system

Abstract

The system according to this embodiment aims to enable users to easily propose, order, and create furniture of their desired size. [Solution] The system according to this embodiment comprises a reception unit, a proposal unit, an order unit, and a supply unit. The reception unit receives size information. The proposal unit proposes furniture based on the size information entered by the reception unit. The order unit orders the furniture proposed by the proposal unit as custom-made. The supply unit provides data for DIY.

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 performed by at least one processor, the method including: 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 in 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 prior art, there is a problem that it is difficult to find furniture of an ideal size, and the options for made-to-order or DIY are limited.

[0005] The system according to the embodiment aims to enable a user to easily propose, order, and make furniture of a desired size.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, an order unit, and a supply unit. The reception unit receives size information. The proposal unit proposes furniture based on the size information entered by the reception unit. The order unit places a custom order for the furniture proposed by the proposal unit. The supply unit provides data for DIY projects. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to easily propose, order, and even create furniture of their desired size. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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) A smartphone application embodying this invention is a system that allows users to easily purchase, custom-order, or create their ideal furniture using a generative AI. The system allows users to input the desired furniture size, and the generative AI screens furniture websites based on this input, suggesting furniture of the desired size. It also partners with multiple custom furniture manufacturers, making it easy to order custom-made furniture. Furthermore, it provides data for DIY projects, allowing users to easily order pre-measured boards, handles, sliding door rails, etc. When a user inputs a photo of the location where they want to place the furniture, the generative AI generates an image of how the furniture would look in that location. The generative AI has been trained on data from e-commerce businesses, and by registering personal information and preferences, users can receive multiple suggestions tailored to trends, age groups, hobbies, etc. For example, if a user is looking for a sofa for their living room, the generative AI will suggest a sofa that suits the user's preferences and the size of the room. Furthermore, through partnerships with custom furniture manufacturers, users can order custom-made furniture that perfectly matches their ideal. Additionally, by providing DIY data, users can also create their own furniture. This application reduces compromises when purchasing furniture, enabling users to obtain the furniture they truly desire. Furthermore, it will contribute to revitalizing the custom-made furniture industry and make the service more accessible to users. For example, if a user is looking for a bookshelf that perfectly fits their room, the generating AI will suggest the optimal bookshelf, and they can even order a custom-made one. In addition, by providing data for DIY, users can even create their own bookshelves. In this way, the smartphone app will enable users to easily purchase, custom-order, or build furniture of their ideal size.

[0029] The smartphone application according to this embodiment comprises a reception unit, a suggestion unit, an order unit, and a delivery unit. The reception unit receives size information for the furniture the user requests. Size information can be entered, for example, by LiDAR distance measurement, manual input, voice input, or from an image. For example, the reception unit measures the dimensions of a room using LiDAR and inputs that data. The reception unit can also receive manual input from the user. Furthermore, the reception unit can receive input from voice. For example, the user might voice-input "a shelf that is 2 meters wide and 1 meter high." For input from an image, for example, the user takes a picture of the room and the image is analyzed to obtain the dimensions. The suggestion unit uses a generation AI to suggest furniture based on the size information entered by the reception unit. The generation AI, for example, screens furniture websites and suggests furniture of the desired size. The generation AI also suggests furniture that suits the user's preferences and room size. In addition, the generation AI learns the user's personal information and preferences and makes multiple suggestions that match trends, age group, and hobbies. For example, the generation AI suggests the most suitable furniture based on the user's past purchase history and preferences. The ordering unit places a custom order for the furniture suggested by the suggestion unit. The ordering unit, for example, partners with a custom furniture manufacturer to enable the user to order custom-made furniture. The ordering unit, for example, takes the user's desired design and dimensions and orders custom-made furniture based on that. The supply unit provides DIY data. The supply unit provides DIY data such as pre-measured boards, handles, and sliding door rails. The supply unit provides, for example, blueprints and material lists for the user to make their own furniture. This enables the smartphone app according to the embodiment to easily purchase, custom-order, or make furniture of the ideal size.

[0030] The reception desk receives the size information of the furniture the user requests. Size information can be entered using methods such as LiDAR distance measurement, manual input, voice input, or from images. Specifically, when using LiDAR, the LiDAR sensor built into the smartphone accurately measures the room dimensions and automatically inputs that data into the app. This allows the user to obtain accurate dimension information without any effort. For manual input, the user measures the room dimensions using a measuring tape or similar tool and inputs the values ​​into the app. Manual input is available even on smartphones without a LiDAR sensor, offering flexibility to meet user needs. For voice input, the user inputs dimensions by voice through the smartphone's microphone. For example, by voice-instructing specific instructions such as "a shelf 2 meters wide and 1 meter high," the app recognizes and inputs that information as text data. Image input involves the user taking a photo of the room, and the image is analyzed to obtain dimensions. AI technology is used in the image analysis to identify the location of objects and walls in the photo and calculate the dimensions. This allows the user to easily obtain room dimension information. The reception desk improves convenience by providing these diverse input methods, allowing users to input dimension information in the way that is most convenient for them.

[0031] The proposal department uses a generative AI to suggest furniture based on the size information entered by the reception department. For example, the generative AI screens furniture websites and suggests furniture of the desired size. Specifically, the generative AI collects data from multiple online furniture sales sites and searches for furniture that matches the size information entered by the user. Furthermore, the generative AI learns the user's past purchase history and preferences to suggest furniture that suits the user's tastes and room size. For example, based on information such as the style, color, and material of furniture the user has purchased in the past, the generative AI analyzes the user's preferences and suggests the most suitable furniture. The generative AI can also make multiple suggestions tailored to trends, age groups, and hobbies. For example, it might suggest modern furniture designs for younger generations and classic designs for middle-aged and older generations. The generative AI comprehensively analyzes this information to suggest the most suitable furniture for the user. In addition, the generative AI can perform a furniture placement simulation. Based on the room dimensions entered by the user, it displays a 3D model showing how the suggested furniture would be placed in the room. This allows the user to visually confirm the furniture placement image and form a concrete image before purchasing.

[0032] The Order Department allows users to order custom-made furniture proposed by the Proposal Department. Specifically, the Order Department partners with custom furniture manufacturers, enabling users to input their desired design and dimensions and order custom-made furniture based on that input. For example, if a user likes a proposed furniture design, they can customize the dimensions, materials, colors, and other details based on that design and place an order. The Order Department sends the customized information entered by the user to the custom furniture manufacturer, who then manufactures the furniture based on that information. Furthermore, the Order Department also has a function to notify users of the progress of the manufacturing process. For example, by sending notifications to the user when the furniture manufacturing has started and when it is completed and ready for shipment, users can keep track of the status of their ordered furniture in real time. The Order Department also supports the selection of payment methods and delivery options. Users can choose payment methods such as credit cards and electronic money, and specify their desired delivery date, time, and destination. In this way, the Order Department provides an environment in which users can easily and smoothly order custom-made furniture, improving user satisfaction.

[0033] The service provider offers DIY data. Specifically, it provides pre-measured boards, handles, sliding door rails, and other DIY data. For example, it provides blueprints and material lists for users to build their own furniture. Based on the size information entered by the user, the service provider calculates the dimensions and quantities of the necessary materials and generates blueprints accordingly. The blueprints include detailed dimensions of each part and assembly instructions, allowing users to build their own furniture based on these blueprints. Furthermore, the service provider also offers video tutorials for DIY. Users can learn how to assemble furniture by watching videos within the app. The videos introduce specific tool usage and assembly tips, making it easy for even beginners to enjoy DIY. The service provider also provides links for purchasing necessary materials online. Users can check the material list within the app and purchase the necessary materials directly from online shops. In this way, the service provider provides all the information and support users need to build their ideal furniture, offering the enjoyment and sense of accomplishment of DIY.

[0034] The reception unit can input size information via LiDAR distance measurement, manual input, voice input, or from images. For example, the reception unit can measure the dimensions of a room using LiDAR and input that data. The reception unit can also accept manual input from the user. The reception unit can also accept input from the user using voice input. For example, the user might voice-input "a shelf that is 2 meters wide and 1 meter high." The reception unit can also accept a photo of the room taken by the user, and analyze the image to obtain the dimensions. This allows for input of size information in a variety of ways. Some or all of the above-described processes in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data acquired by LiDAR into a generating AI and have the generating AI perform the analysis of the size information.

[0035] The suggestion unit can screen furniture websites using generative AI and suggest furniture of the desired size. For example, the suggestion unit uses generative AI to screen furniture websites and suggest furniture of the desired size. For example, the suggestion unit suggests furniture that suits the user's preferences and room size. For example, the suggestion unit learns the user's personal information and preferences and makes multiple suggestions that match trends, age group, and hobbies. In this way, by using generative AI, it is possible to suggest furniture of the desired size. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input data from furniture websites into the generative AI and have the generative AI suggest furniture of the desired size.

[0036] The suggestion unit can learn the user's personal information and preferences using a generative AI and make suggestions that match trends, age group, and hobbies. For example, the suggestion unit can use a generative AI to learn the user's personal information and preferences and make multiple suggestions that match trends, age group, and hobbies. For example, the suggestion unit can suggest the most suitable furniture based on the user's past purchase history and preferences. This allows the suggestion unit to suggest the most suitable furniture based on the user's personal information and preferences. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input data on the user's personal information and preferences into a generative AI and have the generative AI make suggestions for the most suitable furniture.

[0037] The ordering department partners with custom furniture manufacturers to enable users to order custom-made furniture. For example, the ordering department partners with custom furniture manufacturers to enable users to order custom-made furniture. For example, the ordering department takes the user's desired design and dimensions as input and orders custom-made furniture based on that input. This makes it easy for users to order custom-made furniture. Some or all of the above processes in the ordering department may be performed using AI, or not using AI. For example, the ordering department can input the user's desired design and dimensions into a generating AI and have the generating AI execute the order for custom-made furniture.

[0038] The provisioning unit can provide DIY data for pre-measured boards and handles, and sliding door rails. For example, the provisioning unit provides DIY data such as pre-measured boards, handles, and sliding door rails. For example, the provisioning unit provides blueprints and material lists for users to make their own furniture. This makes it easy for users to obtain DIY data. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input DIY data into a generating AI and have the generating AI perform the task of providing the optimal DIY data.

[0039] The proposal unit can take a photo of the place where the user wants to place the furniture and use a generation AI to generate an image of what it would look like with the furniture in place. For example, if the user takes a photo of the place where they want to place the furniture, the generation AI will generate an image of what it would look like with the furniture in place. The proposal unit can also allow the user to take a photo of their room and input that image into the generation AI, allowing them to visually confirm what the furniture would look like with the furniture in place. This allows the user to visually confirm what the furniture would look like with the furniture in place. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input photo data of a room into a generation AI and have the generation AI generate an image of what it would look like with the furniture in place.

[0040] The reception unit can analyze the user's past input history and automatically select the optimal input method. For example, the reception unit can automatically display size information that the user has frequently entered in the past as a candidate. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest size information to be used during a specific time period based on the user's past input history. This allows the system to automatically select the optimal input method based on past input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0041] The reception unit can filter the input content based on the user's current project and areas of interest when size information is entered. For example, the reception unit may prioritize displaying size information related to the user's current project. For example, the reception unit may filter and suggest relevant size information based on the user's areas of interest. For example, the reception unit may suggest appropriate size information based on projects the user has shown interest in in the past. This allows the user to input appropriate size information based on their current project and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input data on the user's projects and areas of interest into a generating AI and have the generating AI perform the filtering of the input content.

[0042] The reception unit can prioritize presenting the most relevant input method when the user enters size information, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize prompting the user to enter size information relevant to that region. For example, the reception unit will suggest the optimal input method based on the user's geographical location. For example, if the user is on the move, the reception unit will prompt the user to enter relevant size information based on their current location. This allows the reception unit to present the optimal input method based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of presenting the most relevant input method.

[0043] The reception unit can analyze the user's social media activity when size information is entered and suggest relevant input methods. For example, the reception unit can suggest relevant size information based on information the user has shared on social media. For example, the reception unit can suggest size information related to projects the user is interested in based on the user's social media activity. For example, the reception unit can suggest appropriate size information based on information from accounts the user follows on social media. This allows the reception unit to suggest relevant input methods based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input methods.

[0044] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the furniture. For example, if the user is looking for important furniture, the AI ​​will generate suggestions that include detailed information. If the user is looking for supplementary furniture, the AI ​​will generate suggestions that include concise information. If the user shows a strong interest in a particular piece of furniture, the AI ​​will generate detailed suggestions about that furniture. This allows the level of detail in suggestions to be adjusted according to the importance of the furniture. Some or all of the above processing in the suggestion unit may be performed using the AI, or not. For example, the suggestion unit can input furniture importance data into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0045] The proposal unit can apply different proposal algorithms depending on the type of furniture when making a proposal. For example, in the case of a sofa, the proposal unit's generating AI will make a proposal that prioritizes comfort and design. For example, in the case of a table, the proposal unit's generating AI will make a proposal that prioritizes functionality and size. For example, in the case of storage furniture, the proposal unit's generating AI will make a proposal that prioritizes storage capacity and ease of placement. This allows the proposal unit to provide the optimal proposal according to the type of furniture. Some or all of the above processing in the proposal unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the proposal unit can input furniture type data into the generating AI and have the generating AI execute the application of different proposal algorithms.

[0046] The proposal department can determine the priority of proposals based on the furniture submission date and time. For example, if a user is looking for furniture they need urgently, the proposal department will prioritize suggesting the furniture that can be obtained as soon as possible. For example, if a user is looking for furniture needed at a specific time, the proposal department's AI will generate suggestions tailored to that time. For example, if a user is looking for furniture to be used long-term, the proposal department will prioritize suggesting highly durable furniture. This allows for the determination of the optimal priority of proposals based on the furniture submission date and time. Some or all of the above processing in the proposal department may be performed using, for example, the AI, or not. For example, the proposal department can input furniture submission date and time data into the AI ​​and have the AI ​​determine the priority of proposals.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the furniture during the suggestion process. For example, if the user shows a strong interest in a particular piece of furniture, the suggestion unit will suggest that piece of furniture first. For example, if the user is looking for multiple pieces of furniture, the suggestion unit will prioritize suggesting the most relevant furniture. For example, if the user is looking for furniture based on a specific theme, the suggestion unit will prioritize suggesting furniture related to that theme. This allows the system to provide an optimal order of suggestions based on the relevance of the furniture. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input furniture relevance data into a generative AI and have the generative AI adjust the order of suggestions.

[0048] The order processing unit can analyze the user's past order history to select the optimal ordering method at the time of ordering. For example, the order processing unit may prioritize suggesting ordering methods that the user has frequently used in the past. For example, the order processing unit may predict and suggest ordering methods to be used at specific times based on the user's past order history. For example, the order processing unit may analyze the trends of products the user has ordered in the past and suggest the optimal ordering method. This allows the system to select the optimal ordering method based on past order history. Some or all of the above processing in the order processing unit may be performed using AI, for example, or without AI. For example, the order processing unit may input the user's past order history data into a generating AI and have the generating AI select the optimal ordering method.

[0049] The ordering system can adjust the ordering method based on the user's current lifestyle when an order is placed. For example, if the user is busy, the ordering system can provide a quick ordering method. For example, if the user is relaxed, the ordering system can provide detailed ordering options. For example, if the user is in a specific lifestyle situation, the ordering system can provide an ordering method tailored to that situation. This allows the system to provide the optimal ordering method based on the user's current lifestyle. Some or all of the above processing in the ordering system may be performed using AI, for example, or without AI. For example, the ordering system can input user lifestyle data into a generating AI and have the generating AI perform the adjustment of the ordering method.

[0050] The ordering unit can select the optimal ordering method when an order is placed, taking into account the user's geographical location. For example, if the user is in a specific region, the ordering unit will prioritize providing ordering methods relevant to that region. For example, the ordering unit will suggest the optimal ordering method based on the user's geographical location. For example, if the user is on the move, the ordering unit will provide relevant ordering methods based on their current location. This allows the ordering unit to provide the optimal ordering method based on geographical location. Some or all of the above processing in the ordering unit may be performed using AI, for example, or without AI. For example, the ordering unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal ordering method.

[0051] The ordering department can analyze a user's social media activity and suggest ordering methods at the time of ordering. For example, the ordering department can suggest relevant ordering methods based on information shared by the user on social media. For example, the ordering department can suggest ordering methods related to products of interest based on the user's social media activity. For example, the ordering department can suggest appropriate ordering methods based on information about accounts followed by the user on social media. This allows the system to provide the optimal ordering method based on social media activity. Some or all of the above processing in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the user's social media activity data into a generating AI and have the generating AI perform the ordering method suggestion.

[0052] The data provider can provide optimal data by referring to the user's past DIY history when providing DIY data. For example, the data provider can provide relevant data based on DIY projects the user has created in the past. For example, the data provider can suggest optimal materials and tools based on the user's past DIY history. For example, the data provider can provide improved data based on DIY data the user has used in the past. This allows the data provider to provide optimal DIY data based on the user's past DIY history. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input the user's past DIY history data into a generating AI and have the generating AI perform the task of providing optimal DIY data.

[0053] The data provider can adjust the data based on the user's current project when providing DIY data. For example, the provider can prioritize providing data related to the user's current project. For example, the provider can suggest the most suitable materials and tools based on the user's current project. For example, the provider can customize and provide the data the user needs for their current project. This allows the provider to provide optimal DIY data based on the current project. Some or all of the above processing in the data provider may be performed using AI, for example, or not using AI. For example, the data provider can input the user's current project data into a generating AI and have the generating AI perform the data adjustments.

[0054] The data provider can provide optimal DIY data by considering the user's geographical location when providing DIY data. For example, if the user is in a specific region, the data provider can provide DIY data relevant to that region. For example, the data provider can suggest optimal materials and tools based on the user's geographical location. For example, if the user is on the move, the data provider can provide relevant DIY data based on the user's current location. This allows the data provider to provide optimal DIY data based on geographical location. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal DIY data.

[0055] The service provider can analyze the user's social media activity and suggest data when providing DIY data. For example, the service provider can suggest relevant DIY data based on information shared by the user on social media. For example, the service provider can suggest DIY data related to projects of interest based on the user's social media activity. For example, the service provider can suggest appropriate DIY data based on information about accounts the user follows on social media. This allows the service provider to provide optimal DIY data based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the DIY data suggestion.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The suggestion unit can analyze a user's past purchase history and suggest the most suitable furniture. For example, it can suggest furniture of a similar style and color based on the style and color of furniture the user has purchased in the past. It can suggest furniture of the same size based on the size of furniture the user has purchased in the past. It can suggest furniture of the same brand based on the brand of furniture the user has purchased in the past. In this way, the system can suggest the most suitable furniture based on past purchase history. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past purchase history data into a generating AI and have the generating AI perform the task of suggesting the most suitable furniture.

[0058] The suggestion unit can adjust the content of its suggestions based on the user's current living situation. For example, if the user has just moved into a new house, it will prioritize suggesting basic furniture. If the user has children, it will prioritize suggesting furniture suitable for children. If the user has pets, it will prioritize suggesting furniture suitable for pets. This allows the suggestion unit to provide optimal suggestions based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's living situation data into a generating AI and have the generating AI adjust the content of the suggestions.

[0059] The suggestion unit can propose the most suitable furniture considering the user's geographical location. For example, if the user lives in a cold region, it will suggest furniture made of warm materials. If the user lives in a humid region, it will suggest furniture that is resistant to humidity. If the user lives in an urban area, it will suggest compact and functional furniture. In this way, the optimal furniture can be suggested based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of suggesting the most suitable furniture.

[0060] The suggestion unit can analyze a user's social media activity and suggest relevant furniture. For example, it can suggest relevant furniture based on information the user has shared on social media. It can suggest appropriate furniture based on information about accounts the user follows on social media. It can suggest relevant furniture based on projects the user has shown interest in on social media. This allows the unit to suggest the most suitable furniture based on social media activity. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant furniture.

[0061] The suggestion unit can adjust the content of its suggestions based on the user's current project. For example, it can prioritize suggesting furniture related to the user's current project. It can customize and suggest furniture that the user needs for their current project. It can suggest the most suitable materials and tools based on the user's current project. This allows the suggestion unit to provide the most suitable suggestions based on the user's current project. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's current project data into a generating AI and have the generating AI adjust the content of the suggestions.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives the size information of the furniture requested by the user. Size information can be entered, for example, by LiDAR distance measurement, manual input, voice input, or from an image. For example, the reception desk measures the dimensions of the room using LiDAR and inputs that data. The reception desk can also receive manual input from the user. Furthermore, the reception desk can receive input using voice input. For example, the user can voice-input "a shelf that is 2 meters wide and 1 meter high." For input from an image, for example, the user takes a picture of the room and the image is analyzed to obtain the dimensions. Step 2: The suggestion department uses a generation AI to propose furniture based on the size information entered by the reception department. The generation AI can, for example, screen furniture websites and propose furniture that matches the desired size. The generation AI can also, for example, propose furniture that suits the user's preferences and room size. Furthermore, the generation AI learns the user's personal information and preferences and makes multiple suggestions that match trends, age group, and hobbies. For example, the generation AI can propose the most suitable furniture based on the user's past purchase history and preferences. Step 3: The ordering department places custom orders for the furniture proposed by the proposal department. The ordering department, for example, partners with custom furniture manufacturers to enable users to order custom-made furniture. The ordering department, for example, takes the user's desired design and dimensions and places a custom order for the furniture based on that information. Step 4: The provider provides DIY data. The provider provides DIY data such as pre-measured boards, handles, and sliding door rails. The provider also provides, for example, blueprints and material lists for users to make their own furniture.

[0064] (Example of form 2) A smartphone application embodying this invention is a system that allows users to easily purchase, custom-order, or create their ideal furniture using a generative AI. The system allows users to input the desired furniture size, and the generative AI screens furniture websites based on this input, suggesting furniture of the desired size. It also partners with multiple custom furniture manufacturers, making it easy to order custom-made furniture. Furthermore, it provides data for DIY projects, allowing users to easily order pre-measured boards, handles, sliding door rails, etc. When a user inputs a photo of the location where they want to place the furniture, the generative AI generates an image of how the furniture would look in that location. The generative AI has been trained on data from e-commerce businesses, and by registering personal information and preferences, users can receive multiple suggestions tailored to trends, age groups, hobbies, etc. For example, if a user is looking for a sofa for their living room, the generative AI will suggest a sofa that suits the user's preferences and the size of the room. Furthermore, through partnerships with custom furniture manufacturers, users can order custom-made furniture that perfectly matches their ideal. Additionally, by providing DIY data, users can also create their own furniture. This application reduces compromises when purchasing furniture, enabling users to obtain the furniture they truly desire. Furthermore, it will contribute to revitalizing the custom-made furniture industry and make the service more accessible to users. For example, if a user is looking for a bookshelf that perfectly fits their room, the generating AI will suggest the optimal bookshelf, and they can even order a custom-made one. In addition, by providing data for DIY, users can even create their own bookshelves. In this way, the smartphone app will enable users to easily purchase, custom-order, or build furniture of their ideal size.

[0065] The smartphone application according to this embodiment comprises a reception unit, a suggestion unit, an order unit, and a delivery unit. The reception unit receives size information for the furniture the user requests. Size information can be entered, for example, by LiDAR distance measurement, manual input, voice input, or from an image. For example, the reception unit measures the dimensions of a room using LiDAR and inputs that data. The reception unit can also receive manual input from the user. Furthermore, the reception unit can receive input from voice. For example, the user might voice-input "a shelf that is 2 meters wide and 1 meter high." For input from an image, for example, the user takes a picture of the room and the image is analyzed to obtain the dimensions. The suggestion unit uses a generation AI to suggest furniture based on the size information entered by the reception unit. The generation AI, for example, screens furniture websites and suggests furniture of the desired size. The generation AI also suggests furniture that suits the user's preferences and room size. In addition, the generation AI learns the user's personal information and preferences and makes multiple suggestions that match trends, age group, and hobbies. For example, the generation AI suggests the most suitable furniture based on the user's past purchase history and preferences. The ordering unit places a custom order for the furniture suggested by the suggestion unit. The ordering unit, for example, partners with a custom furniture manufacturer to enable the user to order custom-made furniture. The ordering unit, for example, takes the user's desired design and dimensions and orders custom-made furniture based on that. The supply unit provides DIY data. The supply unit provides DIY data such as pre-measured boards, handles, and sliding door rails. The supply unit provides, for example, blueprints and material lists for the user to make their own furniture. This enables the smartphone app according to the embodiment to easily purchase, custom-order, or make furniture of the ideal size.

[0066] The reception desk receives the size information of the furniture the user requests. Size information can be entered using methods such as LiDAR distance measurement, manual input, voice input, or from images. Specifically, when using LiDAR, the LiDAR sensor built into the smartphone accurately measures the room dimensions and automatically inputs that data into the app. This allows the user to obtain accurate dimension information without any effort. For manual input, the user measures the room dimensions using a measuring tape or similar tool and inputs the values ​​into the app. Manual input is available even on smartphones without a LiDAR sensor, offering flexibility to meet user needs. For voice input, the user inputs dimensions by voice through the smartphone's microphone. For example, by voice-instructing specific instructions such as "a shelf 2 meters wide and 1 meter high," the app recognizes and inputs that information as text data. Image input involves the user taking a photo of the room, and the image is analyzed to obtain dimensions. AI technology is used in the image analysis to identify the location of objects and walls in the photo and calculate the dimensions. This allows the user to easily obtain room dimension information. The reception desk improves convenience by providing these diverse input methods, allowing users to input dimension information in the way that is most convenient for them.

[0067] The proposal department uses a generative AI to suggest furniture based on the size information entered by the reception department. For example, the generative AI screens furniture websites and suggests furniture of the desired size. Specifically, the generative AI collects data from multiple online furniture sales sites and searches for furniture that matches the size information entered by the user. Furthermore, the generative AI learns the user's past purchase history and preferences to suggest furniture that suits the user's tastes and room size. For example, based on information such as the style, color, and material of furniture the user has purchased in the past, the generative AI analyzes the user's preferences and suggests the most suitable furniture. The generative AI can also make multiple suggestions tailored to trends, age groups, and hobbies. For example, it might suggest modern furniture designs for younger generations and classic designs for middle-aged and older generations. The generative AI comprehensively analyzes this information to suggest the most suitable furniture for the user. In addition, the generative AI can perform a furniture placement simulation. Based on the room dimensions entered by the user, it displays a 3D model showing how the suggested furniture would be placed in the room. This allows the user to visually confirm the furniture placement image and form a concrete image before purchasing.

[0068] The Order Department allows users to order custom-made furniture proposed by the Proposal Department. Specifically, the Order Department partners with custom furniture manufacturers, enabling users to input their desired design and dimensions and order custom-made furniture based on that input. For example, if a user likes a proposed furniture design, they can customize the dimensions, materials, colors, and other details based on that design and place an order. The Order Department sends the customized information entered by the user to the custom furniture manufacturer, who then manufactures the furniture based on that information. Furthermore, the Order Department also has a function to notify users of the progress of the manufacturing process. For example, by sending notifications to the user when the furniture manufacturing has started and when it is completed and ready for shipment, users can keep track of the status of their ordered furniture in real time. The Order Department also supports the selection of payment methods and delivery options. Users can choose payment methods such as credit cards and electronic money, and specify their desired delivery date, time, and destination. In this way, the Order Department provides an environment in which users can easily and smoothly order custom-made furniture, improving user satisfaction.

[0069] The service provider offers DIY data. Specifically, it provides pre-measured boards, handles, sliding door rails, and other DIY data. For example, it provides blueprints and material lists for users to build their own furniture. Based on the size information entered by the user, the service provider calculates the dimensions and quantities of the necessary materials and generates blueprints accordingly. The blueprints include detailed dimensions of each part and assembly instructions, allowing users to build their own furniture based on these blueprints. Furthermore, the service provider also offers video tutorials for DIY. Users can learn how to assemble furniture by watching videos within the app. The videos introduce specific tool usage and assembly tips, making it easy for even beginners to enjoy DIY. The service provider also provides links for purchasing necessary materials online. Users can check the material list within the app and purchase the necessary materials directly from online shops. In this way, the service provider provides all the information and support users need to build their ideal furniture, offering the enjoyment and sense of accomplishment of DIY.

[0070] The reception unit can input size information via LiDAR distance measurement, manual input, voice input, or from images. For example, the reception unit can measure the dimensions of a room using LiDAR and input that data. The reception unit can also accept manual input from the user. The reception unit can also accept input from the user using voice input. For example, the user might voice-input "a shelf that is 2 meters wide and 1 meter high." The reception unit can also accept a photo of the room taken by the user, and analyze the image to obtain the dimensions. This allows for input of size information in a variety of ways. Some or all of the above-described processes in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data acquired by LiDAR into a generating AI and have the generating AI perform the analysis of the size information.

[0071] The suggestion unit can screen furniture websites using generative AI and suggest furniture of the desired size. For example, the suggestion unit uses generative AI to screen furniture websites and suggest furniture of the desired size. For example, the suggestion unit suggests furniture that suits the user's preferences and room size. For example, the suggestion unit learns the user's personal information and preferences and makes multiple suggestions that match trends, age group, and hobbies. In this way, by using generative AI, it is possible to suggest furniture of the desired size. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input data from furniture websites into the generative AI and have the generative AI suggest furniture of the desired size.

[0072] The suggestion unit can learn the user's personal information and preferences using a generative AI and make suggestions that match trends, age group, and hobbies. For example, the suggestion unit can use a generative AI to learn the user's personal information and preferences and make multiple suggestions that match trends, age group, and hobbies. For example, the suggestion unit can suggest the most suitable furniture based on the user's past purchase history and preferences. This allows the suggestion unit to suggest the most suitable furniture based on the user's personal information and preferences. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input data on the user's personal information and preferences into a generative AI and have the generative AI make suggestions for the most suitable furniture.

[0073] The ordering department partners with custom furniture manufacturers to enable users to order custom-made furniture. For example, the ordering department partners with custom furniture manufacturers to enable users to order custom-made furniture. For example, the ordering department takes the user's desired design and dimensions as input and orders custom-made furniture based on that input. This makes it easy for users to order custom-made furniture. Some or all of the above processes in the ordering department may be performed using AI, or not using AI. For example, the ordering department can input the user's desired design and dimensions into a generating AI and have the generating AI execute the order for custom-made furniture.

[0074] The provisioning unit can provide DIY data for pre-measured boards and handles, and sliding door rails. For example, the provisioning unit provides DIY data such as pre-measured boards, handles, and sliding door rails. For example, the provisioning unit provides blueprints and material lists for users to make their own furniture. This makes it easy for users to obtain DIY data. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input DIY data into a generating AI and have the generating AI perform the task of providing the optimal DIY data.

[0075] The proposal unit can take a photo of the place where the user wants to place the furniture and use a generation AI to generate an image of what it would look like with the furniture in place. For example, if the user takes a photo of the place where they want to place the furniture, the generation AI will generate an image of what it would look like with the furniture in place. The proposal unit can also allow the user to take a photo of their room and input that image into the generation AI, allowing them to visually confirm what the furniture would look like with the furniture in place. This allows the user to visually confirm what the furniture would look like with the furniture in place. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input photo data of a room into a generation AI and have the generation AI generate an image of what it would look like with the furniture in place.

[0076] The reception desk can estimate the user's emotions and suggest a method for inputting size information based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of size information. This allows the system to suggest the optimal input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI suggest an emotion-based input method.

[0077] The reception unit can analyze the user's past input history and automatically select the optimal input method. For example, the reception unit can automatically display size information that the user has frequently entered in the past as a candidate. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest size information to be used during a specific time period based on the user's past input history. This allows the system to automatically select the optimal input method based on past input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0078] The reception unit can filter the input content based on the user's current project and areas of interest when size information is entered. For example, the reception unit may prioritize displaying size information related to the user's current project. For example, the reception unit may filter and suggest relevant size information based on the user's areas of interest. For example, the reception unit may suggest appropriate size information based on projects the user has shown interest in in the past. This allows the user to input appropriate size information based on their current project and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input data on the user's projects and areas of interest into a generating AI and have the generating AI perform the filtering of the input content.

[0079] The reception unit can estimate the user's emotions and determine the priority of size information to be entered based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize the input of the most important size information. If the user is relaxed, the reception unit will prioritize the input of detailed size information. If the user is in a hurry, the reception unit will prioritize the input of minimal size information. This allows the system to determine the priority of size information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform the emotion-based priority determination of size information.

[0080] The reception unit can prioritize presenting the most relevant input method when the user enters size information, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize prompting the user to enter size information relevant to that region. For example, the reception unit will suggest the optimal input method based on the user's geographical location. For example, if the user is on the move, the reception unit will prompt the user to enter relevant size information based on their current location. This allows the reception unit to present the optimal input method based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of presenting the most relevant input method.

[0081] The reception unit can analyze the user's social media activity when size information is entered and suggest relevant input methods. For example, the reception unit can suggest relevant size information based on information the user has shared on social media. For example, the reception unit can suggest size information related to projects the user is interested in based on the user's social media activity. For example, the reception unit can suggest appropriate size information based on information from accounts the user follows on social media. This allows the reception unit to suggest relevant input methods based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input methods.

[0082] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit's generating AI will provide suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit's generating AI will provide concise and to-the-point suggestions. If the user is excited, the suggestion unit's generating AI will provide visually appealing suggestions. This allows the suggestion unit to provide the most appropriate way of presenting suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using a generating AI, or not. For example, the suggestion unit can input user emotion data into a generating AI and have the generating AI adjust the way suggestions are presented based on those emotions.

[0083] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the furniture. For example, if the user is looking for important furniture, the AI ​​will generate suggestions that include detailed information. If the user is looking for supplementary furniture, the AI ​​will generate suggestions that include concise information. If the user shows a strong interest in a particular piece of furniture, the AI ​​will generate detailed suggestions about that furniture. This allows the level of detail in suggestions to be adjusted according to the importance of the furniture. Some or all of the above processing in the suggestion unit may be performed using the AI, or not. For example, the suggestion unit can input furniture importance data into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0084] The proposal unit can apply different proposal algorithms depending on the type of furniture when making a proposal. For example, in the case of a sofa, the proposal unit's generating AI will make a proposal that prioritizes comfort and design. For example, in the case of a table, the proposal unit's generating AI will make a proposal that prioritizes functionality and size. For example, in the case of storage furniture, the proposal unit's generating AI will make a proposal that prioritizes storage capacity and ease of placement. This allows the proposal unit to provide the optimal proposal according to the type of furniture. Some or all of the above processing in the proposal unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the proposal unit can input furniture type data into the generating AI and have the generating AI execute the application of different proposal algorithms.

[0085] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit's generating AI will produce short, concise suggestions. If the user is relaxed, the suggestion unit's generating AI will produce longer suggestions with detailed explanations. If the user is excited, the suggestion unit's generating AI will produce visually stimulating suggestions. This allows for the provision of optimal suggestion lengths tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using a generating AI, or not. For example, the suggestion unit can input user emotion data into a generating AI and have the generating AI adjust the length of suggestions based on the emotions.

[0086] The proposal department can determine the priority of proposals based on the furniture submission date and time. For example, if a user is looking for furniture they need urgently, the proposal department will prioritize suggesting the furniture that can be obtained as soon as possible. For example, if a user is looking for furniture needed at a specific time, the proposal department's AI will generate suggestions tailored to that time. For example, if a user is looking for furniture to be used long-term, the proposal department will prioritize suggesting highly durable furniture. This allows for the determination of the optimal priority of proposals based on the furniture submission date and time. Some or all of the above processing in the proposal department may be performed using, for example, the AI, or not. For example, the proposal department can input furniture submission date and time data into the AI ​​and have the AI ​​determine the priority of proposals.

[0087] The suggestion unit can adjust the order of suggestions based on the relevance of the furniture during the suggestion process. For example, if the user shows a strong interest in a particular piece of furniture, the suggestion unit will suggest that piece of furniture first. For example, if the user is looking for multiple pieces of furniture, the suggestion unit will prioritize suggesting the most relevant furniture. For example, if the user is looking for furniture based on a specific theme, the suggestion unit will prioritize suggesting furniture related to that theme. This allows the system to provide an optimal order of suggestions based on the relevance of the furniture. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input furniture relevance data into a generative AI and have the generative AI adjust the order of suggestions.

[0088] The ordering system can estimate the user's emotions and adjust the ordering method based on those emotions. For example, if the user is relaxed, the ordering system can offer detailed ordering options and suggest a customizable ordering method. If the user is in a hurry, the ordering system can offer a concise and quick ordering method. If the user is excited, the ordering system can offer a visually appealing ordering interface. This allows the system to provide the optimal ordering method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using AI or not. For example, the ordering system can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the ordering method.

[0089] The order processing unit can analyze the user's past order history to select the optimal ordering method at the time of ordering. For example, the order processing unit may prioritize suggesting ordering methods that the user has frequently used in the past. For example, the order processing unit may predict and suggest ordering methods to be used at specific times based on the user's past order history. For example, the order processing unit may analyze the trends of products the user has ordered in the past and suggest the optimal ordering method. This allows the system to select the optimal ordering method based on past order history. Some or all of the above processing in the order processing unit may be performed using AI, for example, or without AI. For example, the order processing unit may input the user's past order history data into a generating AI and have the generating AI select the optimal ordering method.

[0090] The ordering system can adjust the ordering method based on the user's current lifestyle when an order is placed. For example, if the user is busy, the ordering system can provide a quick ordering method. For example, if the user is relaxed, the ordering system can provide detailed ordering options. For example, if the user is in a specific lifestyle situation, the ordering system can provide an ordering method tailored to that situation. This allows the system to provide the optimal ordering method based on the user's current lifestyle. Some or all of the above processing in the ordering system may be performed using AI, for example, or without AI. For example, the ordering system can input user lifestyle data into a generating AI and have the generating AI perform the adjustment of the ordering method.

[0091] The order processing unit can estimate the user's emotions and prioritize orders based on those emotions. For example, if the user is in a hurry, the order processing unit will prioritize the most important orders. If the user is relaxed, the order processing unit will prioritize detailed orders. If the user is excited, the order processing unit will prioritize visually appealing orders. This allows for the provision of optimal order prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the order processing unit may be performed using AI or not. For example, the order processing unit can input user emotion data into a generative AI and have the generative AI perform emotion-based order prioritization.

[0092] The ordering unit can select the optimal ordering method when an order is placed, taking into account the user's geographical location. For example, if the user is in a specific region, the ordering unit will prioritize providing ordering methods relevant to that region. For example, the ordering unit will suggest the optimal ordering method based on the user's geographical location. For example, if the user is on the move, the ordering unit will provide relevant ordering methods based on their current location. This allows the ordering unit to provide the optimal ordering method based on geographical location. Some or all of the above processing in the ordering unit may be performed using AI, for example, or without AI. For example, the ordering unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal ordering method.

[0093] The ordering department can analyze a user's social media activity and suggest ordering methods at the time of ordering. For example, the ordering department can suggest relevant ordering methods based on information shared by the user on social media. For example, the ordering department can suggest ordering methods related to products of interest based on the user's social media activity. For example, the ordering department can suggest appropriate ordering methods based on information about accounts followed by the user on social media. This allows the system to provide the optimal ordering method based on social media activity. Some or all of the above processing in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the user's social media activity data into a generating AI and have the generating AI perform the ordering method suggestion.

[0094] The service provider can estimate the user's emotions and adjust the method of providing DIY data based on the estimated user emotions. For example, if the user is relaxed, the service provider can provide detailed DIY data. For example, if the user is in a hurry, the service provider can provide concise and to-the-point DIY data. For example, if the user is excited, the service provider can provide visually appealing DIY data. This allows the service provider to provide the optimal method of providing DIY data according to the user's emotions. 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. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the method of providing DIY data based on emotions.

[0095] The data provider can provide optimal data by referring to the user's past DIY history when providing DIY data. For example, the data provider can provide relevant data based on DIY projects the user has created in the past. For example, the data provider can suggest optimal materials and tools based on the user's past DIY history. For example, the data provider can provide improved data based on DIY data the user has used in the past. This allows the data provider to provide optimal DIY data based on the user's past DIY history. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input the user's past DIY history data into a generating AI and have the generating AI perform the task of providing optimal DIY data.

[0096] The data provider can adjust the data based on the user's current project when providing DIY data. For example, the provider can prioritize providing data related to the user's current project. For example, the provider can suggest the most suitable materials and tools based on the user's current project. For example, the provider can customize and provide the data the user needs for their current project. This allows the provider to provide optimal DIY data based on the current project. Some or all of the above processing in the data provider may be performed using AI, for example, or not using AI. For example, the data provider can input the user's current project data into a generating AI and have the generating AI perform the data adjustments.

[0097] The service provider can estimate the user's emotions and prioritize DIY data based on those emotions. For example, if the user is in a hurry, the service provider will prioritize providing the most important DIY data. If the user is relaxed, the service provider will provide detailed DIY data. If the user is excited, the service provider will provide visually appealing DIY data. This allows for the provision of optimal priority of DIY data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination of DIY data.

[0098] The data provider can provide optimal DIY data by considering the user's geographical location when providing DIY data. For example, if the user is in a specific region, the data provider can provide DIY data relevant to that region. For example, the data provider can suggest optimal materials and tools based on the user's geographical location. For example, if the user is on the move, the data provider can provide relevant DIY data based on the user's current location. This allows the data provider to provide optimal DIY data based on geographical location. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal DIY data.

[0099] The service provider can analyze the user's social media activity and suggest data when providing DIY data. For example, the service provider can suggest relevant DIY data based on information shared by the user on social media. For example, the service provider can suggest DIY data related to projects of interest based on the user's social media activity. For example, the service provider can suggest appropriate DIY data based on information about accounts the user follows on social media. This allows the service provider to provide optimal DIY data based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the DIY data suggestion.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The suggestion unit can estimate the user's emotions and adjust the order of suggestions based on those emotions. For example, if the user is stressed, it will prioritize suggesting the most relaxing furniture. If the user is excited, it will prioritize suggesting visually appealing furniture. If the user is in a hurry, it will prioritize suggesting the furniture that can be obtained most quickly. This allows the system to provide an optimal order of suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the order of suggestions based on those emotions.

[0102] The suggestion unit can analyze a user's past purchase history and suggest the most suitable furniture. For example, it can suggest furniture of a similar style and color based on the style and color of furniture the user has purchased in the past. It can suggest furniture of the same size based on the size of furniture the user has purchased in the past. It can suggest furniture of the same brand based on the brand of furniture the user has purchased in the past. In this way, the system can suggest the most suitable furniture based on past purchase history. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past purchase history data into a generating AI and have the generating AI perform the task of suggesting the most suitable furniture.

[0103] The suggestion unit can estimate the user's emotions and adjust the level of detail in suggestions based on those emotions. For example, if the user is relaxed, the generating AI will provide suggestions with detailed explanations. If the user is in a hurry, the generating AI will provide concise and to-the-point suggestions. If the user is excited, the generating AI will provide visually appealing suggestions. This allows for the provision of the optimal level of detail in suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generating AI and have the generating AI adjust the level of detail in suggestions based on those emotions.

[0104] The suggestion unit can adjust the content of its suggestions based on the user's current living situation. For example, if the user has just moved into a new house, it will prioritize suggesting basic furniture. If the user has children, it will prioritize suggesting furniture suitable for children. If the user has pets, it will prioritize suggesting furniture suitable for pets. This allows the suggestion unit to provide optimal suggestions based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's living situation data into a generating AI and have the generating AI adjust the content of the suggestions.

[0105] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the generating AI can create suggestions that include detailed explanations. If the user is in a hurry, the generating AI can create concise and to-the-point suggestions. If the user is excited, the generating AI can create visually appealing suggestions. This allows the system to provide the most appropriate way to present suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generating AI and have the generating AI adjust the way suggestions are presented based on those emotions.

[0106] The suggestion unit can propose the most suitable furniture considering the user's geographical location. For example, if the user lives in a cold region, it will suggest furniture made of warm materials. If the user lives in a humid region, it will suggest furniture that is resistant to humidity. If the user lives in an urban area, it will suggest compact and functional furniture. In this way, the optimal furniture can be suggested based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of suggesting the most suitable furniture.

[0107] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the generating AI will create a short, concise suggestion. If the user is relaxed, the generating AI will create a longer suggestion with detailed explanations. If the user is excited, the generating AI will create a visually stimulating suggestion. This allows the system to provide the optimal suggestion length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generating AI and have the generating AI adjust the length of the suggestions based on those emotions.

[0108] The suggestion unit can analyze a user's social media activity and suggest relevant furniture. For example, it can suggest relevant furniture based on information the user has shared on social media. It can suggest appropriate furniture based on information about accounts the user follows on social media. It can suggest relevant furniture based on projects the user has shown interest in on social media. This allows the unit to suggest the most suitable furniture based on social media activity. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant furniture.

[0109] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is in a hurry, it will prioritize suggesting the most important furniture. If the user is relaxed, it will prioritize suggesting detailed furniture. If the user is excited, it will prioritize suggesting visually appealing furniture. This allows for the provision of optimal suggestion priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform the emotion-based suggestion prioritization.

[0110] The suggestion unit can adjust the content of its suggestions based on the user's current project. For example, it can prioritize suggesting furniture related to the user's current project. It can customize and suggest furniture that the user needs for their current project. It can suggest the most suitable materials and tools based on the user's current project. This allows the suggestion unit to provide the most suitable suggestions based on the user's current project. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's current project data into a generating AI and have the generating AI adjust the content of the suggestions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives the size information of the furniture requested by the user. Size information can be entered, for example, by LiDAR distance measurement, manual input, voice input, or from an image. For example, the reception desk measures the dimensions of the room using LiDAR and inputs that data. The reception desk can also receive manual input from the user. Furthermore, the reception desk can receive input using voice input. For example, the user can voice-input "a shelf that is 2 meters wide and 1 meter high." For input from an image, for example, the user takes a picture of the room and the image is analyzed to obtain the dimensions. Step 2: The suggestion department uses a generation AI to propose furniture based on the size information entered by the reception department. The generation AI can, for example, screen furniture websites and propose furniture that matches the desired size. The generation AI can also, for example, propose furniture that suits the user's preferences and room size. Furthermore, the generation AI learns the user's personal information and preferences and makes multiple suggestions that match trends, age group, and hobbies. For example, the generation AI can propose the most suitable furniture based on the user's past purchase history and preferences. Step 3: The ordering department places custom orders for the furniture proposed by the proposal department. The ordering department, for example, partners with custom furniture manufacturers to enable users to order custom-made furniture. The ordering department, for example, takes the user's desired design and dimensions and places a custom order for the furniture based on that information. Step 4: The provider provides DIY data. The provider provides DIY data such as pre-measured boards, handles, and sliding door rails. The provider also provides, for example, blueprints and material lists for users to make their own furniture.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, proposal unit, order unit, and supply unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs the size information of the furniture they want. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it uses a generating AI to propose furniture based on the size information input by the reception unit. The order unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the user places a custom order for the furniture proposed by the proposal unit. The supply unit is implemented by, for example, the control unit 46A of the smart device 14, where it provides DIY data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, proposal unit, order unit, and supply unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs the size information of the furniture they want. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where it uses generating AI to propose furniture based on the size information input by the reception unit. The order unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the user places a custom order for the furniture proposed by the proposal unit. The supply unit is implemented, for example, by the control unit 46A of the smart glasses 214, where it provides DIY data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, proposal unit, order unit, and supply unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs the size information of the furniture they want. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it uses a generation AI to propose furniture based on the size information input by the reception unit. The order unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the user places a custom order for the furniture proposed by the proposal unit. The supply unit is implemented by, for example, the control unit 46A of the headset terminal 314, where it provides DIY data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, proposal unit, order unit, and supply unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs the size information of the furniture they want. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it uses a generating AI to propose furniture based on the size information input by the reception unit. The order unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the user places a custom order for the furniture proposed by the proposal unit. The supply unit is implemented by, for example, the control unit 46A of the robot 414, where it provides DIY data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A reception area where size information is entered, A proposal unit that proposes furniture based on the size information entered by the reception unit, The ordering department will take orders for custom-made furniture proposed by the aforementioned proposal department, Equipped with a section that provides data for DIY projects. A system characterized by the following features. (Note 2) The aforementioned reception unit is LiDAR rangefinder, manual input, voice input, or size information input from images. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Our AI-generated system screens furniture websites and suggests furniture in your desired size. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The AI ​​uses generation to learn the user's personal information and preferences, and then makes suggestions that match current trends, age group, and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned ordering section is, We partner with custom furniture makers to enable users to order custom-made furniture. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide pre-measured boards, handles, and sliding door rails for DIY projects. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Enter a photo of the place where you want to place the furniture, and the AI ​​will generate an image of what it would look like in that location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and proposes a method for inputting size information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system analyzes the user's past input history and automatically selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering size information, the input is filtered based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input size information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering size information, the system prioritizes suggesting the most relevant input method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When entering size information, the system suggests relevant input methods based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the furniture. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the type of furniture. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When submitting proposals, prioritize them based on the date and time of furniture submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of the furniture. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned ordering section is, It estimates the user's emotions and adjusts the ordering process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned ordering section is, When an order is placed, the system analyzes the user's past order history to select the optimal ordering method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned ordering section is, When placing an order, the ordering method will be adjusted based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned ordering section is, It estimates the user's emotions and determines order priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned ordering section is, When an order is placed, the system selects the optimal ordering method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned ordering section is, When an order is placed, we suggest ordering methods based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we provide DIY data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing DIY data, we provide the most suitable data based on the user's past DIY history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing DIY data, we adjust the data based on the user's current project. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and prioritizes DIY data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing DIY data, we provide the most suitable data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing DIY data, we suggest data based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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. A reception area where size information is entered, A proposal unit that proposes furniture based on the size information entered by the reception unit, The ordering department will take orders for custom-made furniture proposed by the aforementioned proposal department, Equipped with a section that provides data for DIY projects. A system characterized by the following features.

2. The aforementioned reception unit is LiDAR rangefinder, manual input, voice input, or size information input from images. The system according to feature 1.

3. The aforementioned proposal section is, Using AI-generated content, we screen furniture websites and suggest furniture in your desired size. The system according to feature 1.

4. The aforementioned proposal section is, The AI ​​uses generative methods to learn users' personal information and preferences, and then makes suggestions that match current trends, age groups, and hobbies. The system according to feature 1.

5. The aforementioned ordering section is, We partner with custom furniture makers to enable users to order custom-made furniture. The system according to feature 1.

6. The aforementioned supply unit is, We provide pre-measured boards, handles, and sliding door rails for DIY projects. The system according to feature 1.

7. The aforementioned proposal section is, Enter a photo of the place where you want to place the furniture, and the AI ​​will generate an image of what it would look like in that location. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and proposes a method for inputting size information based on the estimated user emotions. The system according to feature 1.

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