system

Abstract

The system according to this embodiment aims to propose an interior design plan tailored to the user's preferences and budget, and to enable the purchase of optimal furniture. [Solution] The system according to the embodiment comprises a shooting unit, a generation unit, a proposal unit, a purchase unit, and an acquisition unit. The shooting unit takes a picture of the room with a camera app. The generation unit analyzes the image taken by the shooting unit and generates a 3D model of the room. The proposal unit proposes an interior plan that suits the user's preferences and budget based on the 3D model generated by the generation unit. The purchase unit purchases furniture based on the interior plan proposed by the proposal unit. The acquisition unit obtains the best prices and sale information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when a user selects an interior of a room, there is a problem that it is difficult to find an optimal plan that suits the user's preferences and budget.

[0005] The system according to the embodiment aims to propose an interior plan that suits the user's preferences and budget and purchase optimal furniture.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a shooting unit, a generation unit, a proposal unit, a purchase unit, and an acquisition unit. The shooting unit allows the user to take a picture of the room using a camera app. The generation unit analyzes the image taken by the shooting unit and generates a 3D model of the room. The proposal unit proposes an interior plan tailored to the user's preferences and budget based on the 3D model generated by the generation unit. The purchase unit purchases furniture based on the interior plan proposed by the proposal unit. The acquisition unit obtains the best prices and sale information. [Effects of the Invention]

[0007] The system according to this embodiment can propose an interior design plan tailored to the user's preferences and budget, and allow them to purchase the most suitable furniture. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The SmartHomeDesignAgent system according to an embodiment of the present invention is a system in which a generating AI generates a 3D model when a user takes a picture of a room with a camera app, and proposes an interior plan on the 3D model that suits the user's preferences and budget. The SmartHomeDesignAgent system works by having the user take a picture of a room using a camera app, and the generating AI analyzes the image to generate a 3D model of the room. Based on this 3D model, the generating AI proposes an interior plan that suits the user's preferences and budget. Furthermore, the SmartHomeDesignAgent system is linked with e-commerce sites, allowing users to seamlessly purchase furniture based on the proposed interior plan. The intelligent furniture purchasing agent automatically obtains the best prices and sale information, providing users with the most economical purchasing experience. For example, if a user wants to purchase a specific piece of furniture, the agent collects price information from multiple e-commerce sites and presents the lowest price. This system targets general consumers, real estate agents, furniture designers, and others. Users can easily create an interior plan by using this system when they are unsure what kind of furniture would suit their room, or when they find it difficult to imagine whether furniture they saw in a store would suit their room. Furthermore, for users who wish to design custom-made furniture, the generating AI can design furniture according to the user's requests, and custom-made furniture can be manufactured at partner factories. The advantages of this system include the ability for users to easily generate 3D models and perform real-time interior placement and simulations. Additionally, furniture placement can be easily changed using voice commands or touch controls, and interactive coaching by the generating AI helps users achieve their optimal interior design. Moreover, the intelligent furniture purchasing agent provides users with the best prices and sales information, ensuring an economical purchasing experience. Thus, the SmartHomeDesignAgent system allows users to photograph their rooms, generate 3D models, receive interior plan suggestions, purchase furniture, and obtain the best prices and sales information.

[0029] The SmartHomeDesignAgent system according to this embodiment comprises a shooting unit, a generation unit, a proposal unit, a purchase unit, and an acquisition unit. The shooting unit takes pictures of the room with a camera app. The shooting unit can acquire images of the room using, for example, a smartphone camera. The shooting unit can also acquire high-resolution images using a digital camera. Furthermore, the shooting unit can take multiple images in succession using a camera app to capture the overall view of the room. The generation unit analyzes the images taken by the shooting unit and generates a 3D model of the room. The generation unit can perform image analysis using, for example, generation AI to reflect the room's structure and furniture arrangement in the 3D model. The generation unit can also combine multiple images to generate a detailed 3D model of the room. Furthermore, the generation unit can add specific furniture and decorative items to the 3D model according to the user's requests. The proposal unit proposes an interior plan tailored to the user's preferences and budget based on the 3D model generated by the generation unit. The proposal unit can analyze the user's preference data and past purchase history using, for example, generation AI to propose a personalized interior plan. The proposal unit can also select the most suitable furniture and decorations based on the user's budget. Furthermore, the proposal unit can provide interactive coaching that allows users to change the furniture layout using voice commands or touch controls. The purchase unit purchases furniture based on the interior plan proposed by the proposal unit. The purchase unit can seamlessly purchase the furniture selected by the user, for example, by linking with e-commerce sites. The purchase unit can also have custom-made furniture manufactured at partner factories. Furthermore, the purchase unit can provide a function to automatically enter the user's payment method and shipping address information. The acquisition unit obtains the best prices and sale information. For example, the acquisition unit can collect price information from multiple e-commerce sites and present the lowest price. The acquisition unit can also analyze price fluctuation trends by referring to past price data and suggest the optimal timing for purchase. Furthermore, the acquisition unit can estimate the user's emotions and adjust the method of acquiring price information based on the estimated user emotions.As a result, the SmartHomeDesignAgent system according to the embodiment allows the user to take photos of a room, generate a 3D model, receive interior design proposals, purchase furniture, and obtain the best prices and sale information.

[0030] The shooting function allows users to photograph a room using a camera app. For example, it can capture images of a room using a smartphone camera. It can also capture high-resolution images using a digital camera. Furthermore, it can capture multiple images in sequence using a camera app to capture the entire room. Specifically, the smartphone camera app displays guidelines as the user photographs different parts of the room, helping them to shoot at the appropriate angle and distance. The shooting function can also utilize panoramic shooting to capture the entire room at once, allowing users to easily capture the overall picture. Additionally, the shooting function automatically adjusts image resolution and brightness to obtain optimal images. For example, it can perform exposure compensation and HDR (High Dynamic Range) shooting to capture bright and clear images even in dark rooms. The shooting function also features image stabilization to prevent blurring, allowing users to obtain high-quality images even when shooting handheld. This enables the shooting function to easily and reliably capture images of a room, providing the data necessary for the next step: generating a 3D model.

[0031] The generation unit analyzes images captured by the image capture unit and generates a 3D model of the room. For example, the generation unit can use generation AI to perform image analysis and reflect the room's structure and furniture arrangement in the 3D model. Specifically, the generation AI uses image recognition technology to identify the positions of the room's walls, floor, and ceiling, and constructs the 3D space based on these. It can also recognize the position and shape of furniture and decorations and accurately place them in the 3D model. Furthermore, the generation unit can combine multiple images to generate a detailed 3D model of the room. For example, it can integrate images taken from different angles to recreate the overall layout of the room. The generation unit can also add specific furniture and decorations to the 3D model according to user requests. For example, if a user wants to add a new sofa or table, the generation unit can import the 3D model of that furniture and place it in the room's 3D model. This allows users to visually confirm the room layout before actually purchasing furniture. Additionally, the generation unit has a real-time 3D model update function, allowing users to instantly reflect changes in furniture placement or the addition of new items in the 3D model. This makes the generation unit a powerful tool for users to freely experiment with room designs and find the optimal layout.

[0032] The proposal unit proposes interior design plans tailored to the user's preferences and budget, based on 3D models generated by the generation unit. For example, the proposal unit can use generation AI to analyze user preference data and past purchase history to propose personalized interior design plans. Specifically, the generation AI learns the user's preferences and style based on data of furniture and decorations the user has purchased in the past. It can also select the most suitable furniture and decorations considering the budget information provided by the user. Furthermore, the proposal unit can provide interactive coaching, allowing users to change furniture placement via voice commands or touch operations. For example, if the user gives a voice command such as "Move the sofa closer to the window," the proposal unit will change the sofa's position in the 3D model and display the result to the user. Users can also change furniture placement using drag-and-drop touch operations. This allows users to intuitively adjust the room layout and realize their ideal interior. Additionally, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, if the user provides evaluations and comments on the proposed interior design plan, the proposal unit can use this information to make future suggestions more personalized. This allows the proposal department to provide optimal interior plans tailored to user needs, thereby increasing user satisfaction.

[0033] The purchasing department buys furniture based on the interior design plan proposed by the design department. The purchasing department can seamlessly purchase furniture selected by users by, for example, integrating with e-commerce sites. Specifically, the purchasing department displays a list of proposed furniture and decorative items and automatically adds the user's selected items to the e-commerce site's shopping cart. The purchasing department can also create custom-made furniture at partner factories. For example, if a user desires furniture of a specific size or design, the purchasing department arranges for the manufacture of customized furniture according to that request. Furthermore, the purchasing department can provide a function to automatically fill in the user's payment method and shipping address information. This allows users to complete the purchase process smoothly and without hassle. The purchasing department also manages purchase history and allows users to refer to information on items they have previously purchased. This makes it easy for users to repurchase the same items or select new items based on their past purchase history. In addition, the purchasing department can provide after-sales service. For example, it can provide support for furniture assembly and installation, ensuring users can smoothly use the furniture they purchased. In this way, the purchasing department can seamlessly support the entire process of realizing the proposed interior design plan, increasing user satisfaction.

[0034] The acquisition unit obtains the best prices and sale information. For example, it can collect price information from multiple e-commerce sites and present the lowest price. Specifically, the acquisition unit uses the APIs of each e-commerce site to obtain and compare price information in real time. The acquisition unit can also analyze price fluctuation trends by referring to past price data and suggest the optimal purchase timing. For example, it can predict when a particular piece of furniture will go on sale or when its price will drop and notify the user. Furthermore, the acquisition unit can estimate the user's sentiment and adjust how price information is acquired based on the estimated sentiment. For example, if the user is in a hurry to buy, it can quickly present the lowest price, while conversely, if the user has time, it can suggest waiting until the price drops. The acquisition unit can also provide personalized sale information by considering the user's purchase history and preferences. For example, if a product related to an item the user has previously purchased goes on sale, it will prioritize notifying the user of that information. In this way, the acquisition unit can help users purchase furniture at the best price and maximize cost performance. Furthermore, by adjusting the frequency and accuracy of price information acquisition, the acquisition unit can respond flexibly to specific situations and conditions. This allows the acquisition unit to efficiently and effectively collect price information and provide users with the best possible purchasing opportunities.

[0035] The proposal department can manage and analyze user preference data and past purchase history to propose personalized interior plans. For example, the proposal department can propose interior plans based on the user's preferences and interests. For example, the proposal department can analyze data on furniture and decorations that the user has purchased in the past to propose an interior plan that suits the user's preferences. Furthermore, the proposal department can suggest interior styles that the user is interested in based on the user's browsing history. In addition, the proposal department can propose interior plans that match the user's preferences and budget based on the results of user surveys. In this way, it is possible to propose personalized interior plans based on user preference data and past purchase history.

[0036] The acquisition unit can collect price information from multiple e-commerce sites and present the lowest price. For example, the acquisition unit can collect price information from multiple e-commerce sites in real time and present the lowest price. For example, the acquisition unit can compare price information from multiple e-commerce sites and present the most economical option. In addition, the acquisition unit can periodically update price information to provide users with the latest price information. Furthermore, the acquisition unit can estimate the user's sentiment and adjust the method of acquiring price information based on the estimated user sentiment. This allows the system to provide users with an economical purchasing experience by collecting price information from multiple e-commerce sites and presenting the lowest price.

[0037] The design unit can design custom-made furniture according to user requests. For example, if a user desires a specific design or function, the design unit will design custom-made furniture based on those requests. For example, the design unit can design custom-made furniture based on the user's desired materials, colors, and sizes. The design unit can also design custom-made furniture that reflects the user's desired functions and style. Furthermore, the design unit can adjust the design of the custom-made furniture in real time according to user requests. This allows the design unit to create custom-made furniture that meets the user's needs and provides furniture that is tailored to their requirements.

[0038] The purchasing department can have custom-made furniture manufactured at partner factories. For example, the purchasing department can send the design of the custom-made furniture desired by the user to a partner factory and request its manufacture. The purchasing department can, for example, coordinate with the partner factory to manage the manufacturing process of the custom-made furniture. Furthermore, the purchasing department can check the manufacturing status of the custom-made furniture in real time and notify the user. In addition, the purchasing department can handle the procedures for delivery to the user after the manufacturing of the custom-made furniture is completed. In this way, by having custom-made furniture manufactured at partner factories, furniture that meets the user's needs can be provided.

[0039] The suggestion function can provide interactive coaching that allows users to change furniture arrangements using voice commands or touch controls. For example, the suggestion function allows users to change furniture arrangements by giving voice commands. For instance, if a user says, "Move the sofa to the left," the generated AI will adjust the sofa's position on the 3D model accordingly. The suggestion function also allows users to change furniture arrangements using touch controls. For example, a user can change the furniture arrangement on the 3D model by dragging and dropping furniture using the touchscreen. Furthermore, the suggestion function can provide interactive coaching, offering advice to help users achieve the optimal interior arrangement. This makes it easy for users to change their interiors by providing interactive coaching that allows them to change furniture arrangements using voice commands or touch controls.

[0040] The camera unit can automatically adjust the room lighting conditions during shooting to obtain the optimal image. For example, if the room lighting is dim, the AI ​​will automatically adjust the lighting and optimize the brightness. The camera unit can adjust the color temperature of the lighting to capture images with natural colors. The camera unit can also adjust the position of the lighting to minimize shadows. In this way, the optimal image can be obtained by automatically adjusting the room lighting conditions. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI.

[0041] The imaging unit can automatically measure the dimensions of a room during imaging, improving the accuracy of 3D model generation. For example, during imaging, the imaging unit uses AI to recognize the positions of walls and furniture in the room and automatically measure their dimensions. The imaging unit can combine multiple images to measure the precise dimensions of a room. The imaging unit can also display the room dimension data in real time for user verification. This improves the accuracy of 3D model generation by automatically measuring the room dimensions. Some or all of the above processing in the imaging unit may be performed using AI, for example, or without AI.

[0042] The camera unit can suggest the optimal shooting angle by referring to the user's past shooting history during shooting. For example, the camera unit can suggest the optimal shooting angle based on images the user has taken in the past. For example, the camera unit can select the best angle from the user's past shooting history. The camera unit can also suggest the optimal angle by comparing it with images the user has taken in the past. In this way, the camera unit can suggest the optimal shooting angle by referring to the user's past shooting history. Some or all of the above processing in the camera unit may be performed using AI, for example, or without using AI.

[0043] The camera unit can simultaneously capture images from multiple cameras in conjunction with the user's smart device during shooting. For example, the camera unit can connect with the user's smartphone to capture images from multiple cameras simultaneously. For example, the camera unit can connect with the user's tablet to capture images from different angles. The camera unit can also connect with the user's smartwatch to add a viewpoint from the user's hand. This allows for the acquisition of images from different viewpoints by simultaneously capturing images from multiple cameras in conjunction with the user's smart device. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI.

[0044] The generation unit can automatically recognize the furniture arrangement in a room during generation and reflect it in the 3D model. For example, the generation unit can use AI to recognize the position of furniture in a room and accurately place it in the 3D model. For example, the generation unit can analyze multiple images and automatically determine the furniture arrangement. The generation unit can also update the furniture arrangement data in real time and reflect it in the 3D model. As a result, by automatically recognizing the furniture arrangement in a room and reflecting it in the 3D model, an accurate 3D model can be generated. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0045] The generation unit can apply algorithms to realistically reproduce the texture of the room's wallpaper and flooring during the generation process. For example, the generation unit can use AI to recognize the wallpaper pattern and realistically reproduce it in the 3D model. The generation unit can also analyze the texture of the flooring and reflect it in the 3D model. Furthermore, the generation unit can adjust the color tones of the wallpaper and flooring to reproduce a realistic texture. By realistically reproducing the texture of the room's wallpaper and flooring, a more realistic 3D model can be generated. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0046] The generation unit can incorporate the external environment of the room and reflect it in the 3D model during generation. For example, the generation unit can photograph the view from the window and reflect it in the 3D model. For example, the generation unit can acquire data on the external environment and incorporate it into the 3D model. Furthermore, the generation unit can reflect the external environment in the 3D model, taking into account the position and size of the window. In this way, by incorporating the external environment of the room and reflecting it in the 3D model, a more realistic 3D model can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0047] The generation unit can select the optimal generation method by referring to the user's past 3D model generation history during generation. For example, the generation unit selects the optimal generation method based on the user's past 3D model generation history. For example, the generation unit can analyze the user's preferences and tendencies and propose the optimal generation method. The generation unit can also select the most efficient generation method from past generation history. In this way, the optimal generation method can be selected by referring to the user's past 3D model generation history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0048] The proposal unit can propose an optimal interior plan when making a proposal, taking into account the user's lifestyle and family structure. For example, the proposal unit can propose an interior plan that suits the user's lifestyle. For example, the proposal unit can propose an interior plan that takes into account the family structure and allows everyone to live comfortably. Furthermore, the proposal unit can also propose an interior plan that reflects the user's hobbies and preferences. In this way, by proposing an interior plan that takes into account the user's lifestyle and family structure, a more comfortable living space can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI.

[0049] The proposal unit can propose interior plans tailored to the season and events during the proposal process. For example, the proposal unit can propose an interior plan that matches the season. For example, the proposal unit can propose an interior plan that matches a specific event. Furthermore, the proposal unit can adjust the colors and design of the interior according to the season and events. By proposing interior plans that match the season and events, the user's living space can be made more attractive. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI.

[0050] The proposal unit can propose the optimal plan by referring to the user's past interior plan history when making a proposal. For example, the proposal unit can propose the optimal plan based on the user's past interior plan history. For example, the proposal unit can propose the optimal plan by analyzing the user's preferences and tendencies. The proposal unit can also propose the most efficient plan from the past plan history. In this way, the optimal plan can be proposed by referring to the user's past interior plan history. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI.

[0051] The proposal department can analyze the user's social media activity and propose interior plans based on trends. For example, the proposal department can analyze the user's social media activity and propose plans based on the latest trends. For example, the proposal department can refer to the interior styles of influencers that the user follows. The proposal department can also propose plans that incorporate popular designs on social media. In this way, by analyzing the user's social media activity and proposing interior plans based on trends, it is possible to provide interiors that incorporate the latest trends. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or without generative AI.

[0052] The purchasing unit can suggest the optimal purchasing method by referring to the user's past purchase history at the time of purchase. For example, the purchasing unit can prioritize suggesting purchasing methods that the user has used in the past (credit card, electronic money, etc.). For example, the purchasing unit can suggest the most frequently used purchasing method based on the user's past purchase history. The purchasing unit can also analyze the user's past purchase history and suggest the most efficient purchasing method. In this way, the optimal purchasing method can be suggested by referring to the user's past purchase history. Some or all of the above processing in the purchasing unit may be performed using, for example, generative AI, or without using generative AI.

[0053] The purchasing section can provide a function to automatically enter the user's payment method and shipping address information at the time of purchase. For example, the purchasing section can automatically enter the payment method the user has entered in the past. For example, the purchasing section can automatically enter the optimal shipping address information from the user's past purchase history. Furthermore, the purchasing section can also automatically enter the payment method and shipping address information based on the user's account information. This simplifies the purchase process by automatically entering the user's payment method and shipping address information. Some or all of the above processes in the purchasing section may be performed using, for example, a generative AI, or without using a generative AI.

[0054] The purchasing department can suggest the optimal shipping method at the time of purchase, taking into account the user's geographical location. For example, the purchasing department can suggest the optimal shipping method based on the user's current location. For example, the purchasing department can suggest the optimal shipping method by referring to the user's past shipping history. Furthermore, the purchasing department can also suggest the most efficient shipping method by considering the user's geographical location. In this way, the optimal shipping method can be suggested by considering the user's geographical location. Some or all of the above processing in the purchasing department may be performed using AI, for example, or without using AI.

[0055] The purchasing unit can analyze the user's social media activity at the time of purchase and suggest the purchase of relevant products. For example, the purchasing unit can analyze the user's social media activity and suggest the purchase of relevant products. For example, the purchasing unit can suggest products introduced by influencers that the user follows. The purchasing unit can also suggest products that are popular on social media. In this way, by analyzing the user's social media activity, it is possible to suggest the purchase of relevant products. Some or all of the above processing in the purchasing unit may be performed using, for example, generative AI, or without generative AI.

[0056] The acquisition unit can collect price information in real time from multiple e-commerce sites at the time of acquisition and present the optimal price. For example, the acquisition unit can collect price information in real time from multiple e-commerce sites and present the lowest price. For example, the acquisition unit can update price information in real time and provide the user with the best price. The acquisition unit can also compare price information from multiple e-commerce sites and present the most economical option. In this way, by collecting price information in real time from multiple e-commerce sites, the optimal price can be presented. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or without using a generative AI.

[0057] The acquisition unit can analyze price fluctuation trends by referring to past price data during acquisition and propose the optimal purchase timing. For example, the acquisition unit analyzes past price data to understand price fluctuation trends. For example, the acquisition unit can propose the optimal purchase timing based on price fluctuation trends. The acquisition unit can also update past price data in real time to provide the optimal purchase timing. This allows the acquisition unit to propose the optimal purchase timing by analyzing price fluctuation trends by referring to past price data. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or without using a generative AI.

[0058] The acquisition unit can present optimal price information by considering the user's geographical location information at the time of acquisition. For example, the acquisition unit can present optimal price information based on the user's current location. For example, the acquisition unit can present optimal price information by referring to the user's past purchase history. Furthermore, the acquisition unit can also present the most economical price information by considering the user's geographical location information. In this way, optimal price information can be presented by considering the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0059] The acquisition unit can analyze the user's social media activity and obtain price information for related products at the time of acquisition. For example, the acquisition unit can analyze the user's social media activity and obtain price information for related products. For example, the acquisition unit can obtain price information for products introduced by influencers that the user follows. The acquisition unit can also obtain price information for products that are popular on social media. In this way, price information for related products can be obtained by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, generative AI, or without using generative AI.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The camera unit can automatically measure the temperature and humidity of a room when the user photographs it, and reflect this data in a 3D model. For example, the camera unit can use room temperature and humidity sensors to acquire environmental data during shooting. By reflecting this environmental data in the 3D model, the camera unit can propose more realistic interior plans. Furthermore, if the user requests it, the camera unit can provide advice on optimal air conditioning settings and humidity control based on this environmental data. This allows the user to obtain an interior plan that takes into account the environmental conditions of the room.

[0062] The generation unit can automatically measure the acoustic characteristics of a room when analyzing images of the room taken by the user, and reflect this in a 3D model. For example, the generation unit can analyze the reverberation and sound absorption characteristics of the room and reflect them in the 3D model. Furthermore, based on these acoustic characteristics, the generation unit can also propose an interior plan to achieve an optimal acoustic environment. In addition, if the user wishes, the generation unit can suggest the placement of furniture and decorative items to improve the acoustic characteristics. As a result, the user can obtain an interior plan that takes the acoustic environment of the room into consideration.

[0063] The purchasing department can suggest related accessories and decorations to users based on their purchase history and preferences when they buy furniture. For example, if a user buys a sofa, the purchasing department can suggest cushions and rugs that match the sofa. If a user buys a table, it can suggest lamps and vases that match the table. Furthermore, if a user buys a bed, it can suggest bedspreads and pillows that match the bed. This allows users to purchase related accessories and decorations along with their furniture, enabling them to create a cohesive interior.

[0064] The camera unit can automatically measure the scent of a room when the user photographs it and reflect this in a 3D model. For example, the camera unit can use a room scent sensor to acquire scent data during photography. By reflecting this scent data in the 3D model, the camera unit can propose more realistic interior plans. Furthermore, if the user requests it, the camera unit can provide advice on the optimal scent environment based on this scent data. This allows the user to obtain an interior plan that takes the scent of the room into consideration.

[0065] The generation unit can automatically measure the room's lighting conditions when analyzing images of the room taken by the user and reflect them in the 3D model. For example, the generation unit can analyze the brightness and color temperature of the room's lighting and reflect them in the 3D model. Furthermore, based on these lighting conditions, the generation unit can propose an interior plan to achieve the optimal lighting environment. In addition, if the user requests it, the generation unit can suggest the placement of lighting fixtures to improve the lighting conditions. As a result, the user can obtain an interior plan that takes the room's lighting conditions into consideration.

[0066] The purchasing department can suggest relevant services to users based on their purchase history and preferences when they buy furniture. For example, if a user buys a sofa, the purchasing department can suggest a sofa cleaning service. If a user buys a table, the purchasing department can suggest a table assembly service. Furthermore, if a user buys a bed, the purchasing department can suggest a bed mattress replacement service. This allows users to utilize related services along with their furniture purchases, resulting in a more convenient shopping experience.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The shooting unit takes pictures of the room using a camera app. The shooting unit can, for example, use a smartphone camera to capture images of the room. Alternatively, it can use a digital camera to capture high-resolution images. Furthermore, it can use a camera app to take multiple images in sequence to capture the overall view of the room. Step 2: The generation unit analyzes the images captured by the shooting unit and generates a 3D model of the room. The generation unit can, for example, use generation AI to perform image analysis and reflect the room's structure and furniture arrangement in the 3D model. It can also combine multiple images to generate a detailed 3D model of the room. Furthermore, specific furniture and decorations can be added to the 3D model according to the user's requests. Step 3: The proposal unit proposes an interior plan tailored to the user's preferences and budget, based on the 3D model generated by the generation unit. For example, the proposal unit can use generation AI to analyze the user's preference data and past purchase history to propose a personalized interior plan. It can also select the most suitable furniture and decorations based on the user's budget. Furthermore, it can provide interactive coaching that allows the user to change the furniture arrangement using voice commands or touch controls. Step 4: The purchasing department purchases furniture based on the interior plan proposed by the proposal department. The purchasing department can seamlessly purchase the furniture selected by the user by, for example, integrating with an e-commerce site. It can also manufacture custom-made furniture at partner factories. Furthermore, it can provide a function to automatically enter the user's payment method and shipping address information. Step 5: The acquisition unit obtains the best prices and sale information. For example, the acquisition unit can collect price information from multiple e-commerce sites and present the lowest price. It can also analyze price fluctuation trends by referring to past price data and suggest the optimal timing for purchase. Furthermore, it can estimate the user's sentiment and adjust the method of acquiring price information based on the estimated user sentiment.

[0069] (Example of form 2) The SmartHomeDesignAgent system according to an embodiment of the present invention is a system in which a generating AI generates a 3D model when a user takes a picture of a room with a camera app, and proposes an interior plan on the 3D model that suits the user's preferences and budget. The SmartHomeDesignAgent system works by having the user take a picture of a room using a camera app, and the generating AI analyzes the image to generate a 3D model of the room. Based on this 3D model, the generating AI proposes an interior plan that suits the user's preferences and budget. Furthermore, the SmartHomeDesignAgent system is linked with e-commerce sites, allowing users to seamlessly purchase furniture based on the proposed interior plan. The intelligent furniture purchasing agent automatically obtains the best prices and sale information, providing users with the most economical purchasing experience. For example, if a user wants to purchase a specific piece of furniture, the agent collects price information from multiple e-commerce sites and presents the lowest price. This system targets general consumers, real estate agents, furniture designers, and others. Users can easily create an interior plan by using this system when they are unsure what kind of furniture would suit their room, or when they find it difficult to imagine whether furniture they saw in a store would suit their room. Furthermore, for users who wish to design custom-made furniture, the generating AI can design furniture according to the user's requests, and custom-made furniture can be manufactured at partner factories. The advantages of this system include the ability for users to easily generate 3D models and perform real-time interior placement and simulations. Additionally, furniture placement can be easily changed using voice commands or touch controls, and interactive coaching by the generating AI helps users achieve their optimal interior design. Moreover, the intelligent furniture purchasing agent provides users with the best prices and sales information, ensuring an economical purchasing experience. Thus, the SmartHomeDesignAgent system allows users to photograph their rooms, generate 3D models, receive interior plan suggestions, purchase furniture, and obtain the best prices and sales information.

[0070] The SmartHomeDesignAgent system according to this embodiment comprises a shooting unit, a generation unit, a proposal unit, a purchase unit, and an acquisition unit. The shooting unit takes pictures of the room with a camera app. The shooting unit can acquire images of the room using, for example, a smartphone camera. The shooting unit can also acquire high-resolution images using a digital camera. Furthermore, the shooting unit can take multiple images in succession using a camera app to capture the overall view of the room. The generation unit analyzes the images taken by the shooting unit and generates a 3D model of the room. The generation unit can perform image analysis using, for example, generation AI to reflect the room's structure and furniture arrangement in the 3D model. The generation unit can also combine multiple images to generate a detailed 3D model of the room. Furthermore, the generation unit can add specific furniture and decorative items to the 3D model according to the user's requests. The proposal unit proposes an interior plan tailored to the user's preferences and budget based on the 3D model generated by the generation unit. The proposal unit can analyze the user's preference data and past purchase history using, for example, generation AI to propose a personalized interior plan. The proposal unit can also select the most suitable furniture and decorations based on the user's budget. Furthermore, the proposal unit can provide interactive coaching that allows users to change the furniture layout using voice commands or touch controls. The purchase unit purchases furniture based on the interior plan proposed by the proposal unit. The purchase unit can seamlessly purchase the furniture selected by the user, for example, by linking with e-commerce sites. The purchase unit can also have custom-made furniture manufactured at partner factories. Furthermore, the purchase unit can provide a function to automatically enter the user's payment method and shipping address information. The acquisition unit obtains the best prices and sale information. For example, the acquisition unit can collect price information from multiple e-commerce sites and present the lowest price. The acquisition unit can also analyze price fluctuation trends by referring to past price data and suggest the optimal timing for purchase. Furthermore, the acquisition unit can estimate the user's emotions and adjust the method of acquiring price information based on the estimated user emotions.As a result, the SmartHomeDesignAgent system according to the embodiment allows the user to take photos of a room, generate a 3D model, receive interior design proposals, purchase furniture, and obtain the best prices and sale information.

[0071] The shooting function allows users to photograph a room using a camera app. For example, it can capture images of a room using a smartphone camera. It can also capture high-resolution images using a digital camera. Furthermore, it can capture multiple images in sequence using a camera app to capture the entire room. Specifically, the smartphone camera app displays guidelines as the user photographs different parts of the room, helping them to shoot at the appropriate angle and distance. The shooting function can also utilize panoramic shooting to capture the entire room at once, allowing users to easily capture the overall picture. Additionally, the shooting function automatically adjusts image resolution and brightness to obtain optimal images. For example, it can perform exposure compensation and HDR (High Dynamic Range) shooting to capture bright and clear images even in dark rooms. The shooting function also features image stabilization to prevent blurring, allowing users to obtain high-quality images even when shooting handheld. This enables the shooting function to easily and reliably capture images of a room, providing the data necessary for the next step: generating a 3D model.

[0072] The generation unit analyzes images captured by the image capture unit and generates a 3D model of the room. For example, the generation unit can use generation AI to perform image analysis and reflect the room's structure and furniture arrangement in the 3D model. Specifically, the generation AI uses image recognition technology to identify the positions of the room's walls, floor, and ceiling, and constructs the 3D space based on these. It can also recognize the position and shape of furniture and decorations and accurately place them in the 3D model. Furthermore, the generation unit can combine multiple images to generate a detailed 3D model of the room. For example, it can integrate images taken from different angles to recreate the overall layout of the room. The generation unit can also add specific furniture and decorations to the 3D model according to user requests. For example, if a user wants to add a new sofa or table, the generation unit can import the 3D model of that furniture and place it in the room's 3D model. This allows users to visually confirm the room layout before actually purchasing furniture. Additionally, the generation unit has a real-time 3D model update function, allowing users to instantly reflect changes in furniture placement or the addition of new items in the 3D model. This makes the generation unit a powerful tool for users to freely experiment with room designs and find the optimal layout.

[0073] The proposal unit proposes interior design plans tailored to the user's preferences and budget, based on 3D models generated by the generation unit. For example, the proposal unit can use generation AI to analyze user preference data and past purchase history to propose personalized interior design plans. Specifically, the generation AI learns the user's preferences and style based on data of furniture and decorations the user has purchased in the past. It can also select the most suitable furniture and decorations considering the budget information provided by the user. Furthermore, the proposal unit can provide interactive coaching, allowing users to change furniture placement via voice commands or touch operations. For example, if the user gives a voice command such as "Move the sofa closer to the window," the proposal unit will change the sofa's position in the 3D model and display the result to the user. Users can also change furniture placement using drag-and-drop touch operations. This allows users to intuitively adjust the room layout and realize their ideal interior. Additionally, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, if the user provides evaluations and comments on the proposed interior design plan, the proposal unit can use this information to make future suggestions more personalized. This allows the proposal department to provide optimal interior plans tailored to user needs, thereby increasing user satisfaction.

[0074] The purchasing department buys furniture based on the interior design plan proposed by the design department. The purchasing department can seamlessly purchase furniture selected by users by, for example, integrating with e-commerce sites. Specifically, the purchasing department displays a list of proposed furniture and decorative items and automatically adds the user's selected items to the e-commerce site's shopping cart. The purchasing department can also create custom-made furniture at partner factories. For example, if a user desires furniture of a specific size or design, the purchasing department arranges for the manufacture of customized furniture according to that request. Furthermore, the purchasing department can provide a function to automatically fill in the user's payment method and shipping address information. This allows users to complete the purchase process smoothly and without hassle. The purchasing department also manages purchase history and allows users to refer to information on items they have previously purchased. This makes it easy for users to repurchase the same items or select new items based on their past purchase history. In addition, the purchasing department can provide after-sales service. For example, it can provide support for furniture assembly and installation, ensuring users can smoothly use the furniture they purchased. In this way, the purchasing department can seamlessly support the entire process of realizing the proposed interior design plan, increasing user satisfaction.

[0075] The acquisition unit obtains the best prices and sale information. For example, it can collect price information from multiple e-commerce sites and present the lowest price. Specifically, the acquisition unit uses the APIs of each e-commerce site to obtain and compare price information in real time. The acquisition unit can also analyze price fluctuation trends by referring to past price data and suggest the optimal purchase timing. For example, it can predict when a particular piece of furniture will go on sale or when its price will drop and notify the user. Furthermore, the acquisition unit can estimate the user's sentiment and adjust how price information is acquired based on the estimated sentiment. For example, if the user is in a hurry to buy, it can quickly present the lowest price, while conversely, if the user has time, it can suggest waiting until the price drops. The acquisition unit can also provide personalized sale information by considering the user's purchase history and preferences. For example, if a product related to an item the user has previously purchased goes on sale, it will prioritize notifying the user of that information. In this way, the acquisition unit can help users purchase furniture at the best price and maximize cost performance. Furthermore, by adjusting the frequency and accuracy of price information acquisition, the acquisition unit can respond flexibly to specific situations and conditions. This allows the acquisition unit to efficiently and effectively collect price information and provide users with the best possible purchasing opportunities.

[0076] The proposal department can manage and analyze user preference data and past purchase history to propose personalized interior plans. For example, the proposal department can propose interior plans based on the user's preferences and interests. For example, the proposal department can analyze data on furniture and decorations that the user has purchased in the past to propose an interior plan that suits the user's preferences. Furthermore, the proposal department can suggest interior styles that the user is interested in based on the user's browsing history. In addition, the proposal department can propose interior plans that match the user's preferences and budget based on the results of user surveys. In this way, it is possible to propose personalized interior plans based on user preference data and past purchase history.

[0077] The acquisition unit can collect price information from multiple e-commerce sites and present the lowest price. For example, the acquisition unit can collect price information from multiple e-commerce sites in real time and present the lowest price. For example, the acquisition unit can compare price information from multiple e-commerce sites and present the most economical option. In addition, the acquisition unit can periodically update price information to provide users with the latest price information. Furthermore, the acquisition unit can estimate the user's sentiment and adjust the method of acquiring price information based on the estimated user sentiment. This allows the system to provide users with an economical purchasing experience by collecting price information from multiple e-commerce sites and presenting the lowest price.

[0078] The design unit can design custom-made furniture according to user requests. For example, if a user desires a specific design or function, the design unit will design custom-made furniture based on those requests. For example, the design unit can design custom-made furniture based on the user's desired materials, colors, and sizes. The design unit can also design custom-made furniture that reflects the user's desired functions and style. Furthermore, the design unit can adjust the design of the custom-made furniture in real time according to user requests. This allows the design unit to create custom-made furniture that meets the user's needs and provides furniture that is tailored to their requirements.

[0079] The purchasing department can have custom-made furniture manufactured at partner factories. For example, the purchasing department can send the design of the custom-made furniture desired by the user to a partner factory and request its manufacture. The purchasing department can, for example, coordinate with the partner factory to manage the manufacturing process of the custom-made furniture. Furthermore, the purchasing department can check the manufacturing status of the custom-made furniture in real time and notify the user. In addition, the purchasing department can handle the procedures for delivery to the user after the manufacturing of the custom-made furniture is completed. In this way, by having custom-made furniture manufactured at partner factories, furniture that meets the user's needs can be provided.

[0080] The suggestion function can provide interactive coaching that allows users to change furniture arrangements using voice commands or touch controls. For example, the suggestion function allows users to change furniture arrangements by giving voice commands. For instance, if a user says, "Move the sofa to the left," the generated AI will adjust the sofa's position on the 3D model accordingly. The suggestion function also allows users to change furniture arrangements using touch controls. For example, a user can change the furniture arrangement on the 3D model by dragging and dropping furniture using the touchscreen. Furthermore, the suggestion function can provide interactive coaching, offering advice to help users achieve the optimal interior arrangement. This makes it easy for users to change their interiors by providing interactive coaching that allows them to change furniture arrangements using voice commands or touch controls.

[0081] The camera unit can estimate the user's emotions and adjust the shooting timing based on the estimated emotions. For example, if the user is relaxed, the camera unit will adjust the shooting time to take pictures during the time when natural light is best. If the user is in a hurry, the camera unit can start shooting immediately and correct the image later. If the user is excited, the camera unit can also take multiple shots in succession and select the best image. This allows for optimal timing of shooting by adjusting the shooting timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The camera unit can automatically adjust the room lighting conditions during shooting to obtain the optimal image. For example, if the room lighting is dim, the AI ​​will automatically adjust the lighting and optimize the brightness. The camera unit can adjust the color temperature of the lighting to capture images with natural colors. The camera unit can also adjust the position of the lighting to minimize shadows. In this way, the optimal image can be obtained by automatically adjusting the room lighting conditions. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI.

[0083] The imaging unit can automatically measure the dimensions of a room during imaging, improving the accuracy of 3D model generation. For example, during imaging, the imaging unit uses AI to recognize the positions of walls and furniture in the room and automatically measure their dimensions. The imaging unit can combine multiple images to measure the precise dimensions of a room. The imaging unit can also display the room dimension data in real time for user verification. This improves the accuracy of 3D model generation by automatically measuring the room dimensions. Some or all of the above processing in the imaging unit may be performed using AI, for example, or without AI.

[0084] The camera unit can estimate the user's emotions and determine the priority of rooms to film based on those emotions. For example, if the user is relaxed, the camera unit can start filming from the living room. If the user is in a hurry, the camera unit can start filming from the most important room. Alternatively, if the user is excited, the camera unit can film all the rooms in order. This allows for optimal filming by prioritizing rooms based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The camera unit can suggest the optimal shooting angle by referring to the user's past shooting history during shooting. For example, the camera unit can suggest the optimal shooting angle based on images the user has taken in the past. For example, the camera unit can select the best angle from the user's past shooting history. The camera unit can also suggest the optimal angle by comparing it with images the user has taken in the past. In this way, the camera unit can suggest the optimal shooting angle by referring to the user's past shooting history. Some or all of the above processing in the camera unit may be performed using AI, for example, or without using AI.

[0086] The camera unit can simultaneously capture images from multiple cameras in conjunction with the user's smart device during shooting. For example, the camera unit can connect with the user's smartphone to capture images from multiple cameras simultaneously. For example, the camera unit can connect with the user's tablet to capture images from different angles. The camera unit can also connect with the user's smartwatch to add a viewpoint from the user's hand. This allows for the acquisition of images from different viewpoints by simultaneously capturing images from multiple cameras in conjunction with the user's smart device. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI.

[0087] The generation unit can estimate the user's emotions and adjust the 3D model generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed 3D model. If the user is in a hurry, for example, the generation unit can generate a simplified 3D model. Furthermore, if the user is excited, the generation unit can generate a visually appealing 3D model. This allows for the generation of an optimal 3D model by adjusting the 3D model generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The generation unit can automatically recognize the furniture arrangement in a room during generation and reflect it in the 3D model. For example, the generation unit can use AI to recognize the position of furniture in a room and accurately place it in the 3D model. For example, the generation unit can analyze multiple images and automatically determine the furniture arrangement. The generation unit can also update the furniture arrangement data in real time and reflect it in the 3D model. As a result, by automatically recognizing the furniture arrangement in a room and reflecting it in the 3D model, an accurate 3D model can be generated. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0089] The generation unit can apply algorithms to realistically reproduce the texture of the room's wallpaper and flooring during the generation process. For example, the generation unit can use AI to recognize the wallpaper pattern and realistically reproduce it in the 3D model. The generation unit can also analyze the texture of the flooring and reflect it in the 3D model. Furthermore, the generation unit can adjust the color tones of the wallpaper and flooring to reproduce a realistic texture. By realistically reproducing the texture of the room's wallpaper and flooring, a more realistic 3D model can be generated. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0090] The generation unit can incorporate the external environment of the room and reflect it in the 3D model during generation. For example, the generation unit can photograph the view from the window and reflect it in the 3D model. For example, the generation unit can acquire data on the external environment and incorporate it into the 3D model. Furthermore, the generation unit can reflect the external environment in the 3D model, taking into account the position and size of the window. In this way, by incorporating the external environment of the room and reflecting it in the 3D model, a more realistic 3D model can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0091] The generation unit can select the optimal generation method by referring to the user's past 3D model generation history during generation. For example, the generation unit selects the optimal generation method based on the user's past 3D model generation history. For example, the generation unit can analyze the user's preferences and tendencies and propose the optimal generation method. The generation unit can also select the most efficient generation method from past generation history. In this way, the optimal generation method can be selected by referring to the user's past 3D model generation history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it suggests interior plans based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest a detailed interior plan. If the user is in a hurry, for example, the suggestion unit can suggest a simplified interior plan. If the user is excited, the suggestion unit can also suggest a visually appealing interior plan. In this way, by adjusting the way it suggests interior plans based on the user's emotions, it can suggest the optimal interior plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The proposal unit can propose an optimal interior plan when making a proposal, taking into account the user's lifestyle and family structure. For example, the proposal unit can propose an interior plan that suits the user's lifestyle. For example, the proposal unit can propose an interior plan that takes into account the family structure and allows everyone to live comfortably. Furthermore, the proposal unit can also propose an interior plan that reflects the user's hobbies and preferences. In this way, by proposing an interior plan that takes into account the user's lifestyle and family structure, a more comfortable living space can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI.

[0094] The proposal unit can propose interior plans tailored to the season and events during the proposal process. For example, the proposal unit can propose an interior plan that matches the season. For example, the proposal unit can propose an interior plan that matches a specific event. Furthermore, the proposal unit can adjust the colors and design of the interior according to the season and events. By proposing interior plans that match the season and events, the user's living space can be made more attractive. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI.

[0095] The suggestion unit can estimate the user's emotions and prioritize interior design plans based on those emotions. For example, if the user is relaxed, the suggestion unit might prioritize the living room interior design. If the user is in a hurry, the suggestion unit might prioritize the interior design of the most important room. If the user is excited, the suggestion unit might even suggest interior design plans for all rooms in order. This allows the system to provide an interior design plan that meets the user's needs by prioritizing the plans based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The proposal unit can propose the optimal plan by referring to the user's past interior plan history when making a proposal. For example, the proposal unit can propose the optimal plan based on the user's past interior plan history. For example, the proposal unit can propose the optimal plan by analyzing the user's preferences and tendencies. The proposal unit can also propose the most efficient plan from the past plan history. In this way, the optimal plan can be proposed by referring to the user's past interior plan history. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI.

[0097] The proposal department can analyze the user's social media activity and propose interior plans based on trends. For example, the proposal department can analyze the user's social media activity and propose plans based on the latest trends. For example, the proposal department can refer to the interior styles of influencers that the user follows. The proposal department can also propose plans that incorporate popular designs on social media. In this way, by analyzing the user's social media activity and proposing interior plans based on trends, it is possible to provide interiors that incorporate the latest trends. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or without generative AI.

[0098] The purchasing function can estimate the user's emotions and adjust the purchasing process based on those emotions. For example, if the user is relaxed, the purchasing function can provide a detailed purchasing process. If the user is in a hurry, the purchasing function can provide a simplified purchasing process. Furthermore, if the user is excited, the purchasing function can provide a visually appealing purchasing process. This allows for the provision of an optimal purchasing experience by adjusting the purchasing process based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The purchasing unit can suggest the optimal purchasing method by referring to the user's past purchase history at the time of purchase. For example, the purchasing unit can prioritize suggesting purchasing methods that the user has used in the past (credit card, electronic money, etc.). For example, the purchasing unit can suggest the most frequently used purchasing method based on the user's past purchase history. The purchasing unit can also analyze the user's past purchase history and suggest the most efficient purchasing method. In this way, the optimal purchasing method can be suggested by referring to the user's past purchase history. Some or all of the above processing in the purchasing unit may be performed using, for example, generative AI, or without using generative AI.

[0100] The purchasing section can provide a function to automatically enter the user's payment method and shipping address information at the time of purchase. For example, the purchasing section can automatically enter the payment method the user has entered in the past. For example, the purchasing section can automatically enter the optimal shipping address information from the user's past purchase history. Furthermore, the purchasing section can also automatically enter the payment method and shipping address information based on the user's account information. This simplifies the purchase process by automatically entering the user's payment method and shipping address information. Some or all of the above processes in the purchasing section may be performed using, for example, a generative AI, or without using a generative AI.

[0101] The purchasing function can estimate the user's emotions and prioritize products to purchase based on those emotions. For example, if the user is relaxed, the purchasing function might prioritize suggesting living room furniture. If the user is in a hurry, the purchasing function might prioritize suggesting the most important furniture. If the user is excited, the purchasing function might suggest all furniture in order. This allows the system to provide products that meet the user's needs by prioritizing products based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The purchasing department can suggest the optimal shipping method at the time of purchase, taking into account the user's geographical location. For example, the purchasing department can suggest the optimal shipping method based on the user's current location. For example, the purchasing department can suggest the optimal shipping method by referring to the user's past shipping history. Furthermore, the purchasing department can also suggest the most efficient shipping method by considering the user's geographical location. In this way, the optimal shipping method can be suggested by considering the user's geographical location. Some or all of the above processing in the purchasing department may be performed using AI, for example, or without using AI.

[0103] The purchasing unit can analyze the user's social media activity at the time of purchase and suggest the purchase of relevant products. For example, the purchasing unit can analyze the user's social media activity and suggest the purchase of relevant products. For example, the purchasing unit can suggest products introduced by influencers that the user follows. The purchasing unit can also suggest products that are popular on social media. In this way, by analyzing the user's social media activity, it is possible to suggest the purchase of relevant products. Some or all of the above processing in the purchasing unit may be performed using, for example, generative AI, or without generative AI.

[0104] The acquisition unit can estimate the user's emotions and adjust the method of acquiring price information based on the estimated emotions. For example, if the user is relaxed, the acquisition unit can provide detailed price information. For example, if the user is in a hurry, the acquisition unit can provide simplified price information. Furthermore, if the user is excited, the acquisition unit can provide visually appealing price information. In this way, by adjusting the method of acquiring price information based on the user's emotions, the system can provide the user with the most suitable price information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The acquisition unit can collect price information in real time from multiple e-commerce sites at the time of acquisition and present the optimal price. For example, the acquisition unit can collect price information in real time from multiple e-commerce sites and present the lowest price. For example, the acquisition unit can update price information in real time and provide the user with the best price. The acquisition unit can also compare price information from multiple e-commerce sites and present the most economical option. In this way, by collecting price information in real time from multiple e-commerce sites, the optimal price can be presented. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or without using a generative AI.

[0106] The acquisition unit can analyze price fluctuation trends by referring to past price data during acquisition and propose the optimal purchase timing. For example, the acquisition unit analyzes past price data to understand price fluctuation trends. For example, the acquisition unit can propose the optimal purchase timing based on price fluctuation trends. The acquisition unit can also update past price data in real time to provide the optimal purchase timing. This allows the acquisition unit to propose the optimal purchase timing by analyzing price fluctuation trends by referring to past price data. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or without using a generative AI.

[0107] The data acquisition unit can estimate the user's emotions and determine the priority of price information to acquire based on the estimated emotions. For example, if the user is relaxed, the data acquisition unit may prioritize acquiring detailed price information. If the user is in a hurry, for example, the data acquisition unit may prioritize acquiring simplified price information. Furthermore, if the user is excited, the data acquisition unit may prioritize acquiring visually appealing price information. In this way, by determining the priority of price information to acquire based on the user's emotions, the system can provide the user with the most optimal price information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The acquisition unit can present optimal price information by considering the user's geographical location information at the time of acquisition. For example, the acquisition unit can present optimal price information based on the user's current location. For example, the acquisition unit can present optimal price information by referring to the user's past purchase history. Furthermore, the acquisition unit can also present the most economical price information by considering the user's geographical location information. In this way, optimal price information can be presented by considering the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0109] The acquisition unit can analyze the user's social media activity and obtain price information for related products at the time of acquisition. For example, the acquisition unit can analyze the user's social media activity and obtain price information for related products. For example, the acquisition unit can obtain price information for products introduced by influencers that the user follows. The acquisition unit can also obtain price information for products that are popular on social media. In this way, price information for related products can be obtained by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, generative AI, or without using generative AI.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The camera unit can automatically measure the temperature and humidity of a room when the user photographs it, and reflect this data in a 3D model. For example, the camera unit can use room temperature and humidity sensors to acquire environmental data during shooting. By reflecting this environmental data in the 3D model, the camera unit can propose more realistic interior plans. Furthermore, if the user requests it, the camera unit can provide advice on optimal air conditioning settings and humidity control based on this environmental data. This allows the user to obtain an interior plan that takes into account the environmental conditions of the room.

[0112] The generation unit can automatically measure the acoustic characteristics of a room when analyzing images of the room taken by the user, and reflect this in a 3D model. For example, the generation unit can analyze the reverberation and sound absorption characteristics of the room and reflect them in the 3D model. Furthermore, based on these acoustic characteristics, the generation unit can also propose an interior plan to achieve an optimal acoustic environment. In addition, if the user wishes, the generation unit can suggest the placement of furniture and decorative items to improve the acoustic characteristics. As a result, the user can obtain an interior plan that takes the acoustic environment of the room into consideration.

[0113] The proposal function can estimate the user's emotions and adjust the colors of the interior plan based on those emotions. For example, if the user is relaxed, it can suggest an interior plan with calming colors. If the user is excited, it can suggest an interior plan with bright and lively colors. Furthermore, if the user is stressed, it can suggest an interior plan with calming colors. In this way, by adjusting the colors of the interior plan based on the user's emotions, it can provide an interior that matches the user's mood.

[0114] The purchasing department can suggest related accessories and decorations to users based on their purchase history and preferences when they buy furniture. For example, if a user buys a sofa, the purchasing department can suggest cushions and rugs that match the sofa. If a user buys a table, it can suggest lamps and vases that match the table. Furthermore, if a user buys a bed, it can suggest bedspreads and pillows that match the bed. This allows users to purchase related accessories and decorations along with their furniture, enabling them to create a cohesive interior.

[0115] The acquisition unit can estimate the user's emotions and adjust the way price information is displayed based on the estimated emotions. For example, if the user is relaxed, detailed price information can be displayed. If the user is in a hurry, simplified price information can be displayed. Furthermore, if the user is excited, visually appealing price information can be displayed. In this way, by adjusting the way price information is displayed based on the user's emotions, the system can provide the user with the most suitable price information.

[0116] The camera unit can automatically measure the scent of a room when the user photographs it and reflect this in a 3D model. For example, the camera unit can use a room scent sensor to acquire scent data during photography. By reflecting this scent data in the 3D model, the camera unit can propose more realistic interior plans. Furthermore, if the user requests it, the camera unit can provide advice on the optimal scent environment based on this scent data. This allows the user to obtain an interior plan that takes the scent of the room into consideration.

[0117] The generation unit can automatically measure the room's lighting conditions when analyzing images of the room taken by the user and reflect them in the 3D model. For example, the generation unit can analyze the brightness and color temperature of the room's lighting and reflect them in the 3D model. Furthermore, based on these lighting conditions, the generation unit can propose an interior plan to achieve the optimal lighting environment. In addition, if the user requests it, the generation unit can suggest the placement of lighting fixtures to improve the lighting conditions. As a result, the user can obtain an interior plan that takes the room's lighting conditions into consideration.

[0118] The proposal function can estimate the user's emotions and adjust the style of the interior plan based on those emotions. For example, if the user is relaxed, it can suggest a simple and calming style of interior plan. If the user is excited, it can suggest a colorful and lively style of interior plan. Furthermore, if the user is stressed, it can suggest a calming style of interior plan. In this way, by adjusting the style of the interior plan based on the user's emotions, it can provide an interior that matches the user's mood.

[0119] The purchasing department can suggest relevant services to users based on their purchase history and preferences when they buy furniture. For example, if a user buys a sofa, the purchasing department can suggest a sofa cleaning service. If a user buys a table, the purchasing department can suggest a table assembly service. Furthermore, if a user buys a bed, the purchasing department can suggest a bed mattress replacement service. This allows users to utilize related services along with their furniture purchases, resulting in a more convenient shopping experience.

[0120] The data acquisition unit can estimate the user's emotions and adjust the frequency of price information acquisition based on the estimated emotions. For example, if the user is relaxed, the frequency of price information acquisition can be set low. Conversely, if the user is in a hurry, the frequency of price information acquisition can be set high. Furthermore, if the user is excited, price information can be acquired in real time. In this way, by adjusting the frequency of price information acquisition based on the user's emotions, the system can provide the user with the most optimal price information.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The shooting unit takes pictures of the room using a camera app. The shooting unit can, for example, use a smartphone camera to capture images of the room. Alternatively, it can use a digital camera to capture high-resolution images. Furthermore, it can use a camera app to take multiple images in sequence to capture the overall view of the room. Step 2: The generation unit analyzes the images captured by the shooting unit and generates a 3D model of the room. The generation unit can, for example, use generation AI to perform image analysis and reflect the room's structure and furniture arrangement in the 3D model. It can also combine multiple images to generate a detailed 3D model of the room. Furthermore, specific furniture and decorations can be added to the 3D model according to the user's requests. Step 3: The proposal unit proposes an interior plan tailored to the user's preferences and budget, based on the 3D model generated by the generation unit. For example, the proposal unit can use generation AI to analyze the user's preference data and past purchase history to propose a personalized interior plan. It can also select the most suitable furniture and decorations based on the user's budget. Furthermore, it can provide interactive coaching that allows the user to change the furniture arrangement using voice commands or touch controls. Step 4: The purchasing department purchases furniture based on the interior plan proposed by the proposal department. The purchasing department can seamlessly purchase the furniture selected by the user by, for example, integrating with an e-commerce site. It can also manufacture custom-made furniture at partner factories. Furthermore, it can provide a function to automatically enter the user's payment method and shipping address information. Step 5: The acquisition unit obtains the best prices and sale information. For example, the acquisition unit can collect price information from multiple e-commerce sites and present the lowest price. It can also analyze price fluctuation trends by referring to past price data and suggest the optimal timing for purchase. Furthermore, it can estimate the user's sentiment and adjust the method of acquiring price information based on the estimated user sentiment.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the shooting unit, generation unit, proposal unit, purchase unit, and acquisition unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart device 14 to acquire images of the room. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the captured images to generate a 3D model of the room. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes an interior plan based on the generated 3D model. The purchase unit is implemented by the control unit 46A of the smart device 14, which purchases furniture based on the proposed interior plan. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12, which acquires the best prices and sales information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the shooting unit, generation unit, proposal unit, purchase unit, and acquisition unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart glasses 214 to acquire an image of the room. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the captured image to generate a 3D model of the room. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes an interior plan based on the generated 3D model. The purchase unit is implemented by the control unit 46A of the smart glasses 214, which purchases furniture based on the proposed interior plan. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12, which acquires the best prices and sale information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the shooting unit, generation unit, proposal unit, purchase unit, and acquisition unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the headset terminal 314 to acquire images of the room. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the captured images to generate a 3D model of the room. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes an interior plan based on the generated 3D model. The purchase unit is implemented by the control unit 46A of the headset terminal 314, which purchases furniture based on the proposed interior plan. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12, which acquires the best prices and sales information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the imaging unit, generation unit, proposal unit, purchase unit, and acquisition unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the robot 414 to acquire images of the room. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the captured images to generate a 3D model of the room. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes an interior plan based on the generated 3D model. The purchase unit is implemented by the control unit 46A of the robot 414, which purchases furniture based on the proposed interior plan. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12, which acquires the best prices and sales information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The shooting section involves users taking pictures of their rooms with a camera app, A generation unit analyzes the images captured by the aforementioned imaging unit and generates a 3D model of the room, Based on the 3D model generated by the generation unit, the proposal unit proposes an interior plan tailored to the user's preferences and budget. The purchasing department purchases furniture based on the interior plan proposed by the aforementioned proposal department, It includes an acquisition unit that obtains the best prices and sale information. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We manage and analyze user preference data and past purchase history to propose personalized interior design plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, We collect price information from multiple e-commerce sites and display the lowest price. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We design custom-made furniture to meet the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned purchasing department, Custom-made furniture is manufactured at our partner factories. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, It provides interactive coaching that allows you to change the furniture arrangement using voice commands or touch controls. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is It estimates the user's emotions and adjusts the shooting timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is During shooting, the lighting conditions in the room are automatically adjusted to obtain the optimal image. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is During shooting, the system automatically measures the room dimensions to improve the accuracy of 3D model generation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of rooms to photograph based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is During shooting, the system refers to the user's past shooting history to suggest the optimal shooting angle. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is During shooting, the system works in conjunction with the user's smart device to simultaneously capture images from multiple cameras. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the 3D model generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the system automatically recognizes the furniture arrangement in the room and reflects it in the 3D model. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, an algorithm is applied to realistically reproduce the texture of the room's wallpaper and flooring. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the external environment of the room is incorporated and reflected in the 3D model. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the system selects the optimal generation method by referring to the user's past 3D model generation history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the interior design plan proposal method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, we will suggest the optimal interior plan considering the user's lifestyle and family structure. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we suggest interior design plans that are tailored to the season and events. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of the interior plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we refer to the user's past interior design plan history to suggest the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and propose interior design plans based on current trends. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned purchasing department, It estimates the user's emotions and adjusts the purchase process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned purchasing department, When a user makes a purchase, we refer to their past purchase history to suggest the most suitable purchase method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned purchasing department, Provides a feature that automatically enters the user's payment method and shipping address information during the purchase process. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned purchasing department, It estimates the user's emotions and determines the priority of products to purchase based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned purchasing department, When you make a purchase, we will suggest the most suitable shipping method, taking into account your geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned purchasing department, At the time of purchase, the system analyzes the user's social media activity and suggests related products. The system described in Appendix 1, characterized by the features described herein. (Note 30) The acquisition unit is, It estimates user sentiment and adjusts how price information is retrieved based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The acquisition unit is, When acquiring data, the system collects price information in real time from multiple e-commerce sites and presents the best price. The system described in Appendix 1, characterized by the features described herein. (Note 32) The acquisition unit is, When acquiring a product, the system analyzes price fluctuation trends by referencing past price data and suggests the optimal timing for purchase. The system described in Appendix 1, characterized by the features described herein. (Note 33) The acquisition unit is, It estimates the user's sentiment and determines the priority of price information to retrieve based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The acquisition unit is, When acquiring data, the system will consider the user's geographical location to present the most suitable pricing information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The acquisition unit is, During data acquisition, the system analyzes the user's social media activity and retrieves pricing information for related products. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The shooting section involves users taking pictures of their rooms with a camera app, A generation unit analyzes the images captured by the aforementioned imaging unit and generates a 3D model of the room, Based on the 3D model generated by the generation unit, the proposal unit proposes an interior plan tailored to the user's preferences and budget. The purchasing department purchases furniture based on the interior plan proposed by the aforementioned proposal department, It includes an acquisition unit that obtains the best prices and sale information. A system characterized by the following features.

2. The aforementioned proposal section is, We manage and analyze user preference data and past purchase history to propose personalized interior design plans. The system according to feature 1.

3. The acquisition unit is, We collect price information from multiple e-commerce sites and display the lowest price. The system according to feature 1.

4. The generating unit is We design custom-made furniture to meet the user's needs. The system according to feature 1.

5. The aforementioned purchasing department, Custom-made furniture is manufactured at our partner factories. The system according to feature 1.

6. The aforementioned proposal section is, It provides interactive coaching that allows you to change the furniture arrangement using voice commands or touch controls. The system according to feature 1.

7. The aforementioned imaging unit is It estimates the user's emotions and adjusts the shooting timing based on the estimated user emotions. The system according to feature 1.

8. The aforementioned imaging unit is During shooting, the lighting conditions in the room are automatically adjusted to obtain the optimal image. The system according to feature 1.

9. The aforementioned imaging unit is During shooting, the system automatically measures the room dimensions to improve the accuracy of 3D model generation. The system according to feature 1.

10. The aforementioned imaging unit is It estimates the user's emotions and determines the priority of rooms to photograph based on the estimated user emotions. The system according to feature 1.

Images