System

Abstract

To provide a system for supporting a user to concretely image an ideal room and select proper furniture and small articles.SOLUTION: The system includes a room information input unit, a photograph upload unit, a preference information input unit, a coordinate generation unit, an image suggestion unit, a recommended furniture pickup unit, and a net shopping unit. The room information input unit inputs room information. The photograph upload unit uploads a photograph of the room. The favorite information input part inputs a favorite room type, a color tone of a wall, and a remodeling budget. The coordination generation unit generates coordination based on the input information. The image suggestion unit suggests the coordination by reflecting the coordination in the room image. The recommended furniture pickup unit picks up recommended furniture and small articles suitable for coordination. The net shopping part can perform net shopping of the picked-up furniture and small articles.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] With conventional technology, it was difficult to realize the ideal image when redecorating a room or coordinating the interior, and it was difficult to select the appropriate furniture and accessories.

[0005] The system according to the embodiment aims to assist a user in concretely imagining an ideal room and selecting appropriate furniture and accessories. [Means for solving the problem]

[0006] The system according to the embodiment includes a room information input unit, a photo upload unit, a preference information input unit, a coordination generation unit, an image suggestion unit, a recommended furniture pick-up unit, and an online shopping unit. The room information input unit inputs room information. The photo upload unit uploads photos of the room. The preference information input unit inputs the preferred room type, wall color tone, and renovation budget. The coordination generation unit generates a coordination based on the information input by the room information input unit, the photo upload unit, and the preference information input unit. The image suggestion unit proposes a coordination by reflecting the coordination generated by the coordination generation unit in a room image. The recommended furniture pick-up unit picks out recommended furniture and accessories that match the coordination suggested by the image suggestion unit. The online shopping unit allows online shopping for the furniture and accessories picked up by the recommended furniture pick-up unit. [Effects of the Invention]

[0007] The system according to the embodiment can assist the user in concretely imagining an ideal room and selecting appropriate furniture and accessories. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The interior coordination system according to the embodiment of the present invention is a system that allows a user to easily coordinate an ideal room and purchase the necessary furniture and accessories. As a result, the interior coordination system can alleviate the user's anxieties and worries and realize the ideal room.

[0029] An interior coordination system according to an embodiment includes a room information input unit, a photo upload unit, a preference information input unit, a coordinate generation unit, an image suggestion unit, a recommended furniture selection unit, and an online shopping unit. The room information input unit receives input from a user about the size, layout, ceiling height, and other information about the room. For example, the input information is that the room size is 20 square meters, the ceiling height is 2.5 meters, and the layout is a 1LDK (one living room, kitchen, and kitchen). The photo upload unit receives input from a user about photos taken from the four corners of the room. For example, by uploading photos taken from the four corners of the room, the generation AI can visually understand the room layout and furniture arrangement. The preference information input unit receives input from a user about the user's preferred room type (vintage, Scandinavian style, etc.), wall color, and renovation budget. For example, the user inputs information such as "I like Scandinavian-style rooms, with white walls and a renovation budget of less than 100,000 yen." The coordinate generation unit generates a coordinate based on the information input by the room information input unit, the photo upload unit, and the preference information input unit. For example, the generation AI generates a coordination based on a prompt such as, "Please suggest furniture and accessories that would go well with white walls in a Scandinavian-style room." The image suggestion unit proposes the coordination generated by the coordination generation unit by reflecting it in a room image. For example, the generation AI superimposes the coordination proposed by the coordination generation unit onto a photo of the room and suggests, "The room will look like this." The recommended furniture picking unit picks recommended furniture and accessories that go well with the coordination proposed by the image suggestion unit. For example, the generation AI suggests, "This sofa and table would go well with a Scandinavian-style room." The online shopping unit allows online shopping for the furniture and accessories picked by the recommended furniture picking unit. For example, it provides a link where the furniture and accessories proposed by the generation AI can be purchased. In this way, the interior coordination system according to the embodiment allows users to easily coordinate their ideal room and purchase the necessary furniture and accessories.

[0030] The photo upload unit can automatically measure the dimensions of a room and generate a 3D model. For example, when a user uploads photos taken from all four corners of the room, the generation AI uses image analysis technology to automatically measure the room's dimensions. For example, it accurately measures the length of the walls and the height of the ceiling and generates a 3D model. When a user uploads a photo of the room, the generation AI uses image recognition technology to identify the position of furniture and decorations and generates a 3D model of the room based on that. For example, it accurately reproduces the position of a sofa or table. When a user uploads a photo of the room, the generation AI automatically measures the room's dimensions and generates a 3D model. For example, it accurately determines the size and ceiling height of the room and creates a realistic 3D model. This allows for accurate measurement of room dimensions and generation of a 3D model, enabling more realistic room coordination.

[0031] The room information input unit can refer to past data and present examples of coordination for similar rooms. For example, when a user inputs the size and layout of a room, the room information input unit causes the generation AI to refer to a past database and present examples of coordination for similar rooms. For example, it displays examples of coordination for rooms with the same size and layout. Furthermore, when room information is input, the generation AI analyzes past user data and presents successful examples of similar rooms. For example, it displays examples of coordination for rooms with the same ceiling height and wall color tone. Furthermore, when a user inputs room information, the room information input unit causes the generation AI to automatically search for and suggest similar room coordination examples based on past data. For example, it displays examples of coordination for rooms with the same layout and size. In this way, by referring to past data, it is possible to provide coordination examples that can be used as reference for the user.

[0032] The preference information input unit can present customized questions based on the user's lifestyle and hobbies to collect detailed information. For example, when the user inputs information about their room, the preference information input unit causes the generation AI to present customized questions based on the user's lifestyle and hobbies. For example, questions such as "Do you have pets?" or "Do you like reading?" are displayed. Furthermore, when the user inputs information about their room, the preference information input unit causes the generation AI to refer to the user's past data and present customized questions. For example, a question such as "What did you like about your last outfit?" is displayed. Furthermore, when the user inputs information about their room, the preference information input unit causes the generation AI to present customized questions based on the user's hobbies and lifestyle to collect more detailed information. For example, questions such as "Do you like the outdoors?" or "Is cooking your hobby?" are displayed. This allows for more personalized outfit suggestions to be made by collecting detailed information based on the user's lifestyle and hobbies.

[0033] The preference information input unit can perform a cost simulation in real time when the wall color tone and renovation budget are input, and propose the optimal coordination within the budget. For example, when a user inputs the wall color tone and renovation budget, the preference information input unit causes the generation AI to perform a cost simulation in real time and propose the optimal coordination within the budget. For example, the generation AI can propose furniture and accessories that match the wall color within the budget. Furthermore, when the user inputs the wall color tone and renovation budget, the generation AI can perform a cost simulation and propose the optimal coordination within the budget in real time. For example, the generation AI can display a combination of furniture and decorations according to the budget. Furthermore, when a user inputs the wall color tone and renovation budget, the generation AI can perform a cost simulation in real time and propose the optimal coordination within the budget. For example, the generation AI can display the optimal interior plan according to the budget. In this way, the generation AI can perform a cost simulation in real time and propose the optimal coordination within the budget.

[0034] The coordination generation unit can refer to past user data and propose successful coordination patterns. For example, the generation AI in the coordination generation unit analyzes past user data and proposes successful coordination patterns. For example, suggestions are made based on coordination examples that have received high user ratings. Furthermore, when generating a coordination, the generation AI refers to a past database and proposes the most popular coordination patterns. For example, it displays successful examples of rooms with the same style. Furthermore, the coordination generation unit automatically searches for and proposes successful coordination patterns based on past user data. For example, it displays successful examples of rooms with the same floor plan and size. This makes it possible to propose coordination based on past successful examples.

[0035] The image suggestion unit can provide a 360-degree view when reflecting coordination in a room image. For example, when the generation AI reflects coordination in a room image, the image suggestion unit provides a 360-degree view so that the user can check the room from different perspectives. For example, it displays a view that allows a panoramic view in all directions from the center of the room. Furthermore, when the image suggestion unit reflects coordination in a room image, the generation AI provides a 360-degree view to the user so that the user can check the room from different perspectives. For example, it displays a view that allows a detailed view of every corner of the room. Furthermore, when the generation AI reflects coordination in a room image, the image suggestion unit provides a 360-degree view so that the user can check the room from different perspectives. For example, it displays a view looking down on the room from above or a view looking up from the floor. This allows the user to check the room from different perspectives.

[0036] The image suggestion unit can provide a realistic image by simulating the effects of lighting and shadows in real time when reflecting coordination in a room image. For example, the image suggestion unit can provide a realistic image by simulating the effects of lighting and shadows in real time when the generation AI reflects coordination in a room image. For example, the image suggestion unit can simulate the effects of natural light and artificial lighting. Furthermore, the image suggestion unit can provide a realistic image by simulating the effects of lighting and shadows in real time when the generation AI reflects coordination in a room image. For example, the image suggestion unit can simulate lighting conditions during the day and at night. Furthermore, the image suggestion unit can provide a realistic image by simulating the effects of lighting and shadows in real time when the generation AI reflects coordination in a room image. For example, the image suggestion unit can simulate the effects of different lighting fixtures. In this way, a more realistic image can be provided by simulating the effects of lighting and shadows in real time.

[0037] The recommended furniture picker can refer to past purchase data and suggest popular items. For example, the generation AI analyzes past purchase data to suggest popular furniture and accessories. For example, it displays sofas and tables that have received high user ratings. When selecting recommended furniture and accessories, the generation AI refers to a past database to suggest the best-selling items. For example, it displays furniture with popular designs and brands. The generation AI automatically searches for and suggests popular furniture and accessories based on past purchase data. For example, it displays items that suit rooms of the same style. This allows the generation AI to suggest popular items based on past purchase data.

[0038] The recommended furniture picking unit can make customized suggestions based on the user's budget and preferences. In the recommended furniture picking unit, for example, the generation AI suggests furniture and accessories customized based on the user's budget and preferences. For example, sofas and tables that can be purchased within the budget are displayed. When picking recommended furniture and accessories, the generation AI refers to the user's past data and suggests items that match the user's preferences. For example, furniture with a design and color that the user prefers is displayed. In the recommended furniture picking unit, the generation AI automatically searches for and suggests optimal furniture and accessories based on the user's budget and preferences. For example, optimal interior items that can be purchased within the budget are displayed. This makes it possible to make customized suggestions based on the user's budget and preferences.

[0039] The recommended furniture picker can suggest eco-friendly materials and recyclable items. For example, the generation AI of the recommended furniture picker suggests eco-friendly materials and recyclable items. For example, it displays furniture made from recycled wood or recycled plastic. Furthermore, when selecting recommended furniture and accessories, the generation AI suggests eco-friendly materials and recyclable items in real time. For example, it displays eco-friendly paint and recyclable decorations. Furthermore, the recommended furniture picker automatically searches for and suggests eco-friendly materials and recyclable items. For example, it displays furniture and decorations made from sustainable materials. This allows for environmentally conscious coordination by suggesting eco-friendly materials and recyclable items.

[0040] The recommended furniture picker can suggest items from different cultures and regions, providing a global perspective. For example, the generation AI suggests items from different cultures and regions. For example, it displays Scandinavian-style furniture and Asian-style decorative items. When selecting recommended furniture and accessories, the generation AI refers to a past database to suggest items from different cultures and regions. For example, it displays Japanese-style furniture and Moroccan-style decorative items. The generation AI also automatically searches for and suggests items from different cultures and regions. For example, it displays American-style furniture and Italian modern decorative items. This allows the generation AI to suggest items from different cultures and regions, providing a global perspective.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The interior coordination system can also make suggestions that take into account the user's health condition. For example, if the user has allergies, it can suggest furniture and accessories made from allergen-free materials. If the user has back pain, it can also suggest chairs and mattresses that are gentle on the back. Furthermore, if the user is visually impaired, it can also suggest furniture with visually easy-to-understand designs and colors. This makes it possible to realize interior coordination that takes the user's health condition into consideration.

[0043] The interior coordination system can also suggest optimal coordination based on the user's lifestyle. For example, if the user is a nocturnal person, it can suggest lighting and furniture that will allow them to spend the night comfortably. If the user is a morning person, it can also suggest layouts and curtains that will allow the morning light to easily enter. Furthermore, if the user has an irregular lifestyle, it can also suggest furniture and layouts that can be flexibly adapted to the user's lifestyle. This makes it possible to achieve optimal interior coordination that matches the user's lifestyle.

[0044] The interior coordination system can also make customized suggestions based on the user's hobbies and interests. For example, if the user loves music, it can suggest room layouts and music equipment with good acoustics. If the user enjoys painting, it can suggest the optimal wall color and lighting for displaying paintings. Furthermore, if the user enjoys gardening, it can suggest the optimal layout and furniture for growing plants indoors. This allows for customized interior coordination based on the user's hobbies and interests.

[0045] The interior coordination system can also suggest optimal coordination based on the user's family composition. For example, for a household with children, it can suggest furniture and layout that takes safety into consideration. For a household with pets, it can also suggest furniture made of pet-friendly materials and designs. Furthermore, for a household with elderly people, it can also suggest layouts and furniture that take barrier-free access into consideration. This allows for optimal interior coordination to be achieved that matches the user's family composition.

[0046] The interior coordination system can also make eco-friendly suggestions based on the user's eco-consciousness. For example, it can suggest furniture and accessories made from renewable materials. It can also suggest energy-efficient lighting and home appliances. It can also suggest recyclable decorations and furniture. This allows for eco-friendly interior coordination based on the user's eco-consciousness.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: In the room information input section, the user inputs information such as the room size, layout, ceiling height, etc. For example, the user inputs information such as the room size being 20 square meters, the ceiling height being 2.5 meters, and the layout being 1LDK. Step 2: The photo uploading section allows the user to upload photos taken from all four corners of the room. For example, by uploading photos taken from all four corners of the room, the generative AI can visually understand the room layout and furniture placement. Step 3: The preference information input unit inputs information such as the user's preferred room type (vintage, Scandinavian style, etc.), wall color, renovation budget, etc. For example, the user inputs information such as "I like Scandinavian-style rooms, the wall color is white, and the renovation budget is within 100,000 yen." Step 4: The coordination generation unit generates a coordination based on the information entered by the room information input unit, photo upload unit, and preference information input unit. For example, the generation AI generates a coordination based on a prompt such as, "Please suggest furniture and accessories that go well with white walls in a Scandinavian-style room." Step 5: The image suggestion unit reflects the coordination generated by the coordination generation unit in the room image and makes a suggestion. For example, it combines the coordination suggested by the generation AI with a photo of the room and makes a suggestion in the form of "The room will look like this." Step 6: The recommended furniture selection unit selects recommended furniture and accessories that match the coordination suggested by the image suggestion unit. For example, the generation AI might suggest, "This sofa and table would go well with a Scandinavian-style room." Step 7: The online shopping section can then shop online for the furniture and accessories picked by the recommended furniture picker. For example, the online shopping section can provide links to purchase the furniture and accessories suggested by the AI ​​generator.

[0049] (Example 2) The interior coordination system according to the embodiment of the present invention is a system that allows a user to easily coordinate an ideal room and purchase the necessary furniture and accessories. As a result, the interior coordination system can alleviate the user's anxieties and worries and realize the ideal room.

[0050] An interior coordination system according to an embodiment includes a room information input unit, a photo upload unit, a preference information input unit, a coordinate generation unit, an image suggestion unit, a recommended furniture selection unit, and an online shopping unit. The room information input unit receives input from a user about the size, layout, ceiling height, and other information about the room. For example, the input information is that the room size is 20 square meters, the ceiling height is 2.5 meters, and the layout is a 1LDK (one living room, kitchen, and kitchen). The photo upload unit receives input from a user about photos taken from the four corners of the room. For example, by uploading photos taken from the four corners of the room, the generation AI can visually understand the room layout and furniture arrangement. The preference information input unit receives input from a user about the user's preferred room type (vintage, Scandinavian style, etc.), wall color, and renovation budget. For example, the user inputs information such as "I like Scandinavian-style rooms, with white walls and a renovation budget of less than 100,000 yen." The coordinate generation unit generates a coordinate based on the information input by the room information input unit, the photo upload unit, and the preference information input unit. For example, the generation AI generates a coordination based on a prompt such as, "Please suggest furniture and accessories that would go well with white walls in a Scandinavian-style room." The image suggestion unit proposes the coordination generated by the coordination generation unit by reflecting it in a room image. For example, the generation AI superimposes the coordination proposed by the coordination generation unit onto a photo of the room and suggests, "The room will look like this." The recommended furniture picking unit picks recommended furniture and accessories that go well with the coordination proposed by the image suggestion unit. For example, the generation AI suggests, "This sofa and table would go well with a Scandinavian-style room." The online shopping unit allows online shopping for the furniture and accessories picked by the recommended furniture picking unit. For example, it provides a link where the furniture and accessories proposed by the generation AI can be purchased. In this way, the interior coordination system according to the embodiment allows users to easily coordinate their ideal room and purchase the necessary furniture and accessories.

[0051] The photo upload unit can automatically measure the dimensions of a room and generate a 3D model. For example, when a user uploads photos taken from all four corners of the room, the generation AI uses image analysis technology to automatically measure the room's dimensions. For example, it accurately measures the length of the walls and the height of the ceiling and generates a 3D model. When a user uploads a photo of the room, the generation AI uses image recognition technology to identify the position of furniture and decorations and generates a 3D model of the room based on that. For example, it accurately reproduces the position of a sofa or table. When a user uploads a photo of the room, the generation AI automatically measures the room's dimensions and generates a 3D model. For example, it accurately determines the size and ceiling height of the room and creates a realistic 3D model. This allows for accurate measurement of room dimensions and generation of a 3D model, enabling more realistic room coordination.

[0052] The room information input unit can refer to past data and present examples of coordination for similar rooms. For example, when a user inputs the size and layout of a room, the room information input unit causes the generation AI to refer to a past database and present examples of coordination for similar rooms. For example, it displays examples of coordination for rooms with the same size and layout. Furthermore, when room information is input, the generation AI analyzes past user data and presents successful examples of similar rooms. For example, it displays examples of coordination for rooms with the same ceiling height and wall color tone. Furthermore, when a user inputs room information, the room information input unit causes the generation AI to automatically search for and suggest similar room coordination examples based on past data. For example, it displays examples of coordination for rooms with the same layout and size. In this way, by referring to past data, it is possible to provide coordination examples that can be used as reference for the user.

[0053] The preference information input unit can present customized questions based on the user's lifestyle and hobbies to collect detailed information. For example, when the user inputs information about their room, the preference information input unit causes the generation AI to present customized questions based on the user's lifestyle and hobbies. For example, questions such as "Do you have pets?" or "Do you like reading?" are displayed. Furthermore, when the user inputs information about their room, the preference information input unit causes the generation AI to refer to the user's past data and present customized questions. For example, a question such as "What did you like about your last outfit?" is displayed. Furthermore, when the user inputs information about their room, the preference information input unit causes the generation AI to present customized questions based on the user's hobbies and lifestyle to collect more detailed information. For example, questions such as "Do you like the outdoors?" or "Is cooking your hobby?" are displayed. This allows for more personalized outfit suggestions to be made by collecting detailed information based on the user's lifestyle and hobbies.

[0054] The preference information input unit can perform a cost simulation in real time when the wall color tone and renovation budget are input, and propose the optimal coordination within the budget. For example, when a user inputs the wall color tone and renovation budget, the preference information input unit causes the generation AI to perform a cost simulation in real time and propose the optimal coordination within the budget. For example, the generation AI can propose furniture and accessories that match the wall color within the budget. Furthermore, when the user inputs the wall color tone and renovation budget, the generation AI can perform a cost simulation and propose the optimal coordination within the budget in real time. For example, the generation AI can display a combination of furniture and decorations according to the budget. Furthermore, when a user inputs the wall color tone and renovation budget, the generation AI can perform a cost simulation in real time and propose the optimal coordination within the budget. For example, the generation AI can display the optimal interior plan according to the budget. In this way, the generation AI can perform a cost simulation in real time and propose the optimal coordination within the budget.

[0055] The coordination generation unit can refer to past user data and propose successful coordination patterns. For example, the generation AI in the coordination generation unit analyzes past user data and proposes successful coordination patterns. For example, suggestions are made based on coordination examples that have received high user ratings. Furthermore, when generating a coordination, the generation AI refers to a past database and proposes the most popular coordination patterns. For example, it displays successful examples of rooms with the same style. Furthermore, the coordination generation unit automatically searches for and proposes successful coordination patterns based on past user data. For example, it displays successful examples of rooms with the same floor plan and size. This makes it possible to propose coordination based on past successful examples.

[0056] The image suggestion unit can provide a 360-degree view when reflecting coordination in a room image. For example, when the generation AI reflects coordination in a room image, the image suggestion unit provides a 360-degree view so that the user can check the room from different perspectives. For example, it displays a view that allows a panoramic view in all directions from the center of the room. Furthermore, when the image suggestion unit reflects coordination in a room image, the generation AI provides a 360-degree view to the user so that the user can check the room from different perspectives. For example, it displays a view that allows a detailed view of every corner of the room. Furthermore, when the generation AI reflects coordination in a room image, the image suggestion unit provides a 360-degree view so that the user can check the room from different perspectives. For example, it displays a view looking down on the room from above or a view looking up from the floor. This allows the user to check the room from different perspectives.

[0057] The image suggestion unit can provide a realistic image by simulating the effects of lighting and shadows in real time when reflecting coordination in a room image. For example, the image suggestion unit can provide a realistic image by simulating the effects of lighting and shadows in real time when the generation AI reflects coordination in a room image. For example, the image suggestion unit can simulate the effects of natural light and artificial lighting. Furthermore, the image suggestion unit can provide a realistic image by simulating the effects of lighting and shadows in real time when the generation AI reflects coordination in a room image. For example, the image suggestion unit can simulate lighting conditions during the day and at night. Furthermore, the image suggestion unit can provide a realistic image by simulating the effects of lighting and shadows in real time when the generation AI reflects coordination in a room image. For example, the image suggestion unit can simulate the effects of different lighting fixtures. In this way, a more realistic image can be provided by simulating the effects of lighting and shadows in real time.

[0058] The recommended furniture picker can refer to past purchase data and suggest popular items. For example, the generation AI analyzes past purchase data to suggest popular furniture and accessories. For example, it displays sofas and tables that have received high user ratings. When selecting recommended furniture and accessories, the generation AI refers to a past database to suggest the best-selling items. For example, it displays furniture with popular designs and brands. The generation AI automatically searches for and suggests popular furniture and accessories based on past purchase data. For example, it displays items that suit rooms of the same style. This allows the generation AI to suggest popular items based on past purchase data.

[0059] The recommended furniture picking unit can make customized suggestions based on the user's budget and preferences. In the recommended furniture picking unit, for example, the generation AI suggests furniture and accessories customized based on the user's budget and preferences. For example, sofas and tables that can be purchased within the budget are displayed. When picking recommended furniture and accessories, the generation AI refers to the user's past data and suggests items that match the user's preferences. For example, furniture with a design and color that the user prefers is displayed. In the recommended furniture picking unit, the generation AI automatically searches for and suggests optimal furniture and accessories based on the user's budget and preferences. For example, optimal interior items that can be purchased within the budget are displayed. This makes it possible to make customized suggestions based on the user's budget and preferences.

[0060] The recommended furniture picker can suggest eco-friendly materials and recyclable items. For example, the generation AI of the recommended furniture picker suggests eco-friendly materials and recyclable items. For example, it displays furniture made from recycled wood or recycled plastic. Furthermore, when selecting recommended furniture and accessories, the generation AI suggests eco-friendly materials and recyclable items in real time. For example, it displays eco-friendly paint and recyclable decorations. Furthermore, the recommended furniture picker automatically searches for and suggests eco-friendly materials and recyclable items. For example, it displays furniture and decorations made from sustainable materials. This allows for environmentally conscious coordination by suggesting eco-friendly materials and recyclable items.

[0061] The recommended furniture picker can suggest items from different cultures and regions, providing a global perspective. For example, the generation AI suggests items from different cultures and regions. For example, it displays Scandinavian-style furniture and Asian-style decorative items. When selecting recommended furniture and accessories, the generation AI refers to a past database to suggest items from different cultures and regions. For example, it displays Japanese-style furniture and Moroccan-style decorative items. The generation AI also automatically searches for and suggests items from different cultures and regions. For example, it displays American-style furniture and Italian modern decorative items. This allows the generation AI to suggest items from different cultures and regions, providing a global perspective.

[0062] The recommended furniture picking unit uses an emotion estimation function to analyze the user's emotions in real time when selecting recommended furniture and accessories, and can suggest optimal items. For example, when a user selects recommended furniture and accessories, the generation AI uses facial recognition technology to analyze the user's facial expressions and analyze their emotions in real time. For example, it detects expressions of smiles and surprise and suggests optimal items. In addition, when selecting recommended furniture and accessories, the generation AI uses voice analysis technology to analyze the user's tone of voice and analyze their emotions in real time. For example, it detects tones of excitement and joy and suggests optimal items. In addition, when a user selects recommended furniture and accessories, the generation AI uses an emotion estimation function to analyze their emotions in real time and suggest optimal items. For example, it suggests furniture and accessories that elicit positive emotions. This allows the generation AI to analyze the user's emotions in real time and suggest optimal items, enabling a more satisfying coordination.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The interior coordination system can also make suggestions that take into account the user's health condition. For example, if the user has allergies, it can suggest furniture and accessories made from allergen-free materials. If the user has back pain, it can also suggest chairs and mattresses that are gentle on the back. Furthermore, if the user is visually impaired, it can also suggest furniture with visually easy-to-understand designs and colors. This makes it possible to realize interior coordination that takes the user's health condition into consideration.

[0065] The interior coordination system can also estimate the user's emotions and suggest a relaxing space based on the estimated emotions. For example, if the user is feeling stressed, it can suggest furniture that uses colors and materials that have a relaxing effect. If the user is tired, it can also suggest relaxing lighting and music. Furthermore, if the user is feeling anxious, it can also suggest designs and layouts that give a sense of security. In this way, it is possible to provide a relaxing space based on the user's emotions.

[0066] The interior coordination system can also suggest optimal coordination based on the user's lifestyle. For example, if the user is a nocturnal person, it can suggest lighting and furniture that will allow them to spend the night comfortably. If the user is a morning person, it can also suggest layouts and curtains that will allow the morning light to easily enter. Furthermore, if the user has an irregular lifestyle, it can also suggest furniture and layouts that can be flexibly adapted to the user's lifestyle. This makes it possible to achieve optimal interior coordination that matches the user's lifestyle.

[0067] The interior coordination system can also estimate the user's emotions and make suggestions incorporating entertainment elements based on the estimated emotions. For example, if the user is feeling happy, it can suggest furniture and layouts suitable for playing games or watching movies. If the user is feeling bored, it can suggest interactive decorations or digital art. Furthermore, if the user is excited, it can suggest furniture with energetic colors and designs. This makes it possible to realize interior coordination that incorporates entertainment elements based on the user's emotions.

[0068] The interior coordination system can also make customized suggestions based on the user's hobbies and interests. For example, if the user loves music, it can suggest room layouts and music equipment with good acoustics. If the user enjoys painting, it can suggest the optimal wall color and lighting for displaying paintings. Furthermore, if the user enjoys gardening, it can suggest the optimal layout and furniture for growing plants indoors. This allows for customized interior coordination based on the user's hobbies and interests.

[0069] The interior coordination system can also estimate the user's emotions and make suggestions according to the season based on the estimated emotions. For example, if the user feels cold, furniture using warm colors and materials can be suggested. If the user feels hot, furniture using cool colors and materials can be suggested. Furthermore, if the user feels anxious about the change of seasons, it can also suggest designs and decorations that incorporate a sense of the season. This makes it possible to realize interior coordination according to the season based on the user's emotions.

[0070] The interior coordination system can also suggest optimal coordination based on the user's family composition. For example, for a household with children, it can suggest furniture and layout that takes safety into consideration. For a household with pets, it can also suggest furniture made of pet-friendly materials and designs. Furthermore, for a household with elderly people, it can also suggest layouts and furniture that take barrier-free access into consideration. This allows for optimal interior coordination to be achieved that matches the user's family composition.

[0071] The interior coordination system can also estimate the user's emotions and make suggestions that will have a refreshing effect based on the estimated emotions. For example, if the user is feeling tired, it can suggest furniture that uses colors and materials that have a refreshing effect. Also, if the user is feeling stressed, it can suggest lighting and music that will have a relaxing effect. Furthermore, if the user is looking for a change of mood, it can also suggest new designs and layouts. This makes it possible to realize interior coordination that has a refreshing effect based on the user's emotions.

[0072] The interior coordination system can also make eco-friendly suggestions based on the user's eco-consciousness. For example, it can suggest furniture and accessories made from renewable materials. It can also suggest energy-efficient lighting and home appliances. It can also suggest recyclable decorations and furniture. This allows for eco-friendly interior coordination based on the user's eco-consciousness.

[0073] The interior coordination system can also estimate the user's emotions and suggest sociable spaces based on the estimated emotions. For example, if the user is feeling happy, it can suggest sociable spaces for spending time with friends and family. If the user is feeling lonely, it can also suggest layouts and furniture that encourage communication. Furthermore, if the user wants to enjoy a party, it can also suggest designs and decorations that are suitable for a party. In this way, it is possible to provide sociable spaces based on the user's emotions.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: In the room information input section, the user inputs information such as the room size, layout, ceiling height, etc. For example, the user inputs information such as the room size being 20 square meters, the ceiling height being 2.5 meters, and the layout being 1LDK. Step 2: The photo uploading section allows the user to upload photos taken from all four corners of the room. For example, by uploading photos taken from all four corners of the room, the generative AI can visually understand the room layout and furniture placement. Step 3: The preference information input unit inputs information such as the user's preferred room type (vintage, Scandinavian style, etc.), wall color, renovation budget, etc. For example, the user inputs information such as "I like Scandinavian-style rooms, the wall color is white, and the renovation budget is within 100,000 yen." Step 4: The coordination generation unit generates a coordination based on the information entered by the room information input unit, photo upload unit, and preference information input unit. For example, the generation AI generates a coordination based on a prompt such as, "Please suggest furniture and accessories that go well with white walls in a Scandinavian-style room." Step 5: The image suggestion unit reflects the coordination generated by the coordination generation unit in the room image and makes a suggestion. For example, it combines the coordination suggested by the generation AI with a photo of the room and makes a suggestion in the form of "The room will look like this." Step 6: The recommended furniture selection unit selects recommended furniture and accessories that match the coordination suggested by the image suggestion unit. For example, the generation AI might suggest, "This sofa and table would go well with a Scandinavian-style room." Step 7: The online shopping section can then shop online for the furniture and accessories picked by the recommended furniture picker. For example, the online shopping section can provide links to purchase the furniture and accessories suggested by the AI ​​generator.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 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 room information input unit for inputting room information; A photo upload section where you can upload photos of your room, A preference information input section where you can input your preferred room type, wall color, and renovation budget; a coordinate generating unit that generates a coordinate based on the information input by the room information input unit, the photo upload unit, and the preference information input unit; an image suggestion unit that suggests a room image by reflecting the coordinates generated by the coordinate generation unit; a recommended furniture picking unit that picks out recommended furniture and accessories that match the coordination suggested by the image suggesting unit; an online shopping unit that allows online shopping for furniture and accessories picked up by the recommended furniture picking unit; A system characterized by:

2. The photo upload unit Automatically measure the dimensions of the room and generate the 3D model 2. The system of claim 1.

3. The room information input unit Refer to past data to present examples of similar room coordination 2. The system of claim 1.

4. The preference information input unit Gather detailed information by asking customized questions based on your lifestyle and interests 2. The system of claim 1.

5. The preference information input unit When entering the wall color and renovation budget, a cost simulation is performed in real time to suggest the optimal coordination within the budget.

2. The system of claim 1.

Images