System

Abstract

An object of a system according to an embodiment is to enable a user to quickly propose an interior design that matches the characteristics of a room.SOLUTION: A system according to an embodiment includes a photograph / design drawing upload unit, an image analysis unit, a customization unit, and a design display unit. The photograph / design drawing upload unit uploads a photograph or a design drawing of a room from a user. The image analysis unit analyzes the photograph or the design drawing uploaded by the photograph / design drawing upload unit. The customization unit generates a design in accordance with the characteristics of the room on the basis of the result analyzed by the image analysis unit. The design display unit displays the design generated by the customization unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to quickly and effectively propose interior designs that match the characteristics of a room.

[0005] The system according to the embodiment aims to enable a user to quickly propose an interior design that matches the characteristics of a room. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo and blueprint uploading unit, an image analysis unit, a customization unit, and a design display unit. The photo and blueprint uploading unit allows a user to upload photos and blueprints of a room. The image analysis unit analyzes the photos and blueprints uploaded by the photo and blueprint uploading unit. The customization unit generates a design that matches the characteristics of the room based on the results of the analysis by the image analysis unit. The design display unit displays the design generated by the customization unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to quickly propose an interior design that matches the characteristics of the room. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​interior concierge according to an embodiment of the present invention is a system that allows users to upload photos and blueprints of their rooms, which are then analyzed by a generative AI to provide customized design advice tailored to the characteristics of the room. This allows the AI ​​interior concierge to easily improve the interior design of their own rooms.

[0029] An AI interior concierge according to an embodiment includes a photo and blueprint uploading unit, an image analysis unit, a customization unit, and a design display unit. The photo and blueprint uploading unit allows a user to upload photos and blueprints of a room. For example, photos and blueprints can be uploaded in JPEG, PNG, or PDF format. The image analysis unit analyzes the uploaded photos and blueprints. For example, it can analyze the furniture layout using object recognition technology and measure the room size using dimension measurement technology. It can also analyze the room's color using color analysis technology. The customization unit generates a design tailored to the characteristics of the room based on the results of the analysis by the image analysis unit. For example, it proposes furniture layouts that make efficient use of space for small rooms and designs that create an open feeling for large rooms. It can also provide designs tailored to different room uses, such as living rooms, kitchens, and bedrooms. The design display unit displays the design generated by the customization unit to the user. For example, it displays the proposed furniture layouts and color combinations to the user so that the user can confirm them. This allows the AI ​​interior concierge in this embodiment to have users upload photos and blueprints of their rooms, which the generating AI will analyze and suggest designs that suit the characteristics of the room.

[0030] The photo and blueprint uploading unit can automatically measure room dimensions and furniture sizes. For example, when a user uploads a photo of a room, the generating AI automatically measures the room dimensions and recognizes the furniture sizes. For example, when a user uploads a photo of a living room, the generating AI measures the length, width, and height of the room and automatically recognizes the sizes of the sofa and table. Furthermore, when a blueprint is uploaded, the generating AI automatically reads the dimensions and analyzes the room layout. For example, when a kitchen blueprint is uploaded, the generating AI automatically recognizes the position and size of the counter and sink. Furthermore, when a user takes and uploads a photo of a room with their smartphone, the generating AI measures the dimensions in real time and recognizes the furniture sizes. For example, when a photo of a bedroom is taken, the generating AI automatically measures the size of the bed and closet. This automatically measures the room dimensions and furniture sizes, enabling more accurate design proposals.

[0031] The photo / blueprint uploading unit is provided with a form for inputting the user's lifestyle and preferences, and can make design suggestions based on the input. For example, when a user uploads a photo of a room, the photo / blueprint uploading unit is provided with a form for inputting the user's lifestyle and preferences. For example, when uploading a photo of a living room, the user enters information such as family composition, hobbies, and favorite colors. Furthermore, when uploading a blueprint, the unit is provided with a form for inputting the user's lifestyle and preferences, and can make design suggestions based on the input. For example, when uploading a kitchen blueprint, the user enters information such as how often they cook and what types of food they like. Furthermore, when uploading a photo of a room, the unit is provided with a form for inputting the user's lifestyle and preferences, and can make design suggestions based on the input. For example, when uploading a photo of a bedroom, the user enters information such as their sleep quality and favorite interior style. This makes it possible to make design suggestions based on the user's lifestyle and preferences.

[0032] The photo / blueprint uploading unit may have a function that allows room information to be provided using voice input or gesture input. The photo / blueprint uploading unit allows, for example, when a user uploads a photo of a room, room information to be provided using voice input. For example, when uploading a photo of a living room, the user may explain by voice, "This room is a place where the family gathers." Furthermore, when uploading a blueprint, room information can be provided by gesture input. For example, when uploading a kitchen blueprint, the user may explain, "I want to install a counter here," while pointing to a specific location with their finger. Furthermore, when uploading a photo of a room, the user may provide room information by voice input or gesture input. For example, when uploading a photo of a bedroom, the user may explain by voice, "I want this room to be a relaxing space," and indicate the location of the bed with a gesture. In this way, room information can be provided more intuitively using voice input or gesture input.

[0033] The photo and blueprint uploading unit may have a function that enables seamless uploading from different devices. For example, the photo and blueprint uploading unit allows a user to upload photos of a room from a smartphone and then log in with the same account from a tablet or PC to seamlessly receive design proposals. For example, a user may upload photos of a living room from a smartphone and then check the design proposals on a PC. Also, a user may upload blueprints from a tablet and then log in with the same account from a smartphone or PC to seamlessly receive design proposals. For example, a user may upload kitchen blueprints from a tablet and then check the design proposals on a smartphone. Furthermore, a user may upload photos of a room from a PC and then log in with the same account from a smartphone or tablet to seamlessly receive design proposals. For example, a user may upload photos of a bedroom from a PC and then check the design proposals on a tablet. This allows seamless uploading from different devices, improving user convenience.

[0034] The image analysis unit can make optimal design suggestions by taking into account the lighting conditions and time of day in the room. For example, when the generative AI analyzes photos of a room, the image analysis unit makes design suggestions by taking into account the lighting conditions and time of day. For example, it can analyze photos of a living room and propose designs that suit the lighting conditions during the day and at night. In addition, when analyzing blueprints, it makes design suggestions by taking into account the lighting conditions and time of day in the room. For example, it can analyze blueprints of a kitchen and propose designs that suit the lighting conditions at breakfast and dinner time. In addition, when the generative AI analyzes photos of a room, it makes design suggestions by taking into account the lighting conditions and time of day. For example, it can analyze photos of a bedroom and propose designs that suit the lighting conditions at bedtime and when waking up. This makes it possible to make more appropriate design suggestions by taking into account the lighting conditions and time of day.

[0035] The image analysis unit can make design proposals that incorporate the historical background and cultural elements of a room. For example, when the generative AI analyzes a photo of a room, the image analysis unit makes design proposals that incorporate the historical background and cultural elements of the room. For example, it can analyze a photo of a living room and propose a design that incorporates traditional furniture and decorations. In addition, when analyzing a blueprint, it makes design proposals that incorporate the historical background and cultural elements of the room. For example, it can analyze a kitchen blueprint and propose a design that incorporates traditional designs from the region. In addition, when the generative AI analyzes a photo of a room, it makes design proposals that incorporate the historical background and cultural elements of the room. For example, it can analyze a photo of a bedroom and propose a design that incorporates cultural decorations and furniture. In this way, by incorporating historical background and cultural elements, it becomes possible to make more unique design proposals.

[0036] The image analysis unit can refer to the design history of other users and make design suggestions that incorporate trends. For example, when the generation AI analyzes photos of a room, the image analysis unit refers to the design history of other users and makes design suggestions that incorporate the latest trends. For example, it analyzes photos of living rooms and suggests popular furniture arrangements and colors. Also, when analyzing blueprints, it refers to the design history of other users and makes design suggestions that incorporate trends. For example, it analyzes kitchen blueprints and suggests layouts and decorations based on the latest trends. Furthermore, when the generation AI analyzes photos of a room, it refers to the design history of other users and makes design suggestions that incorporate trends. For example, it analyzes photos of bedrooms and suggests trendy bed arrangements and colors. In this way, by referring to the design history of other users, it is possible to make design suggestions that incorporate the latest trends.

[0037] The image analysis unit can make design suggestions that match different seasons and events. For example, when the generative AI analyzes a photo of a room, the image analysis unit makes design suggestions that match the season. For example, it can analyze a photo of a living room and suggest bright colors in spring and warm colors in winter. Also, when analyzing a blueprint, it can make design suggestions that match events. For example, it can analyze a kitchen blueprint and suggest decorations that match events such as Christmas and Halloween. Furthermore, when the generative AI analyzes a photo of a room, it can make design suggestions that match the season and events. For example, it can analyze a photo of a bedroom and suggest cool colors in summer and muted colors in autumn. This makes it possible to make design suggestions that match different seasons and events.

[0038] When analyzing the characteristics of a room, the customization unit takes into account the material and texture of the furniture and can suggest the optimal layout. For example, when the generative AI analyzes a photo of a room, the customization unit takes into account the material and texture of the furniture and suggests the optimal layout. For example, it analyzes a photo of a living room and suggests a combination of wooden furniture and a fabric sofa. Also, when analyzing a blueprint, it takes into account the material and texture of the furniture and suggests the optimal layout. For example, it analyzes a kitchen blueprint and suggests a combination of a stainless steel counter and wooden cabinets. Furthermore, when the generative AI analyzes a photo of a room, it takes into account the material and texture of the furniture and suggests the optimal layout. For example, it analyzes a photo of a bedroom and suggests a combination of a metal bed frame and silk curtains. This makes it possible to suggest more appropriate layouts by taking into account the material and texture of the furniture.

[0039] The customization unit can suggest eco-friendly materials and designs when analyzing the characteristics of a room. For example, when the generative AI analyzes a photo of a room, the customization unit suggests eco-friendly materials and designs. For example, it can analyze a photo of a living room and suggest furniture made from recycled wood and energy-efficient lighting. It can also suggest eco-friendly materials and designs when analyzing a blueprint. For example, it can analyze a kitchen blueprint and suggest countertops made from recycled materials and energy-efficient appliances. It can also suggest eco-friendly materials and designs when the generative AI analyzes a photo of a room. For example, it can analyze a photo of a bedroom and suggest organic cotton bed linen and carpets made from natural materials. This makes it possible to suggest eco-friendly materials and designs, enabling environmentally conscious interior design.

[0040] When analyzing the characteristics of a room, the customization unit can make design suggestions that take into account the safety of pets and children. For example, when the generation AI analyzes a photo of a room, the customization unit makes design suggestions that take into account the safety of pets and children. For example, it analyzes a photo of a living room and suggests furniture with no corners and non-slip flooring. In addition, when analyzing a blueprint, it makes design suggestions that take into account the safety of pets and children. For example, it analyzes a blueprint of a kitchen and suggests storage that is located high up so that children cannot reach it, and spaces where pets can stay safely. In addition, when the generation AI analyzes a photo of a room, it makes design suggestions that take into account the safety of pets and children. For example, it analyzes a photo of a bedroom and suggests a bed that is difficult for children to fall out of and a sleeping area where pets can stay comfortably. This makes it possible to make design suggestions that take into account the safety of pets and children.

[0041] When analyzing the characteristics of a room, the customization unit can make design proposals that take into account integration with smart home devices. For example, when the generation AI analyzes photos of a room, the customization unit makes design proposals that take into account integration with smart home devices. For example, it analyzes photos of a living room and proposes optimal placement for smart lighting and voice assistants. In addition, when analyzing blueprints, it makes design proposals that take into account integration with smart home devices. For example, it analyzes blueprints of a kitchen and proposes optimal placement for smart appliances and sensors. In addition, when the generation AI analyzes photos of a room, it makes design proposals that take into account integration with smart home devices. For example, it analyzes photos of a bedroom and proposes optimal placement for smart curtains and temperature control devices. This makes it possible to make design proposals that take into account integration with smart home devices.

[0042] The design display unit displays the proposed design as a 3D model, allowing the user to experience it in virtual reality. The design display unit, for example, displays the proposed design as a 3D model, allowing the user to experience it in virtual reality. For example, a living room design may be displayed as a 3D model, allowing the user to experience it in virtual reality using a VR headset. A proposed design based on a blueprint may also be displayed as a 3D model, allowing the user to experience it in virtual reality. For example, a kitchen design may be displayed as a 3D model, allowing the user to experience it in virtual reality using a VR headset. Furthermore, the proposed design may also be displayed as a 3D model, allowing the user to experience it in virtual reality. For example, a bedroom design may be displayed as a 3D model, allowing the user to experience it in virtual reality using a VR headset. In this way, the proposed design may be displayed as a 3D model, allowing the user to more specifically confirm the design by experiencing it in virtual reality.

[0043] The design display unit collects user feedback on the proposed design in real time, and the generation AI can improve the design based on that. For example, the design display unit collects user feedback on the proposed design in real time, and the generation AI improves the design based on that. For example, it collects user opinions on the living room design and adjusts the furniture arrangement and colors. It also collects user feedback on a proposed design based on a blueprint in real time, and the generation AI improves the design based on that. For example, it collects user opinions on the kitchen design and adjusts the layout and decoration. It also collects user feedback on the proposed design in real time, and the generation AI improves the design based on that. For example, it collects user opinions on the bedroom design and adjusts the bed arrangement and colors. In this way, it is possible to propose designs that better suit the user's needs by collecting user feedback in real time and the generation AI improving the design based on that.

[0044] The design display unit can seamlessly display the proposed design on different devices. The design display unit, for example, enables the proposed design to be seamlessly displayed on a smartphone, tablet, and PC. For example, the living room design can be checked on a smartphone, and then the details can be checked on a tablet or PC. The proposed design based on blueprints can also be seamlessly displayed on different devices. For example, the kitchen design can be checked on a tablet, and then the details can be checked on a smartphone or PC. The proposed design can also be seamlessly displayed on different devices. For example, the bedroom design can be checked on a PC, and then the details can be checked on a smartphone or tablet. This allows for seamless display on different devices, improving user convenience.

[0045] The design display unit can simulate the proposed design under different lighting conditions and time periods and display it to the user. For example, the design display unit simulates the proposed design under different lighting conditions and time periods and displays it to the user. For example, the design of a living room is simulated under daytime and nighttime lighting conditions and displayed to the user. Furthermore, the design proposed based on a blueprint is simulated under different lighting conditions and time periods and displayed to the user. For example, the design of a kitchen is simulated under lighting conditions for breakfast and dinner and displayed to the user. Furthermore, the proposed design is simulated under different lighting conditions and time periods and displayed to the user. For example, the design of a bedroom is simulated under lighting conditions for bedtime and waking up and displayed to the user. This allows the user to confirm the design more specifically by simulating it under different lighting conditions and time periods.

[0046] The design display unit allows the user to make modifications or additions to a saved design later. The design display unit, for example, allows the user to make modifications or additions to a saved design later. For example, after saving a living room design, the user changes the furniture arrangement or color. The design display unit also allows the user to make modifications or additions to a saved design later based on a blueprint. For example, after saving a kitchen design, the user changes the layout or decoration. The design display unit also allows the user to make modifications or additions to a saved design later. For example, after saving a bedroom design, the user changes the bed arrangement or color. In this way, by making modifications or additions to a saved design later, the user can create a design that satisfies them more.

[0047] The design display unit may be capable of exporting the saved design in different formats. For example, the design display unit may enable the saved design to be exported in PDF format. For example, a living room design may be saved as a PDF file to share with others. The saved design may also be exported as an image file. For example, a kitchen design may be saved as a JPEG or PNG file to share with others. The saved design may also be exported as a 3D model. For example, a bedroom design may be saved as a 3D model to share with others. By exporting the saved design in different formats, a user may share the design in various ways.

[0048] The design display unit links the saved designs with cloud storage, allowing them to be accessed from anywhere. The design display unit, for example, links the saved designs with cloud storage, allowing them to be accessed from anywhere. For example, a living room design can be saved in the cloud and accessed from a smartphone or PC. Also, saved designs based on blueprints can be linked with cloud storage, allowing them to be accessed from anywhere. For example, a kitchen design can be saved in the cloud and accessed from a tablet or PC. Furthermore, saved designs can be linked with cloud storage, allowing them to be accessed from anywhere. For example, a bedroom design can be saved in the cloud and accessed from a smartphone or tablet. In this way, saved designs can be linked with cloud storage, allowing them to be accessed from anywhere.

[0049] The design display unit can link saved designs to social media and messaging apps, allowing them to be easily shared. The design display unit, for example, links saved designs to social media, allowing them to be easily shared. For example, a living room design can be shared on Facebook or Instagram. Saved designs based on blueprints can also be linked to messaging apps, allowing them to be easily shared. For example, a kitchen design can be shared on WhatsApp or LINE. Saved designs can also be linked to social media and messaging apps, allowing them to be easily shared. For example, a bedroom design can be shared on Twitter or Messenger. This allows saved designs to be easily shared by linking them to social media and messaging apps.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The photo and blueprint uploading unit may provide a form for inputting the user's lifestyle and preferences when uploading a photo of a room. For example, when uploading a photo of a living room, the user may input information such as family composition, hobbies, and favorite colors. Furthermore, when uploading a blueprint, the unit may provide a form for inputting the user's lifestyle and preferences, and design suggestions may be made based on the information. For example, when uploading a kitchen blueprint, the user may input information such as how often they cook and what types of food they like. Furthermore, when uploading a photo of a room, the unit may provide a form for inputting the user's lifestyle and preferences, and design suggestions may be made based on the information. For example, when uploading a photo of a bedroom, the user may input information such as their sleep quality and favorite interior style. This makes it possible to make design suggestions based on the user's lifestyle and preferences.

[0052] The photo and blueprint uploading unit may have a function that allows room information to be provided using voice input or gesture input. For example, when a user uploads a photo of a room, the room information can be provided using voice input. For example, when uploading a photo of a living room, the user can explain by voice, "This room is a place where the family gathers." Furthermore, when uploading a blueprint, the room information can be provided by gesture input. For example, when uploading a kitchen blueprint, the user can explain, "I want to install a counter here," while pointing to a specific location with their finger. Furthermore, when uploading a photo of a room, the room information can be provided by voice input or gesture input. For example, when uploading a photo of a bedroom, the user can explain by voice, "I want this room to be a relaxing space," and indicate the location of the bed with a gesture. In this way, room information can be provided more intuitively using voice input or gesture input.

[0053] The photo and blueprint uploading unit can have a function that allows seamless uploading from different devices. For example, a user can upload photos of a room from a smartphone and then log in with the same account from a tablet or PC to seamlessly receive design proposals. For example, a user can upload photos of a living room from a smartphone and then check the design proposals on a PC. Also, a user can upload blueprints from a tablet and then log in with the same account from a smartphone or PC to seamlessly receive design proposals. For example, a user can upload kitchen blueprints from a tablet and then check the design proposals on a smartphone. Furthermore, a user can upload photos of a room from a PC and then log in with the same account from a smartphone or tablet to seamlessly receive design proposals. For example, a user can upload photos of a bedroom from a PC and then check the design proposals on a tablet. This allows seamless uploading from different devices, improving user convenience.

[0054] The image analysis unit can make optimal design suggestions by taking into account the lighting conditions and time of day in a room. For example, when the generative AI analyzes a photo of a room, it makes design suggestions by taking into account the lighting conditions and time of day. For example, it can analyze a photo of a living room and propose designs that suit the lighting conditions during the day and at night. In addition, when analyzing a blueprint, it makes design suggestions by taking into account the lighting conditions and time of day in a room. For example, it can analyze a blueprint of a kitchen and propose designs that suit the lighting conditions at breakfast and dinner. In addition, when the generative AI analyzes a photo of a room, it makes design suggestions by taking into account the lighting conditions and time of day. For example, it can analyze a photo of a bedroom and propose designs that suit the lighting conditions at bedtime and when waking up. This makes it possible to make more appropriate design suggestions by taking into account the lighting conditions and time of day.

[0055] The image analysis unit can make design suggestions that incorporate the historical background and cultural elements of a room. For example, when the generative AI analyzes a photo of a room, it makes design suggestions that incorporate the room's historical background and cultural elements. For example, it can analyze a photo of a living room and suggest a design that incorporates traditional furniture and decorations. In addition, when analyzing a blueprint, it makes design suggestions that incorporate the room's historical background and cultural elements. For example, it can analyze a kitchen blueprint and suggest a design that incorporates traditional local designs. In addition, when the generative AI analyzes a photo of a room, it makes design suggestions that incorporate the room's historical background and cultural elements. For example, it can analyze a photo of a bedroom and suggest a design that incorporates cultural decorations and furniture. Incorporating historical background and cultural elements makes it possible to make more unique design suggestions.

[0056] The image analysis unit can refer to the design history of other users and make design suggestions that incorporate trends. For example, when the generation AI analyzes photos of a room, it refers to the design history of other users and makes design suggestions that incorporate the latest trends. For example, it can analyze photos of a living room and suggest popular furniture arrangements and colors. Also, when analyzing blueprints, it refers to the design history of other users and makes design suggestions that incorporate trends. For example, it can analyze kitchen blueprints and suggest layouts and decorations based on the latest trends. Furthermore, when the generation AI analyzes photos of a room, it refers to the design history of other users and makes design suggestions that incorporate trends. For example, it can analyze photos of a bedroom and suggest trendy bed arrangements and colors. This makes it possible to make design suggestions that incorporate the latest trends by referring to the design history of other users.

[0057] When analyzing the characteristics of a room, the customization unit can take into account the material and texture of the furniture and suggest the optimal layout. For example, when the generative AI analyzes a photo of a room, it will take into account the material and texture of the furniture to suggest the optimal layout. For example, it may analyze a photo of a living room and suggest a combination of wooden furniture and a fabric sofa. Also, when analyzing a blueprint, it will take into account the material and texture of the furniture to suggest the optimal layout. For example, it may analyze a kitchen blueprint and suggest a combination of a stainless steel counter and wooden cabinets. Furthermore, when the generative AI analyzes a photo of a room, it will take into account the material and texture of the furniture to suggest the optimal layout. For example, it may analyze a photo of a bedroom and suggest a combination of a metal bed frame and silk curtains. This makes it possible to suggest more appropriate layouts by taking into account the material and texture of the furniture.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: In the photo and blueprint upload section, users can upload photos and blueprints of their rooms. For example, photos and blueprints can be uploaded in JPEG, PNG, or PDF format. Step 2: The image analysis unit analyzes the uploaded photos and blueprints. For example, it can use object recognition technology to analyze furniture placement, dimension measurement technology to measure the size of the room, and color analysis technology to analyze the color of the room. Step 3: The customization unit generates a design tailored to the characteristics of the room based on the results of the analysis by the image analysis unit. For example, it will suggest furniture arrangements that make the most of the space in a small room, and designs that create an open feeling in a large room. It can also provide designs tailored to the purpose of different rooms, such as living rooms, kitchens, and bedrooms. Step 4: The design display unit displays the design generated by the customization unit to the user, for example, the proposed furniture arrangement and color combination, so that the user can check it.

[0060] (Example 2) The AI ​​interior concierge according to an embodiment of the present invention is a system that allows users to upload photos and blueprints of their rooms, which are then analyzed by a generative AI to provide customized design advice tailored to the characteristics of the room. This allows the AI ​​interior concierge to easily improve the interior design of their own rooms.

[0061] An AI interior concierge according to an embodiment includes a photo and blueprint uploading unit, an image analysis unit, a customization unit, and a design display unit. The photo and blueprint uploading unit allows a user to upload photos and blueprints of a room. For example, photos and blueprints can be uploaded in JPEG, PNG, or PDF format. The image analysis unit analyzes the uploaded photos and blueprints. For example, it can analyze the furniture layout using object recognition technology and measure the room size using dimension measurement technology. It can also analyze the room's color using color analysis technology. The customization unit generates a design tailored to the characteristics of the room based on the results of the analysis by the image analysis unit. For example, it proposes furniture layouts that make efficient use of space for small rooms and designs that create an open feeling for large rooms. It can also provide designs tailored to different room uses, such as living rooms, kitchens, and bedrooms. The design display unit displays the design generated by the customization unit to the user. For example, it displays the proposed furniture layouts and color combinations to the user so that the user can confirm them. This allows the AI ​​interior concierge in this embodiment to have users upload photos and blueprints of their rooms, which the generating AI will analyze and suggest designs that suit the characteristics of the room.

[0062] The photo and blueprint uploading unit can automatically measure room dimensions and furniture sizes. For example, when a user uploads a photo of a room, the generating AI automatically measures the room dimensions and recognizes the furniture sizes. For example, when a user uploads a photo of a living room, the generating AI measures the length, width, and height of the room and automatically recognizes the sizes of the sofa and table. Furthermore, when a blueprint is uploaded, the generating AI automatically reads the dimensions and analyzes the room layout. For example, when a kitchen blueprint is uploaded, the generating AI automatically recognizes the position and size of the counter and sink. Furthermore, when a user takes and uploads a photo of a room with their smartphone, the generating AI measures the dimensions in real time and recognizes the furniture sizes. For example, when a photo of a bedroom is taken, the generating AI automatically measures the size of the bed and closet. This automatically measures the room dimensions and furniture sizes, enabling more accurate design proposals.

[0063] The photo / blueprint uploading unit is provided with a form for inputting the user's lifestyle and preferences, and can make design suggestions based on the input. For example, when a user uploads a photo of a room, the photo / blueprint uploading unit is provided with a form for inputting the user's lifestyle and preferences. For example, when uploading a photo of a living room, the user enters information such as family composition, hobbies, and favorite colors. Furthermore, when uploading a blueprint, the unit is provided with a form for inputting the user's lifestyle and preferences, and can make design suggestions based on the input. For example, when uploading a kitchen blueprint, the user enters information such as how often they cook and what types of food they like. Furthermore, when uploading a photo of a room, the unit is provided with a form for inputting the user's lifestyle and preferences, and can make design suggestions based on the input. For example, when uploading a photo of a bedroom, the user enters information such as their sleep quality and favorite interior style. This makes it possible to make design suggestions based on the user's lifestyle and preferences.

[0064] The photo / blueprint uploading unit can use the emotion estimation function to analyze the user's emotions when uploading and provide an interface for eliciting positive emotions. For example, when a user uploads a photo of a room, the photo / blueprint uploading unit uses the emotion estimation function to analyze the user's facial expressions and voice and provide an interface for eliciting positive emotions. For example, when uploading a photo of a living room, the unit detects that the user is smiling and displays a positive message. Furthermore, when uploading a blueprint, the unit uses the emotion estimation function to analyze the user's emotions and provide an interface for eliciting positive emotions. For example, when uploading a kitchen blueprint, the unit detects that the user is feeling stressed and displays a relaxing message. Furthermore, when a user uploads a photo of a room, the unit uses the emotion estimation function to analyze the user's emotions and provide an interface for eliciting positive emotions. For example, when uploading a photo of a bedroom, the unit detects that the user is tired and displays an encouraging message. This allows for analyzing the user's emotions and eliciting positive emotions, thereby enabling better design suggestions.

[0065] The photo / blueprint uploading unit may have a function that allows room information to be provided using voice input or gesture input. The photo / blueprint uploading unit allows, for example, when a user uploads a photo of a room, room information to be provided using voice input. For example, when uploading a photo of a living room, the user may explain by voice, "This room is a place where the family gathers." Furthermore, when uploading a blueprint, room information can be provided by gesture input. For example, when uploading a kitchen blueprint, the user may explain, "I want to install a counter here," while pointing to a specific location with their finger. Furthermore, when uploading a photo of a room, the user may provide room information by voice input or gesture input. For example, when uploading a photo of a bedroom, the user may explain by voice, "I want this room to be a relaxing space," and indicate the location of the bed with a gesture. In this way, room information can be provided more intuitively using voice input or gesture input.

[0066] The photo and blueprint uploading unit may have a function that enables seamless uploading from different devices. For example, the photo and blueprint uploading unit allows a user to upload photos of a room from a smartphone and then log in with the same account from a tablet or PC to seamlessly receive design proposals. For example, a user may upload photos of a living room from a smartphone and then check the design proposals on a PC. Also, a user may upload blueprints from a tablet and then log in with the same account from a smartphone or PC to seamlessly receive design proposals. For example, a user may upload kitchen blueprints from a tablet and then check the design proposals on a smartphone. Furthermore, a user may upload photos of a room from a PC and then log in with the same account from a smartphone or tablet to seamlessly receive design proposals. For example, a user may upload photos of a bedroom from a PC and then check the design proposals on a tablet. This allows seamless uploading from different devices, improving user convenience.

[0067] The photo / blueprint uploading unit uses the emotion estimation function to provide real-time feedback on the user's emotions when uploading, thereby encouraging uploading at the optimal time. For example, when a user uploads a photo of a room, the photo / blueprint uploading unit uses the emotion estimation function to provide real-time feedback on the user's emotions and encourages uploading at the optimal time. For example, when uploading a photo of a living room, the unit detects that the user is relaxed and encourages the user to upload. Furthermore, when uploading a blueprint, the unit uses the emotion estimation function to provide real-time feedback on the user's emotions and encourages the user to upload at the optimal time. For example, when uploading a kitchen blueprint, the unit detects that the user is concentrating and encourages the user to upload. Furthermore, when the user uploads a photo of a room, the unit uses the emotion estimation function to provide real-time feedback on the user's emotions and encourages the user to upload at the optimal time. For example, when uploading a photo of a bedroom, the unit detects that the user is feeling positive and encourages the user to upload. In this way, by providing real-time feedback on the user's emotions, the unit can encourage the user to upload at the optimal time.

[0068] The image analysis unit can make optimal design suggestions by taking into account the lighting conditions and time of day in the room. For example, when the generative AI analyzes photos of a room, the image analysis unit makes design suggestions by taking into account the lighting conditions and time of day. For example, it can analyze photos of a living room and propose designs that suit the lighting conditions during the day and at night. In addition, when analyzing blueprints, it makes design suggestions by taking into account the lighting conditions and time of day in the room. For example, it can analyze blueprints of a kitchen and propose designs that suit the lighting conditions at breakfast and dinner time. In addition, when the generative AI analyzes photos of a room, it makes design suggestions by taking into account the lighting conditions and time of day. For example, it can analyze photos of a bedroom and propose designs that suit the lighting conditions at bedtime and when waking up. This makes it possible to make more appropriate design suggestions by taking into account the lighting conditions and time of day.

[0069] The image analysis unit can make design proposals that incorporate the historical background and cultural elements of a room. For example, when the generative AI analyzes a photo of a room, the image analysis unit makes design proposals that incorporate the historical background and cultural elements of the room. For example, it can analyze a photo of a living room and propose a design that incorporates traditional furniture and decorations. In addition, when analyzing a blueprint, it makes design proposals that incorporate the historical background and cultural elements of the room. For example, it can analyze a kitchen blueprint and propose a design that incorporates traditional designs from the region. In addition, when the generative AI analyzes a photo of a room, it makes design proposals that incorporate the historical background and cultural elements of the room. For example, it can analyze a photo of a bedroom and propose a design that incorporates cultural decorations and furniture. In this way, by incorporating historical background and cultural elements, it becomes possible to make more unique design proposals.

[0070] The image analysis unit uses the emotion estimation function to make design suggestions based on the user's emotions, allowing it to propose a design that will help the user feel most relaxed. For example, when the generation AI analyzes photos of a room, the image analysis unit uses the emotion estimation function to make design suggestions based on the user's emotions. For example, it may analyze photos of a living room and suggest furniture arrangements and colors that will help the user relax. In addition, when analyzing blueprints, the emotion estimation function may be used to make design suggestions based on the user's emotions. For example, it may analyze blueprints of a kitchen and suggest layouts and lighting that will help the user relax. In addition, when the generation AI analyzes photos of a room, the emotion estimation function may be used to make design suggestions based on the user's emotions. For example, it may analyze photos of a bedroom and suggest bed arrangements and colors that will help the user relax. In this way, by making design suggestions based on the user's emotions, it is possible to provide a design that will help the user feel most relaxed.

[0071] The image analysis unit can refer to the design history of other users and make design suggestions that incorporate trends. For example, when the generation AI analyzes photos of a room, the image analysis unit refers to the design history of other users and makes design suggestions that incorporate the latest trends. For example, it analyzes photos of living rooms and suggests popular furniture arrangements and colors. Also, when analyzing blueprints, it refers to the design history of other users and makes design suggestions that incorporate trends. For example, it analyzes kitchen blueprints and suggests layouts and decorations based on the latest trends. Furthermore, when the generation AI analyzes photos of a room, it refers to the design history of other users and makes design suggestions that incorporate trends. For example, it analyzes photos of bedrooms and suggests trendy bed arrangements and colors. In this way, by referring to the design history of other users, it is possible to make design suggestions that incorporate the latest trends.

[0072] The image analysis unit can make design suggestions that match different seasons and events. For example, when the generative AI analyzes a photo of a room, the image analysis unit makes design suggestions that match the season. For example, it can analyze a photo of a living room and suggest bright colors in spring and warm colors in winter. Also, when analyzing a blueprint, it can make design suggestions that match events. For example, it can analyze a kitchen blueprint and suggest decorations that match events such as Christmas and Halloween. Furthermore, when the generative AI analyzes a photo of a room, it can make design suggestions that match the season and events. For example, it can analyze a photo of a bedroom and suggest cool colors in summer and muted colors in autumn. This makes it possible to make design suggestions that match different seasons and events.

[0073] The image analysis unit uses the emotion estimation function to make design suggestions based on the user's emotions, and can propose designs that elicit specific emotions. For example, when the generation AI analyzes photos of a room, the image analysis unit uses the emotion estimation function to make design suggestions based on the user's emotions. For example, it may analyze photos of a living room and suggest furniture arrangements and colors that will help the user relax. In addition, when analyzing blueprints, the emotion estimation function may be used to make design suggestions based on the user's emotions. For example, it may analyze blueprints of a kitchen and suggest layouts and lighting that will invigorate the user. In addition, when the generation AI analyzes photos of a room, the emotion estimation function may be used to make design suggestions based on the user's emotions. For example, it may analyze photos of a bedroom and suggest bed arrangements and colors that will make the user feel comfortable. In this way, by making design suggestions based on the user's emotions, it is possible to provide designs that elicit specific emotions.

[0074] When analyzing the characteristics of a room, the customization unit takes into account the material and texture of the furniture and can suggest the optimal layout. For example, when the generative AI analyzes a photo of a room, the customization unit takes into account the material and texture of the furniture and suggests the optimal layout. For example, it analyzes a photo of a living room and suggests a combination of wooden furniture and a fabric sofa. Also, when analyzing a blueprint, it takes into account the material and texture of the furniture and suggests the optimal layout. For example, it analyzes a kitchen blueprint and suggests a combination of a stainless steel counter and wooden cabinets. Furthermore, when the generative AI analyzes a photo of a room, it takes into account the material and texture of the furniture and suggests the optimal layout. For example, it analyzes a photo of a bedroom and suggests a combination of a metal bed frame and silk curtains. This makes it possible to suggest more appropriate layouts by taking into account the material and texture of the furniture.

[0075] The customization unit can suggest eco-friendly materials and designs when analyzing the characteristics of a room. For example, when the generative AI analyzes a photo of a room, the customization unit suggests eco-friendly materials and designs. For example, it can analyze a photo of a living room and suggest furniture made from recycled wood and energy-efficient lighting. It can also suggest eco-friendly materials and designs when analyzing a blueprint. For example, it can analyze a kitchen blueprint and suggest countertops made from recycled materials and energy-efficient appliances. It can also suggest eco-friendly materials and designs when the generative AI analyzes a photo of a room. For example, it can analyze a photo of a bedroom and suggest organic cotton bed linen and carpets made from natural materials. This makes it possible to suggest eco-friendly materials and designs, enabling environmentally conscious interior design.

[0076] The customization unit uses the emotion estimation function to perform customization based on the user's emotions and can propose a design that will satisfy the user most. For example, when the generation AI analyzes a photo of a room, the customization unit uses the emotion estimation function to perform customization based on the user's emotions. For example, it analyzes a photo of a living room and proposes furniture arrangements and colors that will make the user most relaxed. Furthermore, when analyzing a blueprint, the emotion estimation function is used to perform customization based on the user's emotions. For example, it analyzes a kitchen blueprint and proposes a layout and lighting that will most energize the user. Furthermore, when the generation AI analyzes a photo of a room, the emotion estimation function is used to perform customization based on the user's emotions. For example, it analyzes a photo of a bedroom and proposes bed arrangements and colors that will make the user feel most comfortable. In this way, by performing customization based on the user's emotions, it is possible to provide a design that will satisfy the user most.

[0077] When analyzing the characteristics of a room, the customization unit can make design suggestions that take into account the safety of pets and children. For example, when the generation AI analyzes a photo of a room, the customization unit makes design suggestions that take into account the safety of pets and children. For example, it analyzes a photo of a living room and suggests furniture with no corners and non-slip flooring. In addition, when analyzing a blueprint, it makes design suggestions that take into account the safety of pets and children. For example, it analyzes a blueprint of a kitchen and suggests storage that is located high up so that children cannot reach it, and spaces where pets can stay safely. In addition, when the generation AI analyzes a photo of a room, it makes design suggestions that take into account the safety of pets and children. For example, it analyzes a photo of a bedroom and suggests a bed that is difficult for children to fall out of and a sleeping area where pets can stay comfortably. This makes it possible to make design suggestions that take into account the safety of pets and children.

[0078] When analyzing the characteristics of a room, the customization unit can make design proposals that take into account integration with smart home devices. For example, when the generation AI analyzes photos of a room, the customization unit makes design proposals that take into account integration with smart home devices. For example, it analyzes photos of a living room and proposes optimal placement for smart lighting and voice assistants. In addition, when analyzing blueprints, it makes design proposals that take into account integration with smart home devices. For example, it analyzes blueprints of a kitchen and proposes optimal placement for smart appliances and sensors. In addition, when the generation AI analyzes photos of a room, it makes design proposals that take into account integration with smart home devices. For example, it analyzes photos of a bedroom and proposes optimal placement for smart curtains and temperature control devices. This makes it possible to make design proposals that take into account integration with smart home devices.

[0079] The customization unit uses the emotion estimation function to perform customization based on the user's emotions and can propose designs that elicit specific emotions. For example, when the generation AI analyzes photos of a room, the customization unit uses the emotion estimation function to perform customization based on the user's emotions. For example, it may analyze photos of a living room and propose furniture arrangements and colors that will relax the user. Furthermore, when analyzing blueprints, the emotion estimation function may perform customization based on the user's emotions. For example, it may analyze blueprints of a kitchen and propose layouts and lighting that will energize the user. Furthermore, when the generation AI analyzes photos of a room, the emotion estimation function may perform customization based on the user's emotions. For example, it may analyze photos of a bedroom and propose bed arrangements and colors that will make the user feel comfortable. In this way, by performing customization based on the user's emotions, it is possible to provide designs that elicit specific emotions.

[0080] The design display unit displays the proposed design as a 3D model, allowing the user to experience it in virtual reality. The design display unit, for example, displays the proposed design as a 3D model, allowing the user to experience it in virtual reality. For example, a living room design may be displayed as a 3D model, allowing the user to experience it in virtual reality using a VR headset. A proposed design based on a blueprint may also be displayed as a 3D model, allowing the user to experience it in virtual reality. For example, a kitchen design may be displayed as a 3D model, allowing the user to experience it in virtual reality using a VR headset. Furthermore, the proposed design may also be displayed as a 3D model, allowing the user to experience it in virtual reality. For example, a bedroom design may be displayed as a 3D model, allowing the user to experience it in virtual reality using a VR headset. In this way, the proposed design may be displayed as a 3D model, allowing the user to more specifically confirm the design by experiencing it in virtual reality.

[0081] The design display unit collects user feedback on the proposed design in real time, and the generation AI can improve the design based on that. For example, the design display unit collects user feedback on the proposed design in real time, and the generation AI improves the design based on that. For example, it collects user opinions on the living room design and adjusts the furniture arrangement and colors. It also collects user feedback on a proposed design based on a blueprint in real time, and the generation AI improves the design based on that. For example, it collects user opinions on the kitchen design and adjusts the layout and decoration. It also collects user feedback on the proposed design in real time, and the generation AI improves the design based on that. For example, it collects user opinions on the bedroom design and adjusts the bed arrangement and colors. In this way, it is possible to propose designs that better suit the user's needs by collecting user feedback in real time and the generation AI improving the design based on that.

[0082] The design display unit can use the emotion estimation function to analyze the emotion of the user when reviewing a design and suggest an optimal display method. For example, when displaying a proposed design, the design display unit uses the emotion estimation function to analyze the user's emotion and suggest an optimal display method. For example, when displaying a living room design, the unit detects that the user is relaxed and displays it in calm colors. Furthermore, when displaying a proposed design based on a blueprint, the unit uses the emotion estimation function to analyze the user's emotion and suggest an optimal display method. For example, when displaying a kitchen design, the unit detects that the user is concentrating and displays it in bright colors. Furthermore, when displaying a proposed design, the unit uses the emotion estimation function to analyze the user's emotion and suggest an optimal display method. For example, when displaying a bedroom design, the unit detects that the user is tired and displays it in relaxing colors. In this way, by analyzing the user's emotion and suggesting an optimal display method, more effective design review is possible.

[0083] The design display unit can seamlessly display the proposed design on different devices. The design display unit, for example, enables the proposed design to be seamlessly displayed on a smartphone, tablet, and PC. For example, the living room design can be checked on a smartphone, and then the details can be checked on a tablet or PC. The proposed design based on blueprints can also be seamlessly displayed on different devices. For example, the kitchen design can be checked on a tablet, and then the details can be checked on a smartphone or PC. The proposed design can also be seamlessly displayed on different devices. For example, the bedroom design can be checked on a PC, and then the details can be checked on a smartphone or tablet. This allows for seamless display on different devices, improving user convenience.

[0084] The design display unit can simulate the proposed design under different lighting conditions and time periods and display it to the user. For example, the design display unit simulates the proposed design under different lighting conditions and time periods and displays it to the user. For example, the design of a living room is simulated under daytime and nighttime lighting conditions and displayed to the user. Furthermore, the design proposed based on a blueprint is simulated under different lighting conditions and time periods and displayed to the user. For example, the design of a kitchen is simulated under lighting conditions for breakfast and dinner and displayed to the user. Furthermore, the proposed design is simulated under different lighting conditions and time periods and displayed to the user. For example, the design of a bedroom is simulated under lighting conditions for bedtime and waking up and displayed to the user. This allows the user to confirm the design more specifically by simulating it under different lighting conditions and time periods.

[0085] The design display unit uses the emotion estimation function to provide real-time feedback on the user's emotions when reviewing a design, and can suggest an optimal display method. For example, when displaying a proposed design, the design display unit uses the emotion estimation function to provide real-time feedback on the user's emotions and suggest an optimal display method. For example, when displaying a living room design, the unit detects that the user is relaxed and displays it in calm colors. Furthermore, when displaying a proposed design based on a blueprint, the unit uses the emotion estimation function to provide real-time feedback on the user's emotions and suggest an optimal display method. For example, when displaying a kitchen design, the unit detects that the user is concentrating and displays it in bright colors. Furthermore, when displaying a proposed design, the unit uses the emotion estimation function to provide real-time feedback on the user's emotions and suggest an optimal display method. For example, when displaying a bedroom design, the unit detects that the user is tired and displays it in relaxing colors. In this way, the unit can provide real-time feedback on the user's emotions and suggest an optimal display method.

[0086] The design display unit allows the user to make modifications or additions to a saved design later. The design display unit, for example, allows the user to make modifications or additions to a saved design later. For example, after saving a living room design, the user changes the furniture arrangement or color. The design display unit also allows the user to make modifications or additions to a saved design later based on a blueprint. For example, after saving a kitchen design, the user changes the layout or decoration. The design display unit also allows the user to make modifications or additions to a saved design later. For example, after saving a bedroom design, the user changes the bed arrangement or color. In this way, by making modifications or additions to a saved design later, the user can create a design that satisfies them more.

[0087] The design display unit may be capable of exporting the saved design in different formats. For example, the design display unit may enable the saved design to be exported in PDF format. For example, a living room design may be saved as a PDF file to share with others. The saved design may also be exported as an image file. For example, a kitchen design may be saved as a JPEG or PNG file to share with others. The saved design may also be exported as a 3D model. For example, a bedroom design may be saved as a 3D model to share with others. By exporting the saved design in different formats, a user may share the design in various ways.

[0088] The design display unit can use the emotion estimation function to analyze the emotion of the user when saving a design and suggest an optimal saving method. For example, when the user saves a design, the design display unit uses the emotion estimation function to analyze the user's emotion and suggest an optimal saving method. For example, when saving a living room design, the design display unit detects that the user is satisfied and encourages the user to save. Furthermore, when saving a design based on a blueprint, the emotion estimation function analyzes the user's emotion and suggests an optimal saving method. For example, when saving a kitchen design, the emotion estimation function detects that the user is excited and encourages the user to save. Furthermore, when the user saves a design, the emotion estimation function analyzes the user's emotion and suggests an optimal saving method. For example, when saving a bedroom design, the emotion estimation function detects that the user is relaxed and encourages the user to save. In this way, by analyzing the user's emotion and suggesting an optimal saving method, it is possible to provide a saving method that is more satisfying to the user.

[0089] The design display unit links the saved designs with cloud storage, allowing them to be accessed from anywhere. The design display unit, for example, links the saved designs with cloud storage, allowing them to be accessed from anywhere. For example, a living room design can be saved in the cloud and accessed from a smartphone or PC. Also, saved designs based on blueprints can be linked with cloud storage, allowing them to be accessed from anywhere. For example, a kitchen design can be saved in the cloud and accessed from a tablet or PC. Furthermore, saved designs can be linked with cloud storage, allowing them to be accessed from anywhere. For example, a bedroom design can be saved in the cloud and accessed from a smartphone or tablet. In this way, saved designs can be linked with cloud storage, allowing them to be accessed from anywhere.

[0090] The design display unit can link saved designs to social media and messaging apps, allowing them to be easily shared. The design display unit, for example, links saved designs to social media, allowing them to be easily shared. For example, a living room design can be shared on Facebook or Instagram. Saved designs based on blueprints can also be linked to messaging apps, allowing them to be easily shared. For example, a kitchen design can be shared on WhatsApp or LINE. Saved designs can also be linked to social media and messaging apps, allowing them to be easily shared. For example, a bedroom design can be shared on Twitter or Messenger. This allows saved designs to be easily shared by linking them to social media and messaging apps.

[0091] The design display unit can use the emotion estimation function to provide real-time feedback on the user's emotions when sharing a design and suggest an optimal sharing method. For example, when a user shares a design, the design display unit can use the emotion estimation function to provide real-time feedback on the user's emotions and suggest an optimal sharing method. For example, when sharing a living room design, the design display unit can detect that the user is excited and encourage the user to share it on a social networking site. Furthermore, when sharing a design based on a blueprint, the design display unit can use the emotion estimation function to provide real-time feedback on the user's emotions and suggest an optimal sharing method. For example, when sharing a kitchen design, the design display unit can detect that the user is satisfied and encourage the user to share it on a messaging app. Furthermore, when sharing a design, the design display unit can use the emotion estimation function to provide real-time feedback on the user's emotions and suggest an optimal sharing method. For example, when sharing a bedroom design, the design display unit can detect that the user is relaxed and encourage the user to share it on cloud storage. In this way, the design display unit can provide real-time feedback on the user's emotions and suggest an optimal sharing method.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The photo and blueprint uploading unit may provide a form for inputting the user's lifestyle and preferences when uploading a photo of a room. For example, when uploading a photo of a living room, the user may input information such as family composition, hobbies, and favorite colors. Furthermore, when uploading a blueprint, the unit may provide a form for inputting the user's lifestyle and preferences, and design suggestions may be made based on the information. For example, when uploading a kitchen blueprint, the user may input information such as how often they cook and what types of food they like. Furthermore, when uploading a photo of a room, the unit may provide a form for inputting the user's lifestyle and preferences, and design suggestions may be made based on the information. For example, when uploading a photo of a bedroom, the user may input information such as their sleep quality and favorite interior style. This makes it possible to make design suggestions based on the user's lifestyle and preferences.

[0094] The photo and blueprint uploading unit may have a function that allows room information to be provided using voice input or gesture input. For example, when a user uploads a photo of a room, the room information can be provided using voice input. For example, when uploading a photo of a living room, the user can explain by voice, "This room is a place where the family gathers." Furthermore, when uploading a blueprint, the room information can be provided by gesture input. For example, when uploading a kitchen blueprint, the user can explain, "I want to install a counter here," while pointing to a specific location with their finger. Furthermore, when uploading a photo of a room, the room information can be provided by voice input or gesture input. For example, when uploading a photo of a bedroom, the user can explain by voice, "I want this room to be a relaxing space," and indicate the location of the bed with a gesture. In this way, room information can be provided more intuitively using voice input or gesture input.

[0095] The photo and blueprint uploading unit can have a function that allows seamless uploading from different devices. For example, a user can upload photos of a room from a smartphone and then log in with the same account from a tablet or PC to seamlessly receive design proposals. For example, a user can upload photos of a living room from a smartphone and then check the design proposals on a PC. Also, a user can upload blueprints from a tablet and then log in with the same account from a smartphone or PC to seamlessly receive design proposals. For example, a user can upload kitchen blueprints from a tablet and then check the design proposals on a smartphone. Furthermore, a user can upload photos of a room from a PC and then log in with the same account from a smartphone or tablet to seamlessly receive design proposals. For example, a user can upload photos of a bedroom from a PC and then check the design proposals on a tablet. This allows seamless uploading from different devices, improving user convenience.

[0096] The photo and blueprint uploading unit can use an emotion estimation function to analyze the user's emotions when uploading photos and provide an interface for eliciting positive emotions. For example, when a user uploads a photo of a room, the emotion estimation function can be used to analyze the user's facial expressions and voice, providing an interface for eliciting positive emotions. For example, when uploading a photo of a living room, the emotion estimation function can be used to detect that the user is smiling and display a positive message. Furthermore, when uploading blueprints, the emotion estimation function can be used to analyze the user's emotions and provide an interface for eliciting positive emotions. For example, when uploading a kitchen blueprint, the emotion estimation function can be used to detect that the user is feeling stressed and display a relaxing message. Furthermore, when a user uploads a photo of a room, the emotion estimation function can be used to analyze the user's emotions and provide an interface for eliciting positive emotions. For example, when uploading a photo of a bedroom, the emotion estimation function can be used to detect that the user is tired and display an encouraging message. This allows for analyzing the user's emotions and eliciting positive emotions, thereby enabling better design proposals.

[0097] The image analysis unit can make optimal design suggestions by taking into account the lighting conditions and time of day in a room. For example, when the generative AI analyzes a photo of a room, it makes design suggestions by taking into account the lighting conditions and time of day. For example, it can analyze a photo of a living room and propose designs that suit the lighting conditions during the day and at night. In addition, when analyzing a blueprint, it makes design suggestions by taking into account the lighting conditions and time of day in a room. For example, it can analyze a blueprint of a kitchen and propose designs that suit the lighting conditions at breakfast and dinner. In addition, when the generative AI analyzes a photo of a room, it makes design suggestions by taking into account the lighting conditions and time of day. For example, it can analyze a photo of a bedroom and propose designs that suit the lighting conditions at bedtime and when waking up. This makes it possible to make more appropriate design suggestions by taking into account the lighting conditions and time of day.

[0098] The image analysis unit can make design suggestions that incorporate the historical background and cultural elements of a room. For example, when the generative AI analyzes a photo of a room, it makes design suggestions that incorporate the room's historical background and cultural elements. For example, it can analyze a photo of a living room and suggest a design that incorporates traditional furniture and decorations. In addition, when analyzing a blueprint, it makes design suggestions that incorporate the room's historical background and cultural elements. For example, it can analyze a kitchen blueprint and suggest a design that incorporates traditional local designs. In addition, when the generative AI analyzes a photo of a room, it makes design suggestions that incorporate the room's historical background and cultural elements. For example, it can analyze a photo of a bedroom and suggest a design that incorporates cultural decorations and furniture. Incorporating historical background and cultural elements makes it possible to make more unique design suggestions.

[0099] The image analysis unit uses the emotion estimation function to make design suggestions based on the user's emotions, allowing it to propose a design that will help the user feel most relaxed. For example, when the generation AI analyzes photos of a room, it uses the emotion estimation function to make design suggestions based on the user's emotions. For example, it can analyze photos of a living room and suggest furniture arrangements and colors that will help the user relax. In addition, when analyzing blueprints, it uses the emotion estimation function to make design suggestions based on the user's emotions. For example, it can analyze blueprints of a kitchen and suggest layouts and lighting that will help the user relax. In addition, when the generation AI analyzes photos of a room, it uses the emotion estimation function to make design suggestions based on the user's emotions. For example, it can analyze photos of a bedroom and suggest bed arrangements and colors that will help the user relax. In this way, by making design suggestions based on the user's emotions, it is possible to provide a design that will help the user feel most relaxed.

[0100] The image analysis unit can refer to the design history of other users and make design suggestions that incorporate trends. For example, when the generation AI analyzes photos of a room, it refers to the design history of other users and makes design suggestions that incorporate the latest trends. For example, it can analyze photos of a living room and suggest popular furniture arrangements and colors. Also, when analyzing blueprints, it refers to the design history of other users and makes design suggestions that incorporate trends. For example, it can analyze kitchen blueprints and suggest layouts and decorations based on the latest trends. Furthermore, when the generation AI analyzes photos of a room, it refers to the design history of other users and makes design suggestions that incorporate trends. For example, it can analyze photos of a bedroom and suggest trendy bed arrangements and colors. This makes it possible to make design suggestions that incorporate the latest trends by referring to the design history of other users.

[0101] When analyzing the characteristics of a room, the customization unit can take into account the material and texture of the furniture and suggest the optimal layout. For example, when the generative AI analyzes a photo of a room, it will take into account the material and texture of the furniture to suggest the optimal layout. For example, it may analyze a photo of a living room and suggest a combination of wooden furniture and a fabric sofa. Also, when analyzing a blueprint, it will take into account the material and texture of the furniture to suggest the optimal layout. For example, it may analyze a kitchen blueprint and suggest a combination of a stainless steel counter and wooden cabinets. Furthermore, when the generative AI analyzes a photo of a room, it will take into account the material and texture of the furniture to suggest the optimal layout. For example, it may analyze a photo of a bedroom and suggest a combination of a metal bed frame and silk curtains. This makes it possible to suggest more appropriate layouts by taking into account the material and texture of the furniture.

[0102] The customization unit uses the emotion estimation function to perform customization based on the user's emotions and propose a design that will satisfy the user most. For example, when the generation AI analyzes photos of a room, it uses the emotion estimation function to perform customization based on the user's emotions. For example, it analyzes photos of a living room and proposes furniture arrangements and colors that will make the user most relaxed. Furthermore, when analyzing blueprints, it uses the emotion estimation function to perform customization based on the user's emotions. For example, it analyzes blueprints of a kitchen and proposes layouts and lighting that will most energize the user. Furthermore, when the generation AI analyzes photos of a room, it uses the emotion estimation function to perform customization based on the user's emotions. For example, it analyzes photos of a bedroom and proposes bed arrangements and colors that will make the user feel most comfortable. In this way, customization based on the user's emotions can be performed to provide a design that will satisfy the user most.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: In the photo and blueprint upload section, users can upload photos and blueprints of their rooms. For example, photos and blueprints can be uploaded in JPEG, PNG, or PDF format. Step 2: The image analysis unit analyzes the uploaded photos and blueprints. For example, it can use object recognition technology to analyze furniture placement, dimension measurement technology to measure the size of the room, and color analysis technology to analyze the color of the room. Step 3: The customization unit generates a design tailored to the characteristics of the room based on the results of the analysis by the image analysis unit. For example, it will suggest furniture arrangements that make the most of the space in a small room, and designs that create an open feeling in a large room. It can also provide designs tailored to the purpose of different rooms, such as living rooms, kitchens, and bedrooms. Step 4: The design display unit displays the design generated by the customization unit to the user, for example, the proposed furniture arrangement and color combination, so that the user can check it.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 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 photo and blueprint upload section where users can upload photos and blueprints of their rooms; an image analysis unit that analyzes the photos and blueprints uploaded by the photo and blueprint upload unit; a customization unit that generates a design that matches the characteristics of the room based on the results of the analysis by the image analysis unit; a design display unit that displays the design created by the customization unit to the user. A system characterized by:

2. The photo and blueprint upload section is Equipped with a function to automatically measure the dimensions of the room and furniture 2. The system of claim 1.

3. The photo and blueprint upload section is A form is provided for inputting the user's lifestyle and preferences, and design suggestions are made based on this.

2. The system of claim 1.

4. The photo and blueprint upload section is Analyze the emotions of the user when uploading and provide an interface to elicit positive emotions 2. The system of claim 1.

5. The photo and blueprint upload section is It has a function that can provide information about the room using voice input or gesture input.

2. The system of claim 1.

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