System

Abstract

An object of a system according to an embodiment is to efficiently coordinate rooms based on a user's preference.SOLUTION: A system according to an embodiment includes a generation AI, an image generation AI, an AR function, and an online shopping site. The generation AI extracts user preferences. The image generation AI creates an image based on the user preferences extracted by the generation AI. The AR function enables an image created by the image generation AI to be confirmed on a smartphone screen as an image arranged in an actual room. The online shopping site enables the purchase of proposed furnishings and interiors based on the image created by the image creation AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to efficiently coordinate a room, and users have had the problem of spending time and effort finding furniture and interior decor that suits their preferences.

[0005] The system according to the embodiment aims to efficiently coordinate a room based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an image generation AI, an AR function, and an online shopping site. The generation AI extracts user preferences. The image generation AI creates images based on the user preferences extracted by the generation AI. The AR function allows users to view images created by the image generation AI placed in an actual room on their smartphone screen. The online shopping site allows users to purchase furniture and interior design suggested based on the images created by the image generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently coordinate a room based on the user's preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A room coordination system according to an embodiment of the present invention is a system that proposes furniture and interior decor based on a user's preferences and room conditions, and allows the user to check on a smartphone screen how the proposals would look in an actual room. As a result, the room coordination system proposes room coordination based on the user's preferences, allows the user to check how the proposals would look in an actual room, and allows the user to purchase the proposed furniture and interior decor on an online shopping site.

[0029] A room coordination system according to an embodiment includes a generation AI, an image generation AI, an AR function, and an online shopping site. The generation AI extracts user preferences. For example, the generation AI collects information such as "size and layout," "style," "budget," and "favorite colors" through conversations with the user to extract the user's preferences. Furthermore, when a user expresses a request such as "I want modern furniture that matches my spacious living room," the generation AI understands the request and suggests appropriate furniture and interior decor. The image generation AI creates images based on the user's preferences extracted by the generation AI. For example, when a user expresses a request such as "I want a modern living room with a blue theme," the image generation AI generates an image of a modern living room with a blue theme based on the request. The AR function allows users to view the image created by the image generation AI arranged in an actual room on their smartphone screen. For example, by pointing their smartphone camera at the living room, they can see the generated modern furniture and interior decor arranged in the actual room on their smartphone screen. The online shopping site allows users to purchase furniture and interior decor suggested based on the image created by the image generation AI. For example, a user can purchase their favorite furniture or interior items directly from an online shopping site. This allows the room coordination system to suggest room coordination based on the user's preferences, allowing the user to check the image of the items arranged in the actual room, and then purchase the suggested furniture or interior items from the online shopping site.

[0030] Generative AI can analyze a user's past purchase history and browsing history to extract latent preferences. For example, generative AI can analyze a user's past purchase history to extract preferences for specific brands and styles. For example, it can identify a user's preferred taste based on the design and color of furniture purchased in the past. Generative AI can also analyze a user's browsing history to extract the styles and colors of interior design that are frequently viewed. For example, it can understand preference trends based on information from interior design magazines and websites that the user frequently visits. Generative AI can also integrate purchase history and browsing history to extract a user's latent preferences. For example, it can find commonalities between furniture purchased in the past and interior design viewed, more accurately identifying the user's preferences. This makes it possible to extract latent preferences more accurately by analyzing a user's past purchase history and browsing history.

[0031] Generative AI can analyze a user's social media posts and blog articles and make suggestions that reflect their lifestyle, hobbies, and preferences. For example, generative AI analyzes a user's social media posts to understand their lifestyle, hobbies, and preferences. For example, it can identify a user's preferred interior style and colors from the photos and comments they post. Generative AI can also analyze a user's blog articles to make interior suggestions based on their lifestyle and hobbies. For example, it can extract a user's preferred furniture design and functions from the content of the articles they write. Generative AI can also integrate data from social media and blogs to make interior suggestions that suit the user's lifestyle. For example, it can suggest furniture made from natural materials to a user who enjoys the outdoors. This makes it possible to analyze a user's social media posts and blog articles to make suggestions that reflect their lifestyle, hobbies, and preferences.

[0032] The generative AI also takes into account the preferences of the user's family and housemates, making suggestions that will satisfy everyone. For example, the generative AI collects the preferences of the user's family and housemates and makes interior suggestions that will satisfy everyone. For example, it can suggest furniture and color combinations that reflect the preferences of all family members. The generative AI also takes into account the preferences of family members and housemates and makes suggestions based on their shared hobbies and lifestyles. For example, if everyone loves the outdoors, it can suggest interiors that use natural materials. The generative AI can also collect the opinions of family members and housemates in real time and make optimal suggestions. For example, it can conduct an online survey in which all family members participate and make suggestions based on the results. This makes it possible to take into account the preferences of family members and housemates and make suggestions that will satisfy everyone.

[0033] Generative AI can suggest interior design ideas based on the user's preferred music and movie genres. For example, generative AI can analyze a user's music playlist and suggest interior design ideas based on the user's preferred music genre. For example, a user who likes jazz can be suggested a classical interior design. Generative AI can also analyze the user's preferred movie genre and suggest interior design ideas that match that atmosphere. For example, a user who likes science fiction movies can be suggested furniture with a modern and futuristic design. Generative AI can also integrate music and movie data to suggest interior design ideas that match the user's preferences. For example, an industrial style can be suggested for a user who likes rock music and action movies. This allows generative AI to suggest interior design ideas based on the user's preferred music and movie genres.

[0034] Image generation AI can generate images under different lighting conditions and time of day, providing users with multiple perspectives. For example, image generation AI can generate images under different lighting conditions, providing users with how an interior looks during the day and at night. For example, it can generate images that reflect the difference between natural light and artificial lighting. Image generation AI can also generate images for different time of day, providing users with how an interior looks in the morning, afternoon, and evening. For example, it can generate images that reflect the soft light of morning and the calm light of evening. Image generation AI can also generate images that reflect lighting conditions by season, providing users with multiple perspectives. For example, it can generate images that reflect the bright light of summer and the warm light of winter. This allows users to have multiple perspectives by generating images under different lighting conditions and time of day.

[0035] Image generation AI provides an interface that allows users to input specific feedback on generated images, allowing the image to be improved over time. For example, image generation AI provides an interface that allows users to input specific feedback on generated images. For example, adding a function that allows users to request changes to color or design. Image generation AI also builds a system that improves images over time based on user feedback. For example, it regenerates an image to match the color or style specified by the user. Image generation AI also reflects feedback in real time and improves the image until the user is satisfied. For example, it provides a function that regenerates an image as many times as the user is satisfied. This allows the image to be improved over time based on user feedback, allowing the image to be adjusted until the user is satisfied.

[0036] Image generation AI can generate interior images that match different seasons and events. For example, image generation AI generates interior images that match different seasons. For example, it might suggest bright-colored interiors in spring and warm-colored interiors in winter. Image generation AI also generates interior images that match specific events. For example, it might generate images that reflect decorations for Christmas or Halloween. Image generation AI can also build systems that suggest interior designs for each season or event. For example, it might suggest interior designs that reflect seasonal trends. This allows it to generate interior images that match different seasons and events, making it possible to provide a variety of suggestions to users.

[0037] Image generation AI can generate images that combine new proposals with the user's existing furniture and interior decor. For example, image generation AI analyzes the user's existing furniture and interior decor and generates images that combine them with new proposals. For example, it generates an image that combines an existing sofa with a new table. Image generation AI also builds a system that allows users to upload photos of their furniture and makes new proposals based on those photos. For example, it can suggest curtains or rugs that go with existing furniture. Image generation AI also generates images in real time that combine existing interior decor with new proposals. For example, it can rearrange the user's furniture and generate an image that reflects the new proposal. This allows it to generate images that combine existing furniture and interior decor with new proposals, making it possible to provide optimal proposals to users.

[0038] The AR function can automatically measure the dimensions of a room and furniture layout and suggest the optimal layout. For example, the AR function can automatically measure the dimensions of a room using a smartphone camera and suggest the optimal furniture layout. For example, the furniture layout can be adjusted to suit the size of the room. The AR function can also build a system that automatically measures furniture layout and suggests the optimal layout. For example, it can suggest furniture layout taking into account the shape of the room and the position of windows. The AR function can also suggest the optimal furniture layout in real time based on room dimension data. For example, as the user moves furniture, the optimal layout can be displayed in real time. This makes it possible to suggest the optimal layout by automatically measuring the dimensions of a room and furniture layout.

[0039] The AR function can provide an interactive interface that allows the user to use the AR function to try out different furniture arrangements. For example, the AR function can provide an interface that allows the user to freely try out different furniture arrangements. For example, a function can be added to move furniture by dragging and dropping. The AR function can also build a system that allows the user to change the furniture arrangement in real time through an interactive interface. For example, a function can be provided to change the size or color of furniture. The AR function can also provide an interface that allows the user to try out different furniture arrangements using the AR function and save the results. For example, a function can be added to save multiple arrangement patterns and compare them later. This allows the user to find the optimal arrangement by providing an interactive interface that allows the user to try out different furniture arrangements.

[0040] The AR function can provide layout images from different viewpoints, allowing users to view them from multiple angles. For example, the AR function can provide layout images from different viewpoints, allowing users to view them from multiple angles. For example, it can provide a top-down view or an oblique view. The AR function can also provide an interface that allows users to freely switch between layout images from different viewpoints using the AR function. For example, it can add a function that allows users to rotate the image 360 ​​degrees and view it. The AR function can also generate layout images from different viewpoints in real time, building a system that allows users to view them from multiple angles. For example, it can provide a function that changes the viewpoint when the user moves their smartphone. This allows users to view layout images from different viewpoints, allowing them to view them from multiple angles.

[0041] The AR function can provide a function that allows a user to save the layout of furniture that they have arranged using the AR function and reconfirm it later. For example, the AR function provides a function that allows a user to save the layout of furniture that they have arranged using the AR function and reconfirm it later. For example, a function is added that allows a layout pattern to be saved and recalled later. The AR function also provides an interface that allows a user to reconfirm the layout data based on the saved layout. For example, a function is provided that displays a list of saved layouts and allows a user to select and reconfirm them. The AR function also builds a system that allows a user to edit and reconfirm the saved layout data. For example, a function is provided that allows a user to try and error new layouts based on the saved layout. This allows a user to find the optimal layout by saving the layout of furniture that they have arranged and reconfirming it later.

[0042] Online shopping sites can analyze a user's purchase history and browsing history to suggest related products. For example, an online shopping site may analyze a user's purchase history to suggest related products. For example, it may suggest interior accessories that go well with furniture previously purchased. An online shopping site may also analyze a user's browsing history to suggest products that the user may be interested in. For example, it may suggest related products based on frequently viewed product categories. An online shopping site may also integrate purchase history and browsing history to build a system that suggests products that match the user's preferences. For example, it may suggest new products that the user may be interested in based on past purchase history and browsing history. In this way, it is possible to suggest related products by analyzing a user's purchase history and browsing history.

[0043] An online shopping site can display other users' reviews and ratings in real time for a product that a user is considering purchasing. For example, an online shopping site displays other users' reviews and ratings in real time for a product that a user is considering purchasing. For example, a function for displaying the latest reviews and ratings is added. The online shopping site also builds a system that filters and displays related reviews and ratings for a product that a user is considering purchasing. For example, reviews for the same purpose or conditions are preferentially displayed. The online shopping site also updates the reviews and ratings in real time, allowing users to consider purchases based on the latest information. For example, a function is provided that updates the display every time a new review is posted. This allows other users' reviews and ratings to be displayed in real time, providing reference information when a user is considering a purchase.

[0044] An online shopping site can provide an interface that makes it easy to compare products from different brands and price ranges. An online shopping site may provide an interface that makes it easy to compare products from different brands and price ranges. For example, a function may be added that allows multiple products to be compared side by side. An online shopping site may also provide a filtering function that allows users to easily compare products from different brands and price ranges. For example, a function may be added that allows users to narrow down the results and display them by price or brand. An online shopping site may also provide an interface that visually displays the comparison results and allows users to intuitively understand them. For example, a function may be added that displays the comparison results using graphs or charts. In this way, an interface that makes it easy to compare products from different brands and price ranges is provided, allowing users to select the most suitable product.

[0045] An online shopping site may provide a function that allows users to share purchased products with other users and receive feedback. For example, the online shopping site may provide a function that allows users to share purchased products with other users and receive feedback. For example, the online shopping site may add a function that allows users to post reviews and comments after purchase. The online shopping site may also build a system that allows users to share purchased products on social media and receive feedback from other users. For example, the online shopping site may provide a function that allows users to post photos of purchased products and receive comments. The online shopping site may also provide a system that updates ratings of purchased products in real time based on feedback. For example, the online shopping site may add a function that updates ratings every time a new review is posted. This allows users to share purchased products with other users and receive feedback, thereby improving product ratings.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The room coordination system can also monitor the user's health and make health-conscious interior design suggestions. For example, it can analyze the user's sleep data and suggest beds and lighting that will provide a comfortable sleeping environment. It can also suggest a relaxing living room layout based on the user's exercise data. It can also monitor the user's stress level and suggest interior design that will have a relaxing effect. This makes it possible to suggest optimal interior design based on the user's health.

[0048] The room coordination system can also make interior suggestions that take into account the preferences and habits of the user's pet. For example, it analyzes pet behavior data to suggest a space where the pet can live comfortably. It can also suggest furniture and interior design that matches the pet's preferences. It can also monitor the pet's health and make interior suggestions that take health into consideration. This makes it possible to suggest the optimal interior design based on the pet's preferences and health.

[0049] The room coordination system can also analyze the user's travel history and make interior design suggestions that reflect the atmosphere of the travel destination. For example, it can suggest interior designs that incorporate the designs and colors of the countries and regions the user has visited. It can also suggest decorations that reflect memories of the travel destination. It can also suggest interior designs that utilize items the user purchased at their travel destination. This makes it possible to make unique interior design suggestions based on the user's travel history.

[0050] The room coordination system can also make interior design suggestions that reflect the user's hobbies and special skills. For example, if the user's hobby is painting, it can suggest the optimal layout for displaying artwork. If the user's hobby is music, it can also suggest space for storing musical instruments. Furthermore, if the user's hobby is cooking, it can also suggest an optimal kitchen layout. This makes it possible to make optimal interior design suggestions based on the user's hobbies and special skills.

[0051] The room coordination system can also suggest interior designs that reflect the user's eco-consciousness. For example, it can suggest furniture and interior designs made from renewable materials. It can also suggest energy-efficient lighting and home appliances. It can also suggest interior designs that incorporate plants to improve indoor air quality. This makes it possible to suggest sustainable interior designs based on the user's eco-consciousness.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The generation AI extracts the user's preferences. For example, the generation AI collects information such as "size and layout," "taste," "budget," and "favorite colors" through conversation with the user to extract the user's preferences. Furthermore, when the user expresses a request such as "I want modern furniture that matches my spacious living room," the generation AI understands that request and suggests appropriate furniture and interior design. Step 2: The image generation AI creates an image based on the user's preferences extracted by the generation AI. For example, if the user expresses a desire for a modern living room with a blue theme, the image generation AI will generate an image of a modern living room with a blue theme based on that desire. Step 3: The AR function allows users to view the image created by the image generation AI placed in a real room on their smartphone screen. For example, if a user points their smartphone camera at a living room, they can see the generated modern furniture and interior design placed in the real room on their smartphone screen. Step 4: The online shopping site allows users to purchase furniture and interior items suggested based on the images created by the image generation AI. For example, users can purchase the furniture and interior items they like directly on the online shopping site.

[0054] (Example 2) A room coordination system according to an embodiment of the present invention is a system that proposes furniture and interior decor based on a user's preferences and room conditions, and allows the user to check on a smartphone screen how the proposals would look in an actual room. As a result, the room coordination system proposes room coordination based on the user's preferences, allows the user to check how the proposals would look in an actual room, and allows the user to purchase the proposed furniture and interior decor on an online shopping site.

[0055] A room coordination system according to an embodiment includes a generation AI, an image generation AI, an AR function, and an online shopping site. The generation AI extracts user preferences. For example, the generation AI collects information such as "size and layout," "style," "budget," and "favorite colors" through conversations with the user to extract the user's preferences. Furthermore, when a user expresses a request such as "I want modern furniture that matches my spacious living room," the generation AI understands the request and suggests appropriate furniture and interior decor. The image generation AI creates images based on the user's preferences extracted by the generation AI. For example, when a user expresses a request such as "I want a modern living room with a blue theme," the image generation AI generates an image of a modern living room with a blue theme based on the request. The AR function allows users to view the image created by the image generation AI arranged in an actual room on their smartphone screen. For example, by pointing their smartphone camera at the living room, they can see the generated modern furniture and interior decor arranged in the actual room on their smartphone screen. The online shopping site allows users to purchase furniture and interior decor suggested based on the image created by the image generation AI. For example, a user can purchase their favorite furniture or interior items directly from an online shopping site. This allows the room coordination system to suggest room coordination based on the user's preferences, allowing the user to check the image of the items arranged in the actual room, and then purchase the suggested furniture or interior items from the online shopping site.

[0056] Generative AI can analyze a user's past purchase history and browsing history to extract latent preferences. For example, generative AI can analyze a user's past purchase history to extract preferences for specific brands and styles. For example, it can identify a user's preferred taste based on the design and color of furniture purchased in the past. Generative AI can also analyze a user's browsing history to extract the styles and colors of interior design that are frequently viewed. For example, it can understand preference trends based on information from interior design magazines and websites that the user frequently visits. Generative AI can also integrate purchase history and browsing history to extract a user's latent preferences. For example, it can find commonalities between furniture purchased in the past and interior design viewed, more accurately identifying the user's preferences. This makes it possible to extract latent preferences more accurately by analyzing a user's past purchase history and browsing history.

[0057] Generative AI can analyze a user's social media posts and blog articles and make suggestions that reflect their lifestyle, hobbies, and preferences. For example, generative AI analyzes a user's social media posts to understand their lifestyle, hobbies, and preferences. For example, it can identify a user's preferred interior style and colors from the photos and comments they post. Generative AI can also analyze a user's blog articles to make interior suggestions based on their lifestyle and hobbies. For example, it can extract a user's preferred furniture design and functions from the content of the articles they write. Generative AI can also integrate data from social media and blogs to make interior suggestions that suit the user's lifestyle. For example, it can suggest furniture made from natural materials to a user who enjoys the outdoors. This makes it possible to analyze a user's social media posts and blog articles to make suggestions that reflect their lifestyle, hobbies, and preferences.

[0058] The generative AI can use its emotion estimation function to analyze the emotions expressed by the user during a conversation and make suggestions that will elicit a positive response. For example, the generative AI uses its emotion estimation function to analyze the user's emotions in real time during a conversation. For example, if the user has a positive response to a particular interior style, the generative AI will suggest that style. The generative AI also uses its emotion estimation function to analyze changes in the user's emotions during a conversation and make optimal suggestions. For example, if the user wants to relax, the generative AI will suggest interior decor with calming colors. The generative AI also accumulates user emotional data and makes suggestions based on past emotional responses. For example, it will prioritize suggestions for interior styles that have previously elicited positive responses. In this way, the emotion estimation function makes it possible to make optimal suggestions based on the user's emotions.

[0059] The generative AI also takes into account the preferences of the user's family and housemates, making suggestions that will satisfy everyone. For example, the generative AI collects the preferences of the user's family and housemates and makes interior suggestions that will satisfy everyone. For example, it can suggest furniture and color combinations that reflect the preferences of all family members. The generative AI also takes into account the preferences of family members and housemates and makes suggestions based on their shared hobbies and lifestyles. For example, if everyone loves the outdoors, it can suggest interiors that use natural materials. The generative AI can also collect the opinions of family members and housemates in real time and make optimal suggestions. For example, it can conduct an online survey in which all family members participate and make suggestions based on the results. This makes it possible to take into account the preferences of family members and housemates and make suggestions that will satisfy everyone.

[0060] Generative AI can suggest interior design ideas based on the user's preferred music and movie genres. For example, generative AI can analyze a user's music playlist and suggest interior design ideas based on the user's preferred music genre. For example, a user who likes jazz can be suggested a classical interior design. Generative AI can also analyze the user's preferred movie genre and suggest interior design ideas that match that atmosphere. For example, a user who likes science fiction movies can be suggested furniture with a modern and futuristic design. Generative AI can also integrate music and movie data to suggest interior design ideas that match the user's preferences. For example, an industrial style can be suggested for a user who likes rock music and action movies. This allows generative AI to suggest interior design ideas based on the user's preferred music and movie genres.

[0061] The generation AI uses the emotion estimation function to monitor the user's emotions in real time when receiving suggestions, allowing it to continuously make optimal suggestions. For example, when making suggestions, the generation AI uses the emotion estimation function to monitor the user's emotions in real time. For example, if the user has a positive reaction to a suggestion, it will continuously suggest that style. The generation AI also builds a system that dynamically adjusts the content of suggestions based on the user's emotion data. For example, if there are a lot of negative reactions, it will change the content of the suggestion. The generation AI also accumulates emotion estimation data and makes optimal suggestions based on past reactions. For example, it will prioritize suggestions that have shown positive reactions in the past. In this way, the emotion estimation function allows it to continuously make optimal suggestions based on the user's emotions.

[0062] Image generation AI can generate images under different lighting conditions and time of day, providing users with multiple perspectives. For example, image generation AI can generate images under different lighting conditions, providing users with how an interior looks during the day and at night. For example, it can generate images that reflect the difference between natural light and artificial lighting. Image generation AI can also generate images for different time of day, providing users with how an interior looks in the morning, afternoon, and evening. For example, it can generate images that reflect the soft light of morning and the calm light of evening. Image generation AI can also generate images that reflect lighting conditions by season, providing users with multiple perspectives. For example, it can generate images that reflect the bright light of summer and the warm light of winter. This allows users to have multiple perspectives by generating images under different lighting conditions and time of day.

[0063] Image generation AI provides an interface that allows users to input specific feedback on generated images, allowing the image to be improved over time. For example, image generation AI provides an interface that allows users to input specific feedback on generated images. For example, adding a function that allows users to request changes to color or design. Image generation AI also builds a system that improves images over time based on user feedback. For example, it regenerates an image to match the color or style specified by the user. Image generation AI also reflects feedback in real time and improves the image until the user is satisfied. For example, it provides a function that regenerates an image as many times as the user is satisfied. This allows the image to be improved over time based on user feedback, allowing the image to be adjusted until the user is satisfied.

[0064] Image generation AI can generate interior images that match different seasons and events. For example, image generation AI generates interior images that match different seasons. For example, it might suggest bright-colored interiors in spring and warm-colored interiors in winter. Image generation AI also generates interior images that match specific events. For example, it might generate images that reflect decorations for Christmas or Halloween. Image generation AI can also build systems that suggest interior designs for each season or event. For example, it might suggest interior designs that reflect seasonal trends. This allows it to generate interior images that match different seasons and events, making it possible to provide a variety of suggestions to users.

[0065] Image generation AI can generate images that combine new proposals with the user's existing furniture and interior decor. For example, image generation AI analyzes the user's existing furniture and interior decor and generates images that combine them with new proposals. For example, it generates an image that combines an existing sofa with a new table. Image generation AI also builds a system that allows users to upload photos of their furniture and makes new proposals based on those photos. For example, it can suggest curtains or rugs that go with existing furniture. Image generation AI also generates images in real time that combine existing interior decor with new proposals. For example, it can rearrange the user's furniture and generate an image that reflects the new proposal. This allows it to generate images that combine existing furniture and interior decor with new proposals, making it possible to provide optimal proposals to users.

[0066] Image generation AI can use the emotion estimation function to monitor the user's emotions in real time when viewing an image and continuously generate optimal images. For example, image generation AI can use the emotion estimation function to monitor the user's emotions in real time for the images it generates. For example, it can prioritize the generation of images to which the user has a positive reaction. Furthermore, image generation AI can build a system that continuously generates optimal images based on user emotion data. For example, it can generate images that reflect colors and designs that the user finds relaxing. Furthermore, image generation AI accumulates emotion estimation data and generates optimal images based on past reactions. For example, it can generate new images based on images that have previously elicited positive reactions. In this way, the emotion estimation function can continuously generate optimal images based on the user's emotions.

[0067] The AR function can automatically measure the dimensions of a room and furniture layout and suggest the optimal layout. For example, the AR function can automatically measure the dimensions of a room using a smartphone camera and suggest the optimal furniture layout. For example, the furniture layout can be adjusted to suit the size of the room. The AR function can also build a system that automatically measures furniture layout and suggests the optimal layout. For example, it can suggest furniture layout taking into account the shape of the room and the position of windows. The AR function can also suggest the optimal furniture layout in real time based on room dimension data. For example, as the user moves furniture, the optimal layout can be displayed in real time. This makes it possible to suggest the optimal layout by automatically measuring the dimensions of a room and furniture layout.

[0068] The AR function can provide an interactive interface that allows the user to use the AR function to try out different furniture arrangements. For example, the AR function can provide an interface that allows the user to freely try out different furniture arrangements. For example, a function can be added to move furniture by dragging and dropping. The AR function can also build a system that allows the user to change the furniture arrangement in real time through an interactive interface. For example, a function can be provided to change the size or color of furniture. The AR function can also provide an interface that allows the user to try out different furniture arrangements using the AR function and save the results. For example, a function can be added to save multiple arrangement patterns and compare them later. This allows the user to find the optimal arrangement by providing an interactive interface that allows the user to try out different furniture arrangements.

[0069] The AR function uses the emotion estimation function to analyze the emotions felt by the user when checking the layout in AR, and can suggest layouts that elicit a positive response. For example, when checking furniture layout, the AR function uses the emotion estimation function to analyze the user's emotions in real time. For example, it may prioritize and suggest layouts that elicit a positive response from the user. The AR function also builds a system that suggests layouts that elicit a positive response based on the user's emotion data. For example, it may suggest layouts that help the user relax. The AR function also accumulates emotion estimation data and suggests optimal layouts based on past responses. For example, it may suggest new layouts based on layouts that elicited positive responses in the past. In this way, the emotion estimation function can be used to suggest optimal layouts based on the user's emotions.

[0070] The AR function can provide layout images from different viewpoints, allowing users to view them from multiple angles. For example, the AR function can provide layout images from different viewpoints, allowing users to view them from multiple angles. For example, it can provide a top-down view or an oblique view. The AR function can also provide an interface that allows users to freely switch between layout images from different viewpoints using the AR function. For example, it can add a function that allows users to rotate the image 360 ​​degrees and view it. The AR function can also generate layout images from different viewpoints in real time, building a system that allows users to view them from multiple angles. For example, it can provide a function that changes the viewpoint when the user moves their smartphone. This allows users to view layout images from different viewpoints, allowing them to view them from multiple angles.

[0071] The AR function can provide a function that allows a user to save the layout of furniture that they have arranged using the AR function and reconfirm it later. For example, the AR function provides a function that allows a user to save the layout of furniture that they have arranged using the AR function and reconfirm it later. For example, a function is added that allows a layout pattern to be saved and recalled later. The AR function also provides an interface that allows a user to reconfirm the layout data based on the saved layout. For example, a function is provided that displays a list of saved layouts and allows a user to select and reconfirm them. The AR function also builds a system that allows a user to edit and reconfirm the saved layout data. For example, a function is provided that allows a user to try and error new layouts based on the saved layout. This allows a user to find the optimal layout by saving the layout of furniture that they have arranged and reconfirming it later.

[0072] The AR function uses the emotion estimation function to monitor the user's emotions in real time when checking the layout, and can continuously suggest optimal layouts. For example, when checking furniture layout, the AR function uses the emotion estimation function to monitor the user's emotions in real time. For example, it may preferentially suggest layouts that the user has a positive reaction to. The AR function also builds a system that continuously suggests optimal layouts based on the user's emotion data. For example, it may suggest layouts that help the user relax. The AR function also accumulates emotion estimation data and suggests optimal layouts based on past reactions. For example, it may suggest new layouts based on layouts that have previously shown positive reactions. In this way, the emotion estimation function can continuously suggest optimal layouts based on the user's emotions.

[0073] Online shopping sites can analyze a user's purchase history and browsing history to suggest related products. For example, an online shopping site may analyze a user's purchase history to suggest related products. For example, it may suggest interior accessories that go well with furniture previously purchased. An online shopping site may also analyze a user's browsing history to suggest products that the user may be interested in. For example, it may suggest related products based on frequently viewed product categories. An online shopping site may also integrate purchase history and browsing history to build a system that suggests products that match the user's preferences. For example, it may suggest new products that the user may be interested in based on past purchase history and browsing history. In this way, it is possible to suggest related products by analyzing a user's purchase history and browsing history.

[0074] An online shopping site can display other users' reviews and ratings in real time for a product that a user is considering purchasing. For example, an online shopping site displays other users' reviews and ratings in real time for a product that a user is considering purchasing. For example, a function for displaying the latest reviews and ratings is added. The online shopping site also builds a system that filters and displays related reviews and ratings for a product that a user is considering purchasing. For example, reviews for the same purpose or conditions are preferentially displayed. The online shopping site also updates the reviews and ratings in real time, allowing users to consider purchases based on the latest information. For example, a function is provided that updates the display every time a new review is posted. This allows other users' reviews and ratings to be displayed in real time, providing reference information when a user is considering a purchase.

[0075] An online shopping site can provide an interface that makes it easy to compare products from different brands and price ranges. An online shopping site may provide an interface that makes it easy to compare products from different brands and price ranges. For example, a function may be added that allows multiple products to be compared side by side. An online shopping site may also provide a filtering function that allows users to easily compare products from different brands and price ranges. For example, a function may be added that allows users to narrow down the results and display them by price or brand. An online shopping site may also provide an interface that visually displays the comparison results and allows users to intuitively understand them. For example, a function may be added that displays the comparison results using graphs or charts. In this way, an interface that makes it easy to compare products from different brands and price ranges is provided, allowing users to select the most suitable product.

[0076] An online shopping site may provide a function that allows users to share purchased products with other users and receive feedback. For example, the online shopping site may provide a function that allows users to share purchased products with other users and receive feedback. For example, the online shopping site may add a function that allows users to post reviews and comments after purchase. The online shopping site may also build a system that allows users to share purchased products on social media and receive feedback from other users. For example, the online shopping site may provide a function that allows users to post photos of purchased products and receive comments. The online shopping site may also provide a system that updates ratings of purchased products in real time based on feedback. For example, the online shopping site may add a function that updates ratings every time a new review is posted. This allows users to share purchased products with other users and receive feedback, thereby improving product ratings.

[0077] An online shopping site can use an emotion estimation function to monitor a user's emotions in real time when they are considering a purchase and continuously make optimal suggestions. For example, an online shopping site can use an emotion estimation function to monitor a user's emotions in real time when they are considering a purchase. For example, the site can prioritize suggesting products to which the user has responded positively. The online shopping site can also build a system that continuously makes optimal suggestions based on the user's emotion data. For example, the system can suggest products that will help the user relax. The online shopping site can also accumulate emotion estimation data and make optimal suggestions based on past responses. For example, the system can make new suggestions based on products that have previously received positive responses. In this way, the emotion estimation function can continuously make optimal suggestions based on the user's emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The room coordination system can also monitor the user's health and make health-conscious interior design suggestions. For example, it can analyze the user's sleep data and suggest beds and lighting that will provide a comfortable sleeping environment. It can also suggest a relaxing living room layout based on the user's exercise data. It can also monitor the user's stress level and suggest interior design that will have a relaxing effect. This makes it possible to suggest optimal interior design based on the user's health.

[0080] The room coordination system can also make interior suggestions that take into account the preferences and habits of the user's pet. For example, it analyzes pet behavior data to suggest a space where the pet can live comfortably. It can also suggest furniture and interior design that matches the pet's preferences. It can also monitor the pet's health and make interior suggestions that take health into consideration. This makes it possible to suggest the optimal interior design based on the pet's preferences and health.

[0081] The room coordination system can also analyze the user's travel history and make interior design suggestions that reflect the atmosphere of the travel destination. For example, it can suggest interior designs that incorporate the designs and colors of the countries and regions the user has visited. It can also suggest decorations that reflect memories of the travel destination. It can also suggest interior designs that utilize items the user purchased at their travel destination. This makes it possible to make unique interior design suggestions based on the user's travel history.

[0082] The room coordination system can also make interior design suggestions that reflect the user's hobbies and special skills. For example, if the user's hobby is painting, it can suggest the optimal layout for displaying artwork. If the user's hobby is music, it can also suggest space for storing musical instruments. Furthermore, if the user's hobby is cooking, it can also suggest an optimal kitchen layout. This makes it possible to make optimal interior design suggestions based on the user's hobbies and special skills.

[0083] The room coordination system can also suggest interior designs that reflect the user's eco-consciousness. For example, it can suggest furniture and interior designs made from renewable materials. It can also suggest energy-efficient lighting and home appliances. It can also suggest interior designs that incorporate plants to improve indoor air quality. This makes it possible to suggest sustainable interior designs based on the user's eco-consciousness.

[0084] The room coordination system uses its emotion estimation function to analyze the emotions expressed by users when receiving interior design suggestions and can make suggestions to reduce stress. For example, if a user is feeling stressed, it can suggest colors and designs that have a relaxing effect. Also, if a user expresses positive emotions, it can prioritize suggestions for styles that reflect that. Furthermore, it can accumulate user emotional data and make optimal suggestions based on past responses. This makes it possible to make interior design suggestions to reduce stress based on the user's emotions.

[0085] The room coordination system uses its emotion estimation function to analyze the emotions felt when receiving suggestions and make suggestions to improve motivation. For example, if a user is feeling motivated, it can suggest interior design that will help maintain that emotion. Also, if a user expresses positive emotions, it can prioritize suggestions that reflect that style. Furthermore, it can accumulate user emotional data and make optimal suggestions based on past responses. This makes it possible to suggest interior design ideas that will improve motivation based on the user's emotions.

[0086] The room coordination system uses its emotion estimation function to analyze the emotions expressed by the user when receiving suggestions, and can make suggestions that have a relaxing effect. For example, if the user feels like relaxing, it can suggest calming colors and designs. Also, if the user expresses positive emotions, it can prioritize suggestions for styles that reflect that. Furthermore, it can accumulate user emotional data and make optimal suggestions based on past responses. This makes it possible to suggest interior designs that have a relaxing effect based on the user's emotions.

[0087] The room coordination system uses its emotion estimation function to analyze the emotions expressed by the user when receiving suggestions, and can make suggestions to improve concentration. For example, if the user feels like they want to concentrate, it can suggest a simple and functional design. Also, if the user shows positive emotions, it can prioritize suggestions for that style. Furthermore, it can accumulate user emotional data and make optimal suggestions based on past responses. This makes it possible to make interior suggestions to improve concentration based on the user's emotions.

[0088] The room coordination system uses its emotion estimation function to analyze the emotions expressed by users when receiving suggestions and make suggestions that stimulate creativity. For example, if a user feels like engaging in creative activities, it can suggest inspiring colors and designs. Also, if a user expresses positive emotions, it can prioritize suggestions that match those styles. Furthermore, it can accumulate user emotional data and make optimal suggestions based on past responses. This makes it possible to make interior suggestions that stimulate creativity based on the user's emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The generation AI extracts the user's preferences. For example, the generation AI collects information such as "size and layout," "taste," "budget," and "favorite colors" through conversation with the user to extract the user's preferences. Furthermore, when the user expresses a request such as "I want modern furniture that matches my spacious living room," the generation AI understands that request and suggests appropriate furniture and interior design. Step 2: The image generation AI creates an image based on the user's preferences extracted by the generation AI. For example, if the user expresses a desire for a modern living room with a blue theme, the image generation AI will generate an image of a modern living room with a blue theme based on that desire. Step 3: The AR function allows users to view the image created by the image generation AI placed in a real room on their smartphone screen. For example, if a user points their smartphone camera at a living room, they can see the generated modern furniture and interior design placed in the real room on their smartphone screen. Step 4: The online shopping site allows users to purchase furniture and interior items suggested based on the images created by the image generation AI. For example, users can purchase the furniture and interior items they like directly on the online shopping site.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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. Generative AI and Image generation AI and AR function and With an online shopping site, The generated AI is Extracting user preferences, The image generation AI is Creating an image based on the user preferences extracted by the generating AI; The AR function is The image created by the image generation AI can be viewed on a smartphone screen as it is placed in an actual room. The online shopping site Allowing customers to purchase furniture and interior design ideas based on images created by the image generation AI A system characterized by:

2. The generated AI is Analyze the user's past purchase history and browsing history to extract potential preferences 2. The system of claim 1.

3. The generated AI is Analyze the user's social media posts and blog articles to make suggestions that reflect their lifestyle and preferences.

2. The system of claim 1.

4. The generated AI is Analyze the emotions expressed by the user during the conversation and make suggestions that will elicit a positive response 2. The system of claim 1.

5. The generated AI is The system also takes into consideration the preferences of the user's family and housemates, and makes suggestions that will satisfy everyone.

2. The system of claim 1.

Images