space according to the resolution and quality of the photo. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the resolution and quality of the uploaded photo into the generation AI and have the generation AI generate the virtual space.

[0059] The generation unit can apply different generation algorithms depending on the user's interior style when generating a virtual space. For example, the generation unit can apply a generation algorithm with a simple, linear design to a user who prefers a modern-style interior. For example, the generation unit can store the user's interior style preferences in a database and select a generation algorithm based on that information. The generation unit can also apply a generation algorithm with a decorative, curved design to a user who prefers a classic-style interior. For example, the generation unit can analyze the user's past interior style selection history and select an optimal generation algorithm. Furthermore, the generation unit can apply a generation algorithm with a simple, streamlined design to a user who prefers a minimalist-style interior. For example, the generation unit can automatically adjust the generation algorithm based on the user's interior style preferences. This allows the generation of an optimal virtual space according to the user's interior style. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's interior style preferences into the generation AI and have the generation AI select a generation algorithm.

[0060] The arrangement unit can use AI to analyze the user's requests and suggest interior design based on them. The arrangement unit can understand the user's requests using, for example, natural language processing technology and arrange the interior design in the virtual space. For example, if the user says, "I want to put a new sofa in my living room," the arrangement unit can arrange various sofas in the virtual space and show the arrangement to the user. The arrangement unit can also suggest interior design patterns based on the user's requests. For example, if the user says, "I want to put a new sofa in my living room," the arrangement unit can suggest multiple sofa design patterns and allow the user to select one. Furthermore, the arrangement unit can automatically adjust the interior design based on the user's requests. For example, if the user says, "I want to put a new sofa in my living room," the arrangement unit can automatically adjust the sofa design and suggest the optimal design. This makes it possible to suggest optimal interior design based on the user's requests by using AI. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the placement unit can input the user's requests into the generation AI and have the generation AI execute interior design suggestions.

[0061] The arrangement unit can estimate the user's emotions and adjust the interior layout based on the estimated user emotions. For example, if the user is relaxed, the arrangement unit can suggest soft colors and a calm layout. For example, the arrangement unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. Furthermore, if the user is feeling stressed, the arrangement unit can suggest a simple and organized layout. For example, the arrangement unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the arrangement unit can suggest vivid colors and a dynamic layout. For example, the arrangement unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This makes it possible to suggest an optimal interior layout based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0062] When arranging the interior, the arrangement unit can select the optimal arrangement method by referring to the user's past interior selection history. The arrangement unit can, for example, propose the optimal arrangement method based on the arrangement patterns of interior items selected by the user in the past. For example, the arrangement unit can store the user's past interior selection history in a database and analyze that history. The arrangement unit can also preferentially propose a specific arrangement style based on the user's past selection history. For example, the arrangement unit can propose the optimal arrangement by referring to the arrangement methods of interior items that the user has previously evaluated. This makes it possible to propose the optimal interior arrangement based on the user's past selection history. Some or all of the above-described processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can input the user's past interior selection history into a generation AI and cause the generation AI to select the optimal arrangement method.

[0063] The arrangement unit can customize the arrangement method based on the user's current living situation when arranging the interior. For example, if the user has children, the arrangement unit can propose an arrangement that takes safety into consideration. For example, the arrangement unit can store the user's family composition and lifestyle habits in a database and customize the arrangement method based on that information. The arrangement unit can also propose a pet-friendly arrangement if the user has a pet. For example, the arrangement unit can propose an arrangement that allows the pet to live safely based on the user's living situation. Furthermore, the arrangement unit can propose a simple and functional arrangement if the user lives alone. For example, the arrangement unit can automatically adjust the optimal arrangement based on the user's living situation. This makes it possible to propose an optimal interior arrangement according to the user's living situation. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without AI. For example, the arrangement unit can input the user's living situation into the generation AI and cause the generation AI to customize the arrangement method.

[0064] The customization unit can accept user operations and customize the interior of the virtual space based on the operations. The customization unit can, for example, accept a user operation to change the color or shape of a sofa and reflect the change in the virtual space. For example, the customization unit can allow a user to select a sofa color through an interface and change the color of the sofa in the virtual space based on the selection. The customization unit can also accept a user operation to change the shape of the sofa and reflect the change in the virtual space. For example, the customization unit can allow a user to select a sofa shape and change the shape of the sofa in the virtual space based on the selection. The customization unit can also accept a user operation to change the layout of the interior and reflect the change in the virtual space. For example, the customization unit can accept a user operation to change the layout of the sofa and change the layout of the sofa in the virtual space based on the change. This makes it possible to customize the interior of the virtual space based on the user operation. Some or all of the above-described processing in the customization unit can be performed using, for example, AI, or without AI. For example, the customization unit can input user operations to a generation AI and cause the generation AI to customize the interior.

[0065] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. For example, if the user is relaxed, the customization unit can provide customization options with soft colors and a calm design. For example, the customization unit can capture the user's facial expressions with a camera and estimate the user's emotions using facial expression recognition technology. Furthermore, if the user is stressed, the customization unit can provide simple and intuitive customization options. For example, the customization unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the customization unit can provide customization options with vivid colors and a dynamic design. For example, the customization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This makes it possible to provide an optimal customization method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0066] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. The customization unit can, for example, propose the optimal customization method based on customization options previously selected by the user. For example, the customization unit can store the user's past customization history in a database and analyze that history. The customization unit can also preferentially propose a specific design style based on the user's past customization history. For example, the customization unit can propose the optimal customization method by referring to customization options previously evaluated by the user. This makes it possible to provide the optimal customization method based on the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past customization history into a generation AI and cause the generation AI to select the optimal customization method.

[0067] During customization, the customization unit can customize the customization means based on the user's current living situation. For example, if the user is a family with children, the customization unit can provide customization options that take safety into consideration. For example, the customization unit can store the user's family composition and lifestyle habits in a database and customize the customization means based on that information. The customization unit can also provide pet-friendly customization options if the user has a pet. For example, the customization unit can provide customization options that ensure the safety of the pet based on the user's living situation. Furthermore, the customization unit can provide simple and functional customization options if the user lives alone. For example, the customization unit can automatically adjust the optimal customization based on the user's living situation. This makes it possible to provide an optimal customization method according to the user's living situation. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit can input the user's living situation into a generation AI and cause the generation AI to customize the customization means.

[0068] The customization unit can estimate the user's emotions and determine customization priorities based on the estimated user emotions. For example, if the user is relaxed, the customization unit can prioritize customization of the living room. For example, the customization unit can capture the user's facial expressions with a camera and estimate the user's emotions using facial expression recognition technology. Furthermore, if the user is feeling stressed, the customization unit can prioritize customization of the bedroom. For example, the customization unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the customization unit can prioritize customization of the entertainment area. For example, the customization unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows for optimal customization priorities to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0069] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, if the user lives in a cold region, the customization unit can provide customization options that emphasize warmth. For example, the customization unit can acquire the user's geographical location information from GPS data and select a customization method based on that information. Furthermore, if the user lives in a tropical region, the customization unit can provide customization options that emphasize coolness. For example, the customization unit can select a customization method that suits the local climatic conditions based on the user's geographical location information. Furthermore, if the user lives in an urban area, the customization unit can provide customization options that emphasize space efficiency. For example, the customization unit can select a customization method that makes effective use of limited space in urban areas based on the user's geographical location information. This makes it possible to provide the optimal customization method based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's geographical location information to a generation AI and cause the generation AI to select a customization method.

[0070] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can reflect interior ideas that the user has received many likes and comments on social media in the customization. For example, the customization unit can store the user's social media activity in a database and analyze that activity. The customization unit can also suggest customization options inspired by interior design accounts that the user follows. For example, the customization unit can suggest customization options related to specific events or seasons based on the user's social media activity. This makes it possible to provide an optimal customization method based on the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the user's social media activity into a generation AI and cause the generation AI to suggest customization methods. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, arrangement unit, and customization unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's photo using the camera 42 or microphone 38B of the smart device 14, and the photo is analyzed by the specific processing unit 290 of the data processing device 12. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the uploaded photo and generates a virtual space. The arrangement unit, realized, for example, by the control unit 46A of the smart device 14, proposes an interior design based on the user's requests. The customization unit, realized, for example, by the control unit 46A of the smart device 14, accepts user operations and customizes the interior design in the virtual space. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, arrangement unit, and customization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's photo using the camera 42 or microphone 238 of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the uploaded photo and generates a virtual space. The arrangement unit, realized, for example, by the control unit 46A of the smart glasses 214, suggests interior design based on the user's requests. The customization unit, realized, for example, by the control unit 46A of the smart glasses 214, accepts user operations and customizes the interior design in the virtual space. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, arrangement unit, and customization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives a user's photo using the camera 42 or microphone 238 of the headset-type terminal 314, and the photo is analyzed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the uploaded photo, and generates a virtual space. The arrangement unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and proposes interior design based on the user's requests. The customization unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and customizes the interior design in the virtual space by accepting user operations. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, placement unit, and customization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's photo using the camera 42 or microphone 238 of the robot 414, which is analyzed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the uploaded photo, and generates a virtual space. The placement unit is realized, for example, by the control unit 46A of the robot 414, and proposes interior design based on the user's requests. The customization unit is realized, for example, by the control unit 46A of the robot 414, and customizes the interior design in the virtual space by accepting user operations.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The reception unit can analyze the user's past interior purchase history and suggest optimal interior items. For example, the reception unit can store data on interior items previously purchased by the user in a database and make new suggestions based on that data. The reception unit can also analyze the style and color of items previously purchased by the user and suggest new items based on that analysis. Furthermore, the reception unit can take into account the ratings of items previously purchased by the user and suggest new items similar to highly rated items. This makes it possible to suggest optimal interior items based on the user's past purchase history.

[0073] The generation unit can estimate the user's emotion and adjust the lighting of the virtual space based on the estimated user's emotion. For example, the generation unit can illuminate the virtual space using soft light if the user is relaxed. The generation unit can also use lighting with a subdued color tone if the user is stressed. Furthermore, the generation unit can use bright, vibrant lighting if the user is excited. This makes it possible to provide an optimal lighting environment according to the user's emotion.

[0074] The layout unit can suggest interior layouts based on the user's lifestyle. For example, if the user works from home, the layout unit can suggest an efficient workspace layout. Also, if the user frequently invites friends over, the layout unit can suggest a sociable space layout. Furthermore, if the user spends a lot of time on a hobby, the layout unit can suggest a layout suitable for that hobby. This makes it possible to provide an optimal interior layout that suits the user's lifestyle.

[0075] The customization unit can estimate the user's emotion and adjust the customization interface based on the estimated user's emotion. For example, the customization unit can provide a simple and intuitive interface when the user is relaxed. The customization unit can also provide a guided interface when the user is stressed. Furthermore, the customization unit can also provide an interactive and fun interface when the user is excited. This makes it possible to provide an optimal customization experience according to the user's emotion.

[0076] The generation unit can adjust the appearance of the virtual space taking into account the user's geographical location information. For example, if the user lives in an urban area, the generation unit can reflect an urban landscape in the virtual space. Also, if the user lives in an area rich in nature, the generation unit can incorporate natural scenery into the virtual space. Furthermore, if the user lives by the sea, the generation unit can reflect an ocean view in the virtual space. This makes it possible to provide an optimal virtual space based on the user's geographical location information.

[0077] The arrangement unit can estimate the user's emotions and dynamically change the interior arrangement based on the estimated user's emotions. For example, if the user is relaxed, the arrangement unit can change the interior arrangement to one that helps relaxation. If the user is stressed, the arrangement unit can change the interior arrangement to one that reduces stress. Furthermore, if the user is excited, the arrangement unit can change the interior arrangement to one that is lively. In this way, it is possible to provide an optimal interior arrangement according to the user's emotions.

[0078] The customization unit can analyze the user's past customization history and suggest optimal customization options. For example, the customization unit can store customization options previously selected by the user in a database and make new suggestions based on that data. The customization unit can also take into account customization options previously rated by the user and suggest new options similar to highly rated options. Furthermore, the customization unit can analyze the style and color of customization options previously selected by the user and suggest new options based on that. This makes it possible to provide optimal customization options based on the user's past customization history.

[0079] The generator can estimate the user's emotions and adjust the acoustic environment of the virtual space based on the estimated user's emotions. For example, if the user is relaxed, the generator can play calm music in the virtual space. If the user is stressed, the generator can play relaxing natural sounds. If the user is excited, the generator can play lively music. This makes it possible to provide an optimal acoustic environment according to the user's emotions.

[0080] The layout unit can analyze the user's social media activity and suggest interior layouts based on trends. For example, the layout unit can collect trend information from interior design accounts that the user follows and suggest layouts based on that information. The layout unit can also reflect interior ideas that have received many likes and comments on social media by the user. Furthermore, the layout unit can consider feedback from interior design communities in which the user participates and suggest layouts based on that feedback. This makes it possible to provide optimal interior layouts based on the user's social media activity.

[0081] The customization unit can estimate the user's emotion and provide customized feedback based on the estimated user's emotion. For example, the customization unit can provide positive feedback when the user is relaxed. The customization unit can also provide encouraging feedback when the user is stressed. Furthermore, the customization unit can also provide energetic feedback when the user is excited. In this way, optimal feedback can be provided according to the user's emotion.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The reception unit allows the user to upload photos of their home. For example, the user can upload photos of their home from a smartphone or computer. The reception unit also saves the uploaded photos and stores them in a database for later analysis. Step 2: The generation unit analyzes the photo received by the reception unit and generates a virtual space. For example, the generation unit may use image recognition technology to identify objects in the photo and 3D modeling technology to generate the virtual space. The generation unit may use CAD software or rendering technology to generate a realistic virtual space. Step 3: The placement unit places the interior design in the virtual space created by the generation unit. For example, the placement unit uses AI to analyze the user's requests and propose interior design ideas based on those requests. It uses natural language processing technology to understand the user's requests and place the interior design in the virtual space. Step 4: The customization unit allows the user to customize the interior space arranged by the arrangement unit. For example, the customization unit accepts user operations and customizes the interior space in the virtual space based on those operations. For example, the customization unit accepts operations by the user to change the color or shape of the sofa, and reflects those changes in the virtual space.

[0084] 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.

[0085] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0086] 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.

[0087] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

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

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section where users upload photos of their homes; a generation unit that analyzes the photo received by the reception unit and generates a virtual space; a placement unit that places an interior in the virtual space generated by the generation unit; a customization unit that allows a user to customize the interior arranged by the arrangement unit. A system characterized by:

2. The reception unit Estimate user emotions and adjust the timing of photo uploads based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyze the user's past photo upload history and select the optimal upload method 2. The system of claim 1.

4. The reception unit Filter photos as they are uploaded based on the user's current interior style and preferences 2. The system of claim 1.

5. The generation unit Creating a virtual space using 3D modeling technology 2. The system of claim 1.

6. The generation unit Estimate the user's emotions and adjust the method of generating the virtual space based on the estimated user emotions.

2. The system of claim 1.

7. The generation unit When generating a virtual space, adjust the level of detail based on the resolution and quality of the photo.

2. The system of claim 1.

8. The generation unit When generating a virtual space, different generation algorithms are applied depending on the user's interior style.

2. The system of claim 1.

9. The placement unit Using AI to analyze user needs and propose interior design ideas based on those needs 2. The system of claim 1.

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