system

Abstract

The system according to this embodiment aims to automatically suggest room coordination based on the user's preferences and budget. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a display unit, an adjustment unit, a link provision unit, a management unit, and a learning unit. The reception unit receives information on the user's preferences, room use, budget, color scheme, style, and furniture preferences. The generation unit generates room coordination suggestions based on the information received by the reception unit. The display unit displays the suggestions generated by the generation unit as a visual preview. The adjustment unit collects feedback provided by the user on the suggestions displayed by the display unit and adjusts the suggestions. The link provision unit provides online shopping links based on the suggestions adjusted by the adjustment unit. The management unit tracks the user's project and manages its progress, budget, and purchase list. The learning unit collects user selections and feedback and improves the AI ​​model of the generation AI.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to automatically propose room coordination based on the user's preferences and budget, and there is room for improvement.

[0005]

Means for Solving the Problems

[0006] ​The system according to this embodiment comprises a reception unit, a generation unit, a display unit, an adjustment unit, a link provision unit, a management unit, and a learning unit. The reception unit receives information on the user's preferences, room use, budget, color scheme, style, and furniture preferences. The generation unit generates room coordination suggestions based on the information received by the reception unit. The display unit displays the suggestions generated by the generation unit as a visual preview. The adjustment unit collects feedback provided by the user on the suggestions displayed by the display unit and adjusts the suggestions. The link provision unit provides online shopping links based on the suggestions adjusted by the adjustment unit. The management unit tracks the user's project and manages its progress, budget, and purchase list. The learning unit collects user selections and feedback and improves the AI ​​model of the generation AI. [Effects of the Invention]

[0007] The system according to this embodiment can automatically suggest room coordination based on the user's preferences and budget. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. 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). <000008​​​​​​​​​​​​​​​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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.<000010a>

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is an interactive and automated system that coordinates a room to suit the user's sensibilities. The system begins with the user interacting with the system and providing information about their preferences, the room's purpose, budget, color scheme, style, furniture preferences, etc. This interaction takes place through natural communication. Next, the Generative AI generates room coordination suggestions based on the user's preferences. This includes furniture placement, lighting, wall coverings, artwork, accent items, etc. The user can view a visual preview of the suggested design through the system. They can visually understand the appearance of the room in real time through 3D models and images. The user can provide feedback on the suggestions and customize the room design. The Generative AI adjusts the suggestions to suit the user's preferences. Online shopping links are provided so that the user can purchase the design elements and furniture they like. This allows the user to easily purchase the necessary items. The system tracks the user's project and manages progress, budget, purchase list, etc. The system collects user selections and feedback and uses them to improve the Generative AI's AI model. This allows the Generative AI to make more appropriate suggestions over time. This allows the system to automatically and interactively coordinate a room to match the user's sensibilities.

[0029] The system according to this embodiment comprises a reception unit, a generation unit, a display unit, an adjustment unit, a link provision unit, a management unit, and a learning unit. The reception unit receives information such as the user's preferences, the room's purpose, budget, color scheme, style, and furniture preferences. For example, the user provides information such as "I want to coordinate my living room in a modern style." This information is input to the generation AI. The generation unit uses the generation AI to generate room coordination suggestions based on the information received by the reception unit. The generation AI suggests, for example, furniture placement, lighting, wall coverings, artwork, and accent items. For example, if the user inputs "I want to coordinate my living room in a modern style," the generation AI will suggest furniture, lighting, artwork, etc. that match the modern style. The display unit displays the suggestions generated by the generation unit as a visual preview. The user can visually understand the appearance of the room in real time through 3D models and images. For example, the user can check the suggested furniture placement in a 3D model and grasp the overall atmosphere of the room. The adjustment unit collects feedback provided by the user regarding the suggestions displayed by the display unit and adjusts the suggestions. For example, a user might provide feedback such as, "I want to change the color of this sofa." This feedback is input into the generative AI, which then adjusts its suggestions to match the user's preferences. The link provider provides online shopping links based on the suggestions adjusted by the adjustment unit. For example, if a user likes a suggested sofa, a link to purchase it is provided. This allows the user to easily purchase the necessary items. The management unit tracks the user's project and manages progress, budget, and purchase list. For example, it can track which items the user has purchased and how much budget remains. The learning unit collects user selections and feedback to improve the generative AI's AI model. This allows the generative AI to make more appropriate suggestions over time. For example, based on the feedback provided by the user, the generative AI can make future suggestions more tailored to the user's preferences.As a result, the system according to the embodiment can automatically suggest room coordination based on the user's preferences, make adjustments through visual previews and feedback, and provide online shopping links and project management.

[0030] The reception desk receives information such as the user's preferences, room purpose, budget, color scheme, style, and furniture preferences. For example, a user might provide information such as, "I want to decorate my living room in a modern style." This information is then input into the generating AI. Specifically, the information entered by the user is accepted in text or multiple-choice format, and the system analyzes it and converts it into the appropriate data format. For example, if a user enters, "I like furniture in natural colors," the system extracts keywords such as "natural colors" and "furniture" and passes them to the generating AI. Also, when a user enters a budget, the system provides the generating AI with data that takes price ranges into consideration in order to make the best suggestions within that range. Furthermore, the reception desk remembers information and feedback that the user has provided in the past and can refer to it for future use. This allows the user to have a consistent experience and enables the system to make more accurate suggestions. For example, if a user previously entered, "I like minimalist design," that information will be taken into consideration for future use. The reception desk is designed to be intuitively operable through the user interface, allowing users to easily enter information and make necessary adjustments. This allows the reception department to accurately understand user needs and provide appropriate data to the generating AI.

[0031] The generation unit uses a generation AI to generate room coordination suggestions based on information received by the reception unit. The generation AI suggests things like furniture placement, lighting, wall coverings, artwork, and accent items. Specifically, the generation AI learns from a large dataset and generates the optimal coordination that suits the user's preferences and style. For example, if a user inputs "I want to coordinate my living room in a modern style," the generation AI will suggest furniture, lighting, and artwork that match a modern style. The generation AI analyzes the user's input using natural language processing technology and extracts appropriate keywords. Furthermore, the generation AI uses image generation technology to generate visuals of the suggested coordination. This allows the user to visually confirm the suggested coordination. The generation unit simulates multiple scenarios to propose the optimal coordination, taking into account the user's preferences and budget. For example, it tries different furniture placements and color schemes to select the most suitable combination. The generation unit utilizes past user data and feedback to improve the accuracy of its suggestions. This allows the generation unit to provide coordination suggestions that best meet the user's needs and increase user satisfaction.

[0032] The display unit shows the suggestions generated by the generation unit as a visual preview. Users can visually understand the appearance of the room in real time through 3D models and images. Specifically, the display unit uses high-resolution 3D rendering technology to display the suggested coordination in real time. Users can rotate the 3D model and zoom in and out to check the overall atmosphere and details of the room. For example, users can check the suggested furniture arrangement in the 3D model and grasp the overall atmosphere of the room. The display unit also has a function to simulate different lighting conditions and times of day, so users can check the atmosphere of the room during the day and at night. Furthermore, the display unit also provides an interface for users to provide feedback on the suggestions. For example, a user can provide feedback such as "I want to change the color of this sofa." The display unit reflects the user's feedback in real time and sends it to the generation unit. This allows users to make necessary adjustments while visually checking the suggestions. The display unit provides an intuitive and easy-to-use interface to improve the user experience. In this way, the display unit plays an important role in enabling users to visually understand the suggestions and provide feedback.

[0033] The adjustment unit collects feedback provided by the user regarding the suggestions displayed by the display unit and adjusts the suggestions accordingly. Specifically, the user provides feedback such as, "I want to change the color of this sofa." This feedback is input into the generating AI, which adjusts the suggestions to match the user's preferences. The adjustment unit analyzes the user's feedback and provides an interface for giving appropriate instructions to the generating AI. For example, if the user inputs, "I want brighter colored curtains," the adjustment unit analyzes this information and instructs the generating AI to "suggest brighter colored curtains." The generating AI generates a new suggestion based on this instruction and sends it to the display unit. The adjustment unit can reflect user feedback in real time and quickly adjust suggestions. This allows users to receive suggestions that match their preferences. The adjustment unit can also record user feedback and use it for future suggestions. This allows the system to learn user preferences over time and make more appropriate suggestions. The adjustment unit provides an intuitive and easy-to-use interface to improve the user experience. In this way, the adjustment unit plays a crucial role in collecting user feedback and adjusting suggestions.

[0034] The link provider unit provides online shopping links based on suggestions adjusted by the adjustment unit. Specifically, if a user likes a suggested sofa, a link to purchase that sofa is provided. The link provider unit collects information on online shopping sites related to the suggested items and provides the user with the best purchase options. For example, it compares prices and availability from multiple online shopping sites and provides the user with the most suitable link. The link provider unit provides an intuitive and user-friendly interface so that users can easily purchase the items they need. For example, when a user clicks on a suggested item, a link that takes them directly to the purchase page is displayed. The link provider unit can also record the user's purchase history and preferences and use this information for future suggestions. This allows the system to learn the user's preferences over time and provide more appropriate purchase options. The link provider unit provides an intuitive and user-friendly interface to improve the user experience. In this way, the link provider unit plays a crucial role in making it easy for users to purchase suggested items.

[0035] The management department tracks user projects and manages progress, budgets, and purchase lists. Specifically, it can track which items users have purchased and how much budget remains. The management department provides an intuitive and user-friendly interface so that users can see project progress at a glance. For example, users can check current progress, budget balance, and purchase lists through a dashboard. The management department provides tools to help users efficiently manage projects. For example, it has a function to automatically generate purchase lists and list necessary items, and a function to issue alerts to ensure that budgets are not exceeded. Furthermore, the management department securely stores user project data and has a function to back it up as needed. This allows users to proceed with projects with peace of mind. The management department provides an intuitive and user-friendly interface to improve the user experience. In this way, the management department plays a crucial role in enabling users to efficiently manage projects and understand progress and budgets.

[0036] The learning unit collects user selections and feedback to improve the generative AI's AI model. Specifically, based on user feedback, the generative AI can tailor future suggestions to better suit the user's preferences. The learning unit analyzes user selections and feedback and uses them as training data for the generative AI. For example, if a user provides feedback such as "I don't like this suggestion," the learning unit analyzes this information and instructs the generative AI to "avoid this style." The generative AI adjusts its next suggestion based on this instruction. The learning unit regularly updates the generative AI's training data to improve the accuracy of suggestions. This allows the generative AI to make more appropriate suggestions over time. Furthermore, the learning unit can record user selections and feedback and use them for future suggestions. This allows the system to learn user preferences over time and make more appropriate suggestions. The learning unit can also incorporate the latest technologies and algorithms to improve the performance of the generative AI. In this way, the learning unit plays a crucial role in improving the accuracy of the generative AI's suggestions and enhancing the user experience.

[0037] The generation unit can suggest furniture placement, lighting, wall coverings, artwork, and accent items. For example, it can suggest furniture placement, such as the placement of a sofa in a living room or the location of a dining table. It can also suggest lighting placement, such as the location of ceiling lights or floor lamps. It can also suggest wall coverings, such as wallpaper designs or paint colors. It can also suggest artwork placement, such as the placement of paintings or photographs. Furthermore, it can suggest accent item placement, such as the placement of cushions or rugs. In this way, the generation unit can provide users with detailed design proposals by offering specific coordination suggestions. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can make suggestions using an AI model that generates optimal coordination suggestions based on the user's preferences and the use of the room.

[0038] The display unit can display the exterior of a room in real time through a 3D model or an image. For example, the display unit can display the exterior of a room through a 3D model. For example, the user can check the suggested furniture arrangement in the 3D model and grasp the overall atmosphere of the room. The display unit can also display the exterior of a room through an image. For example, the user can check a visual preview of the suggested design in an image and visually understand the exterior of the room. This allows the display unit to visually confirm the suggested design to the user. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that generates an optimal visual preview based on the user's preferences and the room's purpose.

[0039] The adjustment unit can adjust suggestions based on user feedback. For example, the adjustment unit adjusts suggestions based on feedback provided by the user. For example, the user provides feedback such as, "I want to change the color of this sofa." This feedback is input to the generating AI, which adjusts the suggestions to match the user's preferences. The adjustment unit can also collect user feedback and customize suggestions. For example, the user provides feedback such as, "I want to change the position of this light." This feedback is input to the generating AI, which adjusts the suggestions to match the user's preferences. In this way, the adjustment unit can customize suggestions to reflect user feedback. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can make suggestions using an AI model that adjusts suggestions based on user feedback.

[0040] The link provider can provide online shopping links for users to purchase design elements or furniture they like. For example, if a user likes a suggested sofa, the link provider can provide a link to purchase that sofa. This allows the user to easily purchase the items they need. The link provider can also provide links for users to purchase design elements or furniture they like. For example, if a user likes a suggested light fixture, a link to purchase that light fixture can be provided. This allows the user to easily purchase the items they need. In this way, the link provider can enable users to easily purchase the items they need. Some or all of the above processing in the link provider may be performed using AI, for example, or not using AI. For example, the link provider can provide links using an AI model that provides optimal online shopping links based on the user's preferences and the use of the room.

[0041] The management department can manage the progress, budget, and purchase list of users' projects. For example, the management department can manage the progress of users' projects, such as which items users have purchased and how much budget remains. The management department can also manage users' budgets, for example, to ensure that projects proceed within the budget set by the user. The management department can also manage users' purchase lists, for example, by listing and managing items that users have purchased or plan to purchase. This allows the management department to streamline the management of users' projects. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can use an AI model to manage the progress, budget, and purchase list of users' projects.

[0042] The learning unit can collect user selections and feedback to improve the AI ​​model of the generative AI. For example, the learning unit can improve the AI ​​model of the generative AI based on feedback provided by the user. For instance, a user might provide feedback such as, "I want to change the color of this sofa." This feedback is input into the generative AI, allowing it to make future suggestions more tailored to the user's preferences. The learning unit can also collect user selections to improve the AI ​​model of the generative AI. For example, if a user selects a suggested design element or piece of furniture, the learning unit collects this selection information and improves the AI ​​model of the generative AI. This allows the learning unit to improve the accuracy of the generative AI's suggestions. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can learn using an AI model that collects user selections and feedback and improves the AI ​​model of the generative AI.

[0043] The reception desk can analyze the user's past interaction history and select the optimal method for receiving information. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also provide predictive input based on information the user has previously entered. Furthermore, the reception desk can predict and suggest information to be used during specific time periods based on the user's past interaction history. This allows the reception desk to provide the optimal method for receiving information based on the user's past interaction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can receive information using an AI model that analyzes the user's past interaction history and selects the optimal method for receiving information.

[0044] The reception unit can filter information upon receipt based on the user's current living situation and areas of interest. For example, the reception unit can prioritize receiving relevant information based on the user's current living situation. It can also filter and receive relevant information based on the user's areas of interest. Furthermore, it can exclude unnecessary information based on the user's living situation and areas of interest. This allows the reception unit to receive relevant information based on the user's living situation 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 can receive information using an AI model that filters information based on the user's living situation and areas of interest.

[0045] The reception unit can prioritize receiving information that is highly relevant based on the user's geographical location. For example, the reception unit can prioritize receiving relevant information based on the user's current location. It can also prioritize receiving information related to a specific region based on the user's geographical location. Furthermore, the reception unit can filter out unnecessary information, taking the user's location into consideration. This allows the reception unit to provide highly relevant information based on the user's 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 receive information using an AI model that filters information while considering the user's geographical location.

[0046] The reception unit can analyze the user's social media activity and receive relevant information upon receiving it. For example, the reception unit can prioritize receiving relevant information based on the user's social media activity. The reception unit can also filter and receive information based on the user's areas of interest on social media. Furthermore, the reception unit can analyze the user's social media activity and eliminate unnecessary information. This allows the reception unit to provide relevant information based on the user's 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 receive information using an AI model that analyzes the user's social media activity and filters relevant information.

[0047] The generation unit can adjust the level of detail in the coordination proposals based on the room's purpose and budget. For example, the generation unit can adjust the level of detail of necessary furniture and items based on the room's purpose. It can also adjust the level of detail of the proposals based on the budget to reduce costs. Furthermore, the generation unit can generate optimal proposals considering both the room's purpose and budget. This allows the generation unit to provide detailed coordination proposals tailored to the room's purpose and budget. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make proposals using an AI model that adjusts the level of detail of proposals based on the room's purpose and budget.

[0048] The generation unit can apply different generation algorithms depending on the room style and color scheme when generating coordination suggestions. For example, the generation unit can select the optimal generation algorithm based on the room style. It can also apply different generation algorithms depending on the color scheme. Furthermore, the generation unit can generate optimal suggestions considering both the room style and color scheme. This allows the generation unit to provide optimal coordination suggestions tailored to the room style and color scheme. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make suggestions using an AI model that applies different generation algorithms depending on the room style and color scheme.

[0049] The generation unit can determine the priority of coordination suggestions based on the frequency of room use when generating suggestions. For example, the generation unit can prioritize suggesting important items based on the frequency of room use. The generation unit can also provide cost-effective suggestions for rooms that are used infrequently. Furthermore, the generation unit can prioritize detailed suggestions for rooms that are used frequently. In this way, the generation unit can provide high-priority suggestions according to the frequency of room use. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make suggestions using an AI model that determines the priority of suggestions based on the frequency of room use.

[0050] The generation unit can adjust the order of coordination suggestions based on the relationships between rooms when generating them. For example, the generation unit can prioritize important suggestions based on the relationships between rooms. It can also postpone suggestions for rooms with low relevance. Furthermore, the generation unit can determine the optimal order of suggestions by considering the relationships between rooms. This allows the generation unit to provide the optimal order of suggestions according to the relationships between rooms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make suggestions using an AI model that adjusts the order of suggestions based on the relationships between rooms.

[0051] The display unit can select the optimal display method by referring to the user's past design history when displaying a visual preview. For example, the display unit can select the optimal display method based on the user's past design history. The display unit can also prioritize displaying design styles that the user has previously preferred. Furthermore, the display unit can analyze the user's past design history and select the most visually appealing display method. This allows the display unit to provide an optimal visual preview based on the user's past design history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that selects the optimal display method by referring to the user's past design history.

[0052] The display unit can customize the displayed content based on the size and shape of the room when displaying a visual preview. For example, the display unit can customize the optimal displayed content based on the size of the room. The display unit can also adjust the displayed content according to the shape of the room. Furthermore, the display unit can provide an optimal visual preview considering the size and shape of the room. In this way, the display unit can provide an optimal visual preview according to the size and shape of the room. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that customizes the displayed content based on the size and shape of the room.

[0053] The display unit can select the optimal display method when displaying a visual preview, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, the display unit can provide a concise and highly visible display method if the user is using a smartwatch. This allows the display unit to provide the optimal visual preview based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that selects the optimal display method considering the user's device information.

[0054] The display unit can analyze the user's social media activity and display relevant previews when displaying visual previews. For example, the display unit can display relevant visual previews based on the user's social media activity. The display unit can also customize visual previews based on the user's areas of interest on social media. Furthermore, the display unit can analyze the user's social media activity and eliminate unnecessary visual previews. This allows the display unit to provide relevant visual previews based on the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that analyzes the user's social media activity and displays relevant previews.

[0055] The adjustment unit can select the optimal adjustment method when adjusting a proposal by referring to the user's past feedback history. For example, the adjustment unit selects the optimal adjustment method based on the user's past feedback history. The adjustment unit can also prioritize providing adjustment methods that the user has preferred in the past. Furthermore, the adjustment unit can analyze the user's past feedback history and select the most effective adjustment method. This allows the adjustment unit to provide the optimal proposal adjustment method based on the user's past feedback history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can perform adjustments using an AI model that selects the optimal adjustment method by referring to the user's past feedback history.

[0056] The adjustment unit can customize the adjustments made to the proposal based on the room's intended use and budget. For example, the adjustment unit can provide the optimal adjustments based on the room's intended use. The adjustment unit can also customize the adjustments based on the budget to reduce costs. Furthermore, the adjustment unit can provide the optimal adjustments considering both the room's intended use and budget. This allows the adjustment unit to provide the optimal proposal adjustments tailored to the room's intended use and budget. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can perform adjustments using an AI model that customizes the adjustments based on the room's intended use and budget.

[0057] The adjustment unit can select the optimal adjustment method when adjusting proposals, taking into account the user's geographical location information. For example, the adjustment unit selects the optimal adjustment method based on the user's geographical location information. The adjustment unit can also customize the adjustment content by taking into account region-related information. Furthermore, the adjustment unit can eliminate unnecessary adjustments by taking into account the user's location information. In this way, the adjustment unit can provide the optimal proposal adjustment method based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can perform adjustments using an AI model that selects the optimal adjustment method by taking into account the user's geographical location information.

[0058] The adjustment unit can analyze the user's social media activity and propose adjustments when adjusting proposals. For example, the adjustment unit proposes relevant adjustments based on the user's social media activity. The adjustment unit can also customize the adjustments based on the user's areas of interest on social media. Furthermore, the adjustment unit can analyze the user's social media activity and eliminate unnecessary adjustments. This allows the adjustment unit to provide optimal adjustments to proposals based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can perform adjustments using an AI model that analyzes the user's social media activity and proposes adjustments.

[0059] The link provider can select the most suitable link when providing online shopping links by referring to the user's past purchase history. For example, the link provider can select the most suitable link based on the user's past purchase history. The link provider can also provide links related to items the user has previously purchased. Furthermore, the link provider can analyze the user's past purchase history and select the most relevant link. This allows the link provider to provide the most suitable online shopping links based on the user's past purchase history. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that selects the most suitable link by referring to the user's past purchase history.

[0060] The link provider can customize the content of online shopping links based on the user's budget and preferences. For example, the link provider can provide the most suitable link content based on the user's budget. It can also customize the link content according to the user's preferences. Furthermore, it can provide the most suitable link content considering both the user's budget and preferences. This allows the link provider to provide the most suitable online shopping links according to the user's budget and preferences. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that customizes the link content based on the user's budget and preferences.

[0061] The link provider can select the most suitable link when providing online shopping links, taking into account the user's geographical location. For example, the link provider can select the most suitable link based on the user's geographical location. The link provider can also customize the link content by considering region-related information. Furthermore, the link provider can eliminate unnecessary links by considering the user's location. This allows the link provider to provide the most suitable online shopping links based on the user's geographical location. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that selects the most suitable link by considering the user's geographical location.

[0062] The link provider can provide relevant links by analyzing the user's social media activity when providing online shopping links. For example, the link provider can provide relevant links based on the user's social media activity. The link provider can also customize the content of links based on the user's areas of interest on social media. Furthermore, the link provider can analyze the user's social media activity and eliminate unnecessary links. This allows the link provider to provide optimal online shopping links based on the user's social media activity. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that analyzes the user's social media activity and provides relevant links.

[0063] The management department can select the optimal management method by referring to the user's past project history during project management. For example, the management department selects the optimal management method based on the user's past project history. The management department can also prioritize providing management methods that the user has preferred in the past. Furthermore, the management department can analyze the user's past project history and select the most effective management method. This allows the management department to provide the optimal project management method based on the user's past project history. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can perform management using an AI model that selects the optimal management method by referring to the user's past project history.

[0064] The management department can customize project management based on the user's budget and progress. For example, the management department can provide optimal management based on the user's budget. It can also customize management according to the user's progress. Furthermore, it can provide optimal management considering both the user's budget and progress. This allows the management department to provide optimal project management tailored to the user's budget and progress. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can perform management using an AI model that customizes management based on the user's budget and progress.

[0065] The management department can select the optimal management method when managing a project, taking into account the user's geographical location information. For example, the management department can select the optimal management method based on the user's geographical location information. The management department can also customize the management content by taking into account region-related information. Furthermore, the management department can eliminate unnecessary management items by taking into account the user's location information. In this way, the management department can provide the optimal project management method based on the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can perform management using an AI model that selects the optimal management method by taking into account the user's geographical location information.

[0066] The management department can analyze users' social media activity and propose management strategies during project management. For example, the management department can propose relevant management strategies based on users' social media activity. Furthermore, the management department can customize management strategies based on users' areas of interest on social media. The management department can also analyze users' social media activity and eliminate unnecessary management items. This allows the management department to provide optimal project management strategies based on users' social media activity. Some or all of the above processes performed by the management department may be carried out using AI, for example, or without AI. For instance, the management department can perform management using an AI model that analyzes users' social media activity and proposes management strategies.

[0067] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the algorithm parameters. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit can provide the optimal learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that optimizes the learning algorithm by referring to past learning data.

[0068] The learning unit can customize the learning content by analyzing the user's feedback history during the learning process. For example, the learning unit customizes the learning content based on the user's feedback history. The learning unit can also optimize the learning content by referring to feedback previously provided by the user. Furthermore, the learning unit can improve the accuracy of the learning content by analyzing the user's feedback history. This allows the learning unit to provide optimal learning content based on the user's feedback history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that analyzes the user's feedback history to customize the learning content.

[0069] The learning unit can weight the training data while considering the user's geographical location information during training. For example, the learning unit weights the training data based on the user's geographical location information. The learning unit can also adjust the weighting of the training data by considering region-related information. Furthermore, the learning unit can optimize the weighting of the training data by considering the user's location information. This allows the learning unit to provide optimal training data weighting based on the user's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that weights the training data while considering the user's geographical location information.

[0070] The learning unit can optimize learning content by analyzing the user's social media activity during the learning process. For example, the learning unit optimizes learning content based on the user's social media activity. The learning unit can also customize learning content based on the user's areas of interest on social media. Furthermore, the learning unit can analyze the user's social media activity and eliminate unnecessary learning content. This allows the learning unit to provide optimal learning content based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that analyzes the user's social media activity and optimizes learning content.

[0071] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0072] The reception desk can analyze the user's past interaction history and select the optimal method for receiving information. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also provide predictive input based on information the user has previously entered. Furthermore, the reception desk can predict and suggest information to be used during specific time periods based on the user's past interaction history. This allows the reception desk to provide the optimal method for receiving information based on the user's past interaction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can receive information using an AI model that analyzes the user's past interaction history and selects the optimal method for receiving information.

[0073] The generation unit can determine the priority of coordination suggestions based on the frequency of room use when generating suggestions. For example, it can prioritize suggesting important items based on the frequency of room use. The generation unit can also provide cost-effective suggestions for rooms that are used infrequently. Furthermore, the generation unit can prioritize detailed suggestions for rooms that are used frequently. In this way, the generation unit can provide high-priority suggestions according to the frequency of room use. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make suggestions using an AI model that determines the priority of suggestions based on the frequency of room use.

[0074] The display unit can select the optimal display method when displaying a visual preview, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal visual preview based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that selects the optimal display method considering the user's device information.

[0075] The adjustment unit can select the optimal adjustment method when adjusting a proposal by referring to the user's past feedback history. For example, it can select the optimal adjustment method based on the user's past feedback history. The adjustment unit can also prioritize providing adjustment methods that the user has preferred in the past. Furthermore, the adjustment unit can analyze the user's past feedback history and select the most effective adjustment method. This allows the adjustment unit to provide the optimal proposal adjustment method based on the user's past feedback history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can perform adjustments using an AI model that selects the optimal adjustment method by referring to the user's past feedback history.

[0076] The link provider can select the most suitable link when providing online shopping links by referring to the user's past purchase history. For example, it can select the most suitable link based on the user's past purchase history. The link provider can also provide links related to items the user has previously purchased. Furthermore, the link provider can analyze the user's past purchase history and select the most relevant link. This allows the link provider to provide the most suitable online shopping links based on the user's past purchase history. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that selects the most suitable link by referring to the user's past purchase history.

[0077] The following briefly describes the processing flow for example form 1.

[0078] Step 1: The reception desk receives information such as the user's preferences, the room's purpose, budget, color scheme, style, and furniture preferences. For example, the user might provide information such as, "I want to decorate my living room in a modern style." This information is then input into the generating AI. Step 2: The generation unit uses a generation AI to generate room coordination suggestions based on the information received by the reception unit. The generation AI suggests things like furniture placement, lighting, wall coverings, artwork, and accent items. For example, if a user inputs "I want to coordinate my living room in a modern style," the generation AI will suggest furniture, lighting, artwork, etc. that would suit a modern style. Step 3: The display unit shows the suggestions generated by the generation unit as a visual preview. The user can visually understand the appearance of the room in real time through 3D models and images. For example, the user can check the suggested furniture arrangement in the 3D model and grasp the overall atmosphere of the room. Step 4: The adjustment unit collects feedback provided by the user regarding the suggestions displayed by the display unit and adjusts the suggestions. For example, the user might provide feedback such as, "I want to change the color of this sofa." This feedback is input into the generating AI, which then adjusts the suggestions to match the user's preferences. Step 5: The link provider provides online shopping links based on the suggestions adjusted by the adjustment unit. For example, if the user likes a suggested sofa, a link to purchase that sofa is provided. This allows the user to easily purchase the item they need. Step 6: The management team tracks user projects and manages progress, budgets, purchase lists, etc. For example, they can track which items users have purchased and how much budget remains. Step 7: The learning unit collects user selections and feedback to improve the generative AI's AI model. This allows the generative AI to make more appropriate suggestions over time. For example, based on user feedback, the generative AI can tailor future suggestions to better suit the user's preferences.

[0079] (Example of form 2) The system according to an embodiment of the present invention is an interactive and automated system that coordinates a room to suit the user's sensibilities. The system begins with the user interacting with the system and providing information about their preferences, the room's purpose, budget, color scheme, style, furniture preferences, etc. This interaction takes place through natural communication. Next, the Generative AI generates room coordination suggestions based on the user's preferences. This includes furniture placement, lighting, wall coverings, artwork, accent items, etc. The user can view a visual preview of the suggested design through the system. They can visually understand the appearance of the room in real time through 3D models and images. The user can provide feedback on the suggestions and customize the room design. The Generative AI adjusts the suggestions to suit the user's preferences. Online shopping links are provided so that the user can purchase the design elements and furniture they like. This allows the user to easily purchase the necessary items. The system tracks the user's project and manages progress, budget, purchase list, etc. The system collects user selections and feedback and uses them to improve the Generative AI's AI model. This allows the Generative AI to make more appropriate suggestions over time. This allows the system to automatically and interactively coordinate a room to match the user's sensibilities.

[0080] The system according to this embodiment comprises a reception unit, a generation unit, a display unit, an adjustment unit, a link provision unit, a management unit, and a learning unit. The reception unit receives information such as the user's preferences, the room's purpose, budget, color scheme, style, and furniture preferences. For example, the user provides information such as "I want to coordinate my living room in a modern style." This information is input to the generation AI. The generation unit uses the generation AI to generate room coordination suggestions based on the information received by the reception unit. The generation AI suggests, for example, furniture placement, lighting, wall coverings, artwork, and accent items. For example, if the user inputs "I want to coordinate my living room in a modern style," the generation AI will suggest furniture, lighting, artwork, etc. that match the modern style. The display unit displays the suggestions generated by the generation unit as a visual preview. The user can visually understand the appearance of the room in real time through 3D models and images. For example, the user can check the suggested furniture placement in a 3D model and grasp the overall atmosphere of the room. The adjustment unit collects feedback provided by the user regarding the suggestions displayed by the display unit and adjusts the suggestions. For example, a user might provide feedback such as, "I want to change the color of this sofa." This feedback is input into the generative AI, which then adjusts its suggestions to match the user's preferences. The link provider provides online shopping links based on the suggestions adjusted by the adjustment unit. For example, if a user likes a suggested sofa, a link to purchase it is provided. This allows the user to easily purchase the necessary items. The management unit tracks the user's project and manages progress, budget, and purchase list. For example, it can track which items the user has purchased and how much budget remains. The learning unit collects user selections and feedback to improve the generative AI's AI model. This allows the generative AI to make more appropriate suggestions over time. For example, based on the feedback provided by the user, the generative AI can make future suggestions more tailored to the user's preferences.As a result, the system according to the embodiment can automatically suggest room coordination based on the user's preferences, make adjustments through visual previews and feedback, and provide online shopping links and project management.

[0081] The reception desk receives information such as the user's preferences, room purpose, budget, color scheme, style, and furniture preferences. For example, a user might provide information such as, "I want to decorate my living room in a modern style." This information is then input into the generating AI. Specifically, the information entered by the user is accepted in text or multiple-choice format, and the system analyzes it and converts it into the appropriate data format. For example, if a user enters, "I like furniture in natural colors," the system extracts keywords such as "natural colors" and "furniture" and passes them to the generating AI. Also, when a user enters a budget, the system provides the generating AI with data that takes price ranges into consideration in order to make the best suggestions within that range. Furthermore, the reception desk remembers information and feedback that the user has provided in the past and can refer to it for future use. This allows the user to have a consistent experience and enables the system to make more accurate suggestions. For example, if a user previously entered, "I like minimalist design," that information will be taken into consideration for future use. The reception desk is designed to be intuitively operable through the user interface, allowing users to easily enter information and make necessary adjustments. This allows the reception department to accurately understand user needs and provide appropriate data to the generating AI.

[0082] The generation unit uses a generation AI to generate room coordination suggestions based on information received by the reception unit. The generation AI suggests things like furniture placement, lighting, wall coverings, artwork, and accent items. Specifically, the generation AI learns from a large dataset and generates the optimal coordination that suits the user's preferences and style. For example, if a user inputs "I want to coordinate my living room in a modern style," the generation AI will suggest furniture, lighting, and artwork that match a modern style. The generation AI analyzes the user's input using natural language processing technology and extracts appropriate keywords. Furthermore, the generation AI uses image generation technology to generate visuals of the suggested coordination. This allows the user to visually confirm the suggested coordination. The generation unit simulates multiple scenarios to propose the optimal coordination, taking into account the user's preferences and budget. For example, it tries different furniture placements and color schemes to select the most suitable combination. The generation unit utilizes past user data and feedback to improve the accuracy of its suggestions. This allows the generation unit to provide coordination suggestions that best meet the user's needs and increase user satisfaction.

[0083] The display unit shows the suggestions generated by the generation unit as a visual preview. Users can visually understand the appearance of the room in real time through 3D models and images. Specifically, the display unit uses high-resolution 3D rendering technology to display the suggested coordination in real time. Users can rotate the 3D model and zoom in and out to check the overall atmosphere and details of the room. For example, users can check the suggested furniture arrangement in the 3D model and grasp the overall atmosphere of the room. The display unit also has a function to simulate different lighting conditions and times of day, so users can check the atmosphere of the room during the day and at night. Furthermore, the display unit also provides an interface for users to provide feedback on the suggestions. For example, a user can provide feedback such as "I want to change the color of this sofa." The display unit reflects the user's feedback in real time and sends it to the generation unit. This allows users to make necessary adjustments while visually checking the suggestions. The display unit provides an intuitive and easy-to-use interface to improve the user experience. In this way, the display unit plays an important role in enabling users to visually understand the suggestions and provide feedback.

[0084] The adjustment unit collects feedback provided by the user regarding the suggestions displayed by the display unit and adjusts the suggestions accordingly. Specifically, the user provides feedback such as, "I want to change the color of this sofa." This feedback is input into the generating AI, which adjusts the suggestions to match the user's preferences. The adjustment unit analyzes the user's feedback and provides an interface for giving appropriate instructions to the generating AI. For example, if the user inputs, "I want brighter colored curtains," the adjustment unit analyzes this information and instructs the generating AI to "suggest brighter colored curtains." The generating AI generates a new suggestion based on this instruction and sends it to the display unit. The adjustment unit can reflect user feedback in real time and quickly adjust suggestions. This allows users to receive suggestions that match their preferences. The adjustment unit can also record user feedback and use it for future suggestions. This allows the system to learn user preferences over time and make more appropriate suggestions. The adjustment unit provides an intuitive and easy-to-use interface to improve the user experience. In this way, the adjustment unit plays a crucial role in collecting user feedback and adjusting suggestions.

[0085] The link provider unit provides online shopping links based on suggestions adjusted by the adjustment unit. Specifically, if a user likes a suggested sofa, a link to purchase that sofa is provided. The link provider unit collects information on online shopping sites related to the suggested items and provides the user with the best purchase options. For example, it compares prices and availability from multiple online shopping sites and provides the user with the most suitable link. The link provider unit provides an intuitive and user-friendly interface so that users can easily purchase the items they need. For example, when a user clicks on a suggested item, a link that takes them directly to the purchase page is displayed. The link provider unit can also record the user's purchase history and preferences and use this information for future suggestions. This allows the system to learn the user's preferences over time and provide more appropriate purchase options. The link provider unit provides an intuitive and user-friendly interface to improve the user experience. In this way, the link provider unit plays a crucial role in making it easy for users to purchase suggested items.

[0086] The management department tracks user projects and manages progress, budgets, and purchase lists. Specifically, it can track which items users have purchased and how much budget remains. The management department provides an intuitive and user-friendly interface so that users can see project progress at a glance. For example, users can check current progress, budget balance, and purchase lists through a dashboard. The management department provides tools to help users efficiently manage projects. For example, it has a function to automatically generate purchase lists and list necessary items, and a function to issue alerts to ensure that budgets are not exceeded. Furthermore, the management department securely stores user project data and has a function to back it up as needed. This allows users to proceed with projects with peace of mind. The management department provides an intuitive and user-friendly interface to improve the user experience. In this way, the management department plays a crucial role in enabling users to efficiently manage projects and understand progress and budgets.

[0087] The learning unit collects user selections and feedback to improve the generative AI's AI model. Specifically, based on user feedback, the generative AI can tailor future suggestions to better suit the user's preferences. The learning unit analyzes user selections and feedback and uses them as training data for the generative AI. For example, if a user provides feedback such as "I don't like this suggestion," the learning unit analyzes this information and instructs the generative AI to "avoid this style." The generative AI adjusts its next suggestion based on this instruction. The learning unit regularly updates the generative AI's training data to improve the accuracy of suggestions. This allows the generative AI to make more appropriate suggestions over time. Furthermore, the learning unit can record user selections and feedback and use them for future suggestions. This allows the system to learn user preferences over time and make more appropriate suggestions. The learning unit can also incorporate the latest technologies and algorithms to improve the performance of the generative AI. In this way, the learning unit plays a crucial role in improving the accuracy of the generative AI's suggestions and enhancing the user experience.

[0088] The generation unit can suggest furniture placement, lighting, wall coverings, artwork, and accent items. For example, it can suggest furniture placement, such as the placement of a sofa in a living room or the location of a dining table. It can also suggest lighting placement, such as the location of ceiling lights or floor lamps. It can also suggest wall coverings, such as wallpaper designs or paint colors. It can also suggest artwork placement, such as the placement of paintings or photographs. Furthermore, it can suggest accent item placement, such as the placement of cushions or rugs. In this way, the generation unit can provide users with detailed design proposals by offering specific coordination suggestions. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can make suggestions using an AI model that generates optimal coordination suggestions based on the user's preferences and the use of the room.

[0089] The display unit can display the exterior of a room in real time through a 3D model or an image. For example, the display unit can display the exterior of a room through a 3D model. For example, the user can check the suggested furniture arrangement in the 3D model and grasp the overall atmosphere of the room. The display unit can also display the exterior of a room through an image. For example, the user can check a visual preview of the suggested design in an image and visually understand the exterior of the room. This allows the display unit to visually confirm the suggested design to the user. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that generates an optimal visual preview based on the user's preferences and the room's purpose.

[0090] The adjustment unit can adjust suggestions based on user feedback. For example, the adjustment unit adjusts suggestions based on feedback provided by the user. For example, the user provides feedback such as, "I want to change the color of this sofa." This feedback is input to the generating AI, which adjusts the suggestions to match the user's preferences. The adjustment unit can also collect user feedback and customize suggestions. For example, the user provides feedback such as, "I want to change the position of this light." This feedback is input to the generating AI, which adjusts the suggestions to match the user's preferences. In this way, the adjustment unit can customize suggestions to reflect user feedback. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can make suggestions using an AI model that adjusts suggestions based on user feedback.

[0091] The link provider can provide online shopping links for users to purchase design elements or furniture they like. For example, if a user likes a suggested sofa, the link provider can provide a link to purchase that sofa. This allows the user to easily purchase the items they need. The link provider can also provide links for users to purchase design elements or furniture they like. For example, if a user likes a suggested light fixture, a link to purchase that light fixture can be provided. This allows the user to easily purchase the items they need. In this way, the link provider can enable users to easily purchase the items they need. Some or all of the above processing in the link provider may be performed using AI, for example, or not using AI. For example, the link provider can provide links using an AI model that provides optimal online shopping links based on the user's preferences and the use of the room.

[0092] The management department can manage the progress, budget, and purchase list of users' projects. For example, the management department can manage the progress of users' projects, such as which items users have purchased and how much budget remains. The management department can also manage users' budgets, for example, to ensure that projects proceed within the budget set by the user. The management department can also manage users' purchase lists, for example, by listing and managing items that users have purchased or plan to purchase. This allows the management department to streamline the management of users' projects. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can use an AI model to manage the progress, budget, and purchase list of users' projects.

[0093] The learning unit can collect user selections and feedback to improve the AI ​​model of the generative AI. For example, the learning unit can improve the AI ​​model of the generative AI based on feedback provided by the user. For instance, a user might provide feedback such as, "I want to change the color of this sofa." This feedback is input into the generative AI, allowing it to make future suggestions more tailored to the user's preferences. The learning unit can also collect user selections to improve the AI ​​model of the generative AI. For example, if a user selects a suggested design element or piece of furniture, the learning unit collects this selection information and improves the AI ​​model of the generative AI. This allows the learning unit to improve the accuracy of the generative AI's suggestions. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can learn using an AI model that collects user selections and feedback and improves the AI ​​model of the generative AI.

[0094] The reception desk can estimate the user's emotions and adjust the information reception method 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. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. In this way, the reception desk can provide an information reception method that is tailored 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 reception desk may be performed using AI or not. For example, the reception desk can receive information using an AI model that estimates the user's emotions and adjusts the information reception method based on the estimated emotions.

[0095] The reception desk can analyze the user's past interaction history and select the optimal method for receiving information. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also provide predictive input based on information the user has previously entered. Furthermore, the reception desk can predict and suggest information to be used during specific time periods based on the user's past interaction history. This allows the reception desk to provide the optimal method for receiving information based on the user's past interaction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can receive information using an AI model that analyzes the user's past interaction history and selects the optimal method for receiving information.

[0096] The reception unit can filter information upon receipt based on the user's current living situation and areas of interest. For example, the reception unit can prioritize receiving relevant information based on the user's current living situation. It can also filter and receive relevant information based on the user's areas of interest. Furthermore, it can exclude unnecessary information based on the user's living situation and areas of interest. This allows the reception unit to receive relevant information based on the user's living situation 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 can receive information using an AI model that filters information based on the user's living situation and areas of interest.

[0097] The reception desk can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving important information. If the user is relaxed, the reception desk may also prioritize receiving detailed information. If the user is in a hurry, the reception desk may also prioritize receiving information that requires quick processing. In this way, the reception desk can provide information prioritization 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 processing described above in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can receive information using an AI model that estimates the user's emotions and determines the priority of information based on the estimated emotions.

[0098] The reception unit can prioritize receiving information that is highly relevant based on the user's geographical location. For example, the reception unit can prioritize receiving relevant information based on the user's current location. It can also prioritize receiving information related to a specific region based on the user's geographical location. Furthermore, the reception unit can filter out unnecessary information, taking the user's location into consideration. This allows the reception unit to provide highly relevant information based on the user's 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 receive information using an AI model that filters information while considering the user's geographical location.

[0099] The reception unit can analyze the user's social media activity and receive relevant information upon receiving it. For example, the reception unit can prioritize receiving relevant information based on the user's social media activity. The reception unit can also filter and receive information based on the user's areas of interest on social media. Furthermore, the reception unit can analyze the user's social media activity and eliminate unnecessary information. This allows the reception unit to provide relevant information based on the user's 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 receive information using an AI model that analyzes the user's social media activity and filters relevant information.

[0100] The generation unit can estimate the user's emotions and adjust the presentation of coordination suggestions based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate suggestions that proceed at a relaxed pace. If the user is in a hurry, the generation unit can also generate suggestions that emphasize the shortest route. If the user is excited, the generation unit can also generate suggestions with visually stimulating effects. In this way, the generation unit can provide a presentation of coordination suggestions that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can make suggestions using an AI model that estimates the user's emotions and adjusts the presentation of coordination suggestions based on the estimated emotions.

[0101] The generation unit can adjust the level of detail in the coordination proposals based on the room's purpose and budget. For example, the generation unit can adjust the level of detail of necessary furniture and items based on the room's purpose. It can also adjust the level of detail of the proposals based on the budget to reduce costs. Furthermore, the generation unit can generate optimal proposals considering both the room's purpose and budget. This allows the generation unit to provide detailed coordination proposals tailored to the room's purpose and budget. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make proposals using an AI model that adjusts the level of detail of proposals based on the room's purpose and budget.

[0102] The generation unit can apply different generation algorithms depending on the room style and color scheme when generating coordination suggestions. For example, the generation unit can select the optimal generation algorithm based on the room style. It can also apply different generation algorithms depending on the color scheme. Furthermore, the generation unit can generate optimal suggestions considering both the room style and color scheme. This allows the generation unit to provide optimal coordination suggestions tailored to the room style and color scheme. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make suggestions using an AI model that applies different generation algorithms depending on the room style and color scheme.

[0103] The generation 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 generation unit can generate short, concise suggestions. If the user is relaxed, the generation unit can generate longer suggestions with detailed explanations. If the user is excited, the generation unit can generate suggestions with visually stimulating effects. In this way, the generation unit can provide suggestions of a length that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can make suggestions using an AI model that estimates the user's emotions and adjusts the length of suggestions based on the estimated emotions.

[0104] The generation unit can determine the priority of coordination suggestions based on the frequency of room use when generating suggestions. For example, the generation unit can prioritize suggesting important items based on the frequency of room use. The generation unit can also provide cost-effective suggestions for rooms that are used infrequently. Furthermore, the generation unit can prioritize detailed suggestions for rooms that are used frequently. In this way, the generation unit can provide high-priority suggestions according to the frequency of room use. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make suggestions using an AI model that determines the priority of suggestions based on the frequency of room use.

[0105] The generation unit can adjust the order of coordination suggestions based on the relationships between rooms when generating them. For example, the generation unit can prioritize important suggestions based on the relationships between rooms. It can also postpone suggestions for rooms with low relevance. Furthermore, the generation unit can determine the optimal order of suggestions by considering the relationships between rooms. This allows the generation unit to provide the optimal order of suggestions according to the relationships between rooms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make suggestions using an AI model that adjusts the order of suggestions based on the relationships between rooms.

[0106] The display unit can estimate the user's emotions and adjust the display method of the visual preview based on the estimated emotions. For example, if the user is tense, the display unit can provide a simple and highly visible display method. If the user is relaxed, the display unit can also provide a display method that includes detailed information. If the user is in a hurry, the display unit can also provide a display method that gets straight to the point. In this way, the display unit can provide a visual preview display method that is appropriate 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 display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that estimates the user's emotions and adjusts the display method of the visual preview based on the estimated emotions.

[0107] The display unit can select the optimal display method by referring to the user's past design history when displaying a visual preview. For example, the display unit can select the optimal display method based on the user's past design history. The display unit can also prioritize displaying design styles that the user has previously preferred. Furthermore, the display unit can analyze the user's past design history and select the most visually appealing display method. This allows the display unit to provide an optimal visual preview based on the user's past design history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that selects the optimal display method by referring to the user's past design history.

[0108] The display unit can customize the displayed content based on the size and shape of the room when displaying a visual preview. For example, the display unit can customize the optimal displayed content based on the size of the room. The display unit can also adjust the displayed content according to the shape of the room. Furthermore, the display unit can provide an optimal visual preview considering the size and shape of the room. In this way, the display unit can provide an optimal visual preview according to the size and shape of the room. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that customizes the displayed content based on the size and shape of the room.

[0109] The display unit can estimate the user's emotions and determine the priority of visual previews based on the estimated emotions. For example, if the user is stressed, the display unit may prioritize displaying important visual previews. If the user is relaxed, the display unit may also prioritize displaying detailed visual previews. If the user is in a hurry, the display unit may also prioritize displaying concise visual previews. In this way, the display unit can provide a priority of visual previews 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 display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that estimates the user's emotions and determines the priority of visual previews based on the estimated emotions.

[0110] The display unit can select the optimal display method when displaying a visual preview, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, the display unit can provide a concise and highly visible display method if the user is using a smartwatch. This allows the display unit to provide the optimal visual preview based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that selects the optimal display method considering the user's device information.

[0111] The display unit can analyze the user's social media activity and display relevant previews when displaying visual previews. For example, the display unit can display relevant visual previews based on the user's social media activity. The display unit can also customize visual previews based on the user's areas of interest on social media. Furthermore, the display unit can analyze the user's social media activity and eliminate unnecessary visual previews. This allows the display unit to provide relevant visual previews based on the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that analyzes the user's social media activity and displays relevant previews.

[0112] The adjustment unit can estimate the user's emotions and modify the adjustment method of the suggestions based on the estimated user emotions. For example, if the user is nervous, the adjustment unit can provide a simple and highly visible adjustment method. If the user is relaxed, the adjustment unit can also provide an adjustment method that includes detailed information. If the user is in a hurry, the adjustment unit can also provide a concise adjustment method. In this way, the adjustment unit can provide an adjustment method of suggestions that is appropriate 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 adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can perform adjustments using an AI model that estimates the user's emotions and modifies the adjustment method of the suggestions based on the estimated emotions.

[0113] The adjustment unit can select the optimal adjustment method when adjusting a proposal by referring to the user's past feedback history. For example, the adjustment unit selects the optimal adjustment method based on the user's past feedback history. The adjustment unit can also prioritize providing adjustment methods that the user has preferred in the past. Furthermore, the adjustment unit can analyze the user's past feedback history and select the most effective adjustment method. This allows the adjustment unit to provide the optimal proposal adjustment method based on the user's past feedback history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can perform adjustments using an AI model that selects the optimal adjustment method by referring to the user's past feedback history.

[0114] The adjustment unit can customize the adjustments made to the proposal based on the room's intended use and budget. For example, the adjustment unit can provide the optimal adjustments based on the room's intended use. The adjustment unit can also customize the adjustments based on the budget to reduce costs. Furthermore, the adjustment unit can provide the optimal adjustments considering both the room's intended use and budget. This allows the adjustment unit to provide the optimal proposal adjustments tailored to the room's intended use and budget. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can perform adjustments using an AI model that customizes the adjustments based on the room's intended use and budget.

[0115] The adjustment unit can estimate the user's emotions and determine the priority of adjustments to suggestions based on the estimated emotions. For example, if the user is stressed, the adjustment unit will prioritize important adjustments. If the user is relaxed, the adjustment unit may also prioritize detailed adjustments. If the user is in a hurry, the adjustment unit may also prioritize concise adjustments. In this way, the adjustment unit can provide an adjustment priority for suggestions that corresponds 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 adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can perform adjustments using an AI model that estimates the user's emotions and determines the adjustment priority of suggestions based on the estimated emotions.

[0116] The adjustment unit can select the optimal adjustment method when adjusting proposals, taking into account the user's geographical location information. For example, the adjustment unit selects the optimal adjustment method based on the user's geographical location information. The adjustment unit can also customize the adjustment content by taking into account region-related information. Furthermore, the adjustment unit can eliminate unnecessary adjustments by taking into account the user's location information. In this way, the adjustment unit can provide the optimal proposal adjustment method based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can perform adjustments using an AI model that selects the optimal adjustment method by taking into account the user's geographical location information.

[0117] The adjustment unit can analyze the user's social media activity and propose adjustments when adjusting proposals. For example, the adjustment unit proposes relevant adjustments based on the user's social media activity. The adjustment unit can also customize the adjustments based on the user's areas of interest on social media. Furthermore, the adjustment unit can analyze the user's social media activity and eliminate unnecessary adjustments. This allows the adjustment unit to provide optimal adjustments to proposals based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can perform adjustments using an AI model that analyzes the user's social media activity and proposes adjustments.

[0118] The link provider can estimate the user's emotions and adjust how it provides online shopping links based on those emotions. For example, if the user is stressed, the link provider can provide simple, highly visible links. If the user is relaxed, it can also provide links containing more detailed information. If the user is in a hurry, it can provide concise links. This allows the link provider to provide online shopping links in a way that suits 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 link provider may be performed using AI or not. For example, the link provider can provide links using an AI model that estimates the user's emotions and adjusts how it provides online shopping links based on those emotions.

[0119] The link provider can select the most suitable link when providing online shopping links by referring to the user's past purchase history. For example, the link provider can select the most suitable link based on the user's past purchase history. The link provider can also provide links related to items the user has previously purchased. Furthermore, the link provider can analyze the user's past purchase history and select the most relevant link. This allows the link provider to provide the most suitable online shopping links based on the user's past purchase history. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that selects the most suitable link by referring to the user's past purchase history.

[0120] The link provider can customize the content of online shopping links based on the user's budget and preferences. For example, the link provider can provide the most suitable link content based on the user's budget. It can also customize the link content according to the user's preferences. Furthermore, it can provide the most suitable link content considering both the user's budget and preferences. This allows the link provider to provide the most suitable online shopping links according to the user's budget and preferences. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that customizes the link content based on the user's budget and preferences.

[0121] The link provider can estimate the user's emotions and determine the priority of link provision based on the estimated emotions. For example, if the user is stressed, the link provider may prioritize providing important links. If the user is relaxed, the link provider may prioritize providing detailed links. If the user is in a hurry, the link provider may prioritize providing concise links. In this way, the link provider can provide a priority for link provision that corresponds 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 link provider may be performed using AI, for example, or not using AI. For example, the link provider can provide links using an AI model that estimates the user's emotions and determines the priority of link provision based on the estimated emotions.

[0122] The link provider can select the most suitable link when providing online shopping links, taking into account the user's geographical location. For example, the link provider can select the most suitable link based on the user's geographical location. The link provider can also customize the link content by considering region-related information. Furthermore, the link provider can eliminate unnecessary links by considering the user's location. This allows the link provider to provide the most suitable online shopping links based on the user's geographical location. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that selects the most suitable link by considering the user's geographical location.

[0123] The link provider can provide relevant links by analyzing the user's social media activity when providing online shopping links. For example, the link provider can provide relevant links based on the user's social media activity. The link provider can also customize the content of links based on the user's areas of interest on social media. Furthermore, the link provider can analyze the user's social media activity and eliminate unnecessary links. This allows the link provider to provide optimal online shopping links based on the user's social media activity. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that analyzes the user's social media activity and provides relevant links.

[0124] The management department can estimate the user's emotions and adjust project management methods based on those estimated emotions. For example, if the user is stressed, the management department can provide a simple and highly visible management method. If the user is relaxed, the management department can provide a management method that includes detailed information. If the user is in a hurry, the management department can provide a management method that gets straight to the point. In this way, the management department can provide project management methods that are tailored 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 management department may be performed using AI, for example, or not using AI. For example, the management department can perform management using an AI model that estimates the user's emotions and adjusts project management methods based on those estimated emotions.

[0125] The management department can select the optimal management method by referring to the user's past project history during project management. For example, the management department selects the optimal management method based on the user's past project history. The management department can also prioritize providing management methods that the user has preferred in the past. Furthermore, the management department can analyze the user's past project history and select the most effective management method. This allows the management department to provide the optimal project management method based on the user's past project history. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can perform management using an AI model that selects the optimal management method by referring to the user's past project history.

[0126] The management department can customize project management based on the user's budget and progress. For example, the management department can provide optimal management based on the user's budget. It can also customize management according to the user's progress. Furthermore, it can provide optimal management considering both the user's budget and progress. This allows the management department to provide optimal project management tailored to the user's budget and progress. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can perform management using an AI model that customizes management based on the user's budget and progress.

[0127] The management department can estimate the user's emotions and determine project management priorities based on those estimated emotions. For example, if the user is stressed, the management department can prioritize providing important management items. If the user is relaxed, the management department can prioritize providing detailed management items. If the user is in a hurry, the management department can prioritize providing concise management items. This allows the management department to provide project management priorities that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can perform management using an AI model that estimates the user's emotions and determines project management priorities based on those estimated emotions.

[0128] The management department can select the optimal management method when managing a project, taking into account the user's geographical location information. For example, the management department can select the optimal management method based on the user's geographical location information. The management department can also customize the management content by taking into account region-related information. Furthermore, the management department can eliminate unnecessary management items by taking into account the user's location information. In this way, the management department can provide the optimal project management method based on the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can perform management using an AI model that selects the optimal management method by taking into account the user's geographical location information.

[0129] The management department can analyze users' social media activity and propose management strategies during project management. For example, the management department can propose relevant management strategies based on users' social media activity. Furthermore, the management department can customize management strategies based on users' areas of interest on social media. The management department can also analyze users' social media activity and eliminate unnecessary management items. This allows the management department to provide optimal project management strategies based on users' social media activity. Some or all of the above processes performed by the management department may be carried out using AI, for example, or without AI. For instance, the management department can perform management using an AI model that analyzes users' social media activity and proposes management strategies.

[0130] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can also select concise training data. If the user is excited, the learning unit can also select visually stimulating training data. In this way, the learning unit can provide training data selection that is appropriate 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that estimates the user's emotions and selects training data based on the estimated emotions.

[0131] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the algorithm parameters. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit can provide the optimal learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that optimizes the learning algorithm by referring to past learning data.

[0132] The learning unit can customize the learning content by analyzing the user's feedback history during the learning process. For example, the learning unit customizes the learning content based on the user's feedback history. The learning unit can also optimize the learning content by referring to feedback previously provided by the user. Furthermore, the learning unit can improve the accuracy of the learning content by analyzing the user's feedback history. This allows the learning unit to provide optimal learning content based on the user's feedback history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that analyzes the user's feedback history to customize the learning content.

[0133] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed, decrease it when the user is in a hurry, and adjust it when the user is excited. This allows the learning unit to provide a learning frequency that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that estimates the user's emotions and adjusts the learning frequency based on the estimated emotions.

[0134] The learning unit can weight the training data while considering the user's geographical location information during training. For example, the learning unit weights the training data based on the user's geographical location information. The learning unit can also adjust the weighting of the training data by considering region-related information. Furthermore, the learning unit can optimize the weighting of the training data by considering the user's location information. This allows the learning unit to provide optimal training data weighting based on the user's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that weights the training data while considering the user's geographical location information.

[0135] The learning unit can optimize learning content by analyzing the user's social media activity during the learning process. For example, the learning unit optimizes learning content based on the user's social media activity. The learning unit can also customize learning content based on the user's areas of interest on social media. Furthermore, the learning unit can analyze the user's social media activity and eliminate unnecessary learning content. This allows the learning unit to provide optimal learning content based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that analyzes the user's social media activity and optimizes learning content.

[0136] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0137] The reception desk can analyze the user's past interaction history and select the optimal method for receiving information. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also provide predictive input based on information the user has previously entered. Furthermore, the reception desk can predict and suggest information to be used during specific time periods based on the user's past interaction history. This allows the reception desk to provide the optimal method for receiving information based on the user's past interaction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can receive information using an AI model that analyzes the user's past interaction history and selects the optimal method for receiving information.

[0138] The generation unit can determine the priority of coordination suggestions based on the frequency of room use when generating suggestions. For example, it can prioritize suggesting important items based on the frequency of room use. The generation unit can also provide cost-effective suggestions for rooms that are used infrequently. Furthermore, the generation unit can prioritize detailed suggestions for rooms that are used frequently. In this way, the generation unit can provide high-priority suggestions according to the frequency of room use. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can make suggestions using an AI model that determines the priority of suggestions based on the frequency of room use.

[0139] The display unit can select the optimal display method when displaying a visual preview, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal visual preview based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can perform the display using an AI model that selects the optimal display method considering the user's device information.

[0140] The adjustment unit can select the optimal adjustment method when adjusting a proposal by referring to the user's past feedback history. For example, it can select the optimal adjustment method based on the user's past feedback history. The adjustment unit can also prioritize providing adjustment methods that the user has preferred in the past. Furthermore, the adjustment unit can analyze the user's past feedback history and select the most effective adjustment method. This allows the adjustment unit to provide the optimal proposal adjustment method based on the user's past feedback history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can perform adjustments using an AI model that selects the optimal adjustment method by referring to the user's past feedback history.

[0141] The link provider can select the most suitable link when providing online shopping links by referring to the user's past purchase history. For example, it can select the most suitable link based on the user's past purchase history. The link provider can also provide links related to items the user has previously purchased. Furthermore, the link provider can analyze the user's past purchase history and select the most relevant link. This allows the link provider to provide the most suitable online shopping links based on the user's past purchase history. Some or all of the above processing in the link provider may be performed using AI, for example, or without AI. For example, the link provider can provide links using an AI model that selects the most suitable link by referring to the user's past purchase history.

[0142] The reception desk can estimate the user's emotions and adjust the information reception method based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. In this way, the reception desk can provide an information reception method that is tailored 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 reception desk may be performed using AI or not. For example, the reception desk can receive information using an AI model that estimates the user's emotions and adjusts the information reception method based on the estimated emotions.

[0143] The generation unit can estimate the user's emotions and adjust the presentation of coordination suggestions based on the estimated emotions. For example, if the user is relaxed, it can generate suggestions that proceed at a leisurely pace. If the user is in a hurry, the generation unit can also generate suggestions that emphasize the shortest route. Furthermore, if the user is excited, the generation unit can generate suggestions with visually stimulating effects. In this way, the generation unit can provide a presentation of coordination suggestions that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can make suggestions using an AI model that estimates the user's emotions and adjusts the presentation of coordination suggestions based on the estimated emotions.

[0144] The display unit can estimate the user's emotions and adjust the display method of the visual preview based on the estimated emotions. For example, if the user is tense, it can provide a simple and highly visible display method. The display unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a concise display method. Thus, the display unit can provide a visual preview display method that is appropriate 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 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 display unit may be performed using AI, or not. For example, the display unit can perform the display using an AI model that estimates the user's emotions and adjusts the display method of the visual preview based on the estimated emotions.

[0145] The adjustment unit can estimate the user's emotions and modify the adjustment method of the suggestions based on the estimated user emotions. For example, if the user is nervous, it can provide a simple and highly visible adjustment method. If the user is relaxed, the adjustment unit can also provide an adjustment method that includes detailed information. Furthermore, if the user is in a hurry, the adjustment unit can provide a concise adjustment method. In this way, the adjustment unit can provide an adjustment method of suggestions that is appropriate 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 adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can perform adjustments using an AI model that estimates the user's emotions and modifies the adjustment method of the suggestions based on the estimated emotions.

[0146] The link provider can estimate the user's emotions and adjust how it provides online shopping links based on those emotions. For example, if the user is stressed, it can provide simple, highly visible links. If the user is relaxed, the link provider can also provide links containing more detailed information. Furthermore, if the user is in a hurry, it can provide concise links. This allows the link provider to provide online shopping links in a way that suits 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 link provider may be performed using AI or not. For example, the link provider can provide links using an AI model that estimates the user's emotions and adjusts how it provides online shopping links based on those emotions.

[0147] The following briefly describes the processing flow for example form 2.

[0148] Step 1: The reception desk receives information such as the user's preferences, the room's purpose, budget, color scheme, style, and furniture preferences. For example, the user might provide information such as, "I want to decorate my living room in a modern style." This information is then input into the generating AI. Step 2: The generation unit uses a generation AI to generate room coordination suggestions based on the information received by the reception unit. The generation AI suggests things like furniture placement, lighting, wall coverings, artwork, and accent items. For example, if a user inputs "I want to coordinate my living room in a modern style," the generation AI will suggest furniture, lighting, artwork, etc. that would suit a modern style. Step 3: The display unit shows the suggestions generated by the generation unit as a visual preview. The user can visually understand the appearance of the room in real time through 3D models and images. For example, the user can check the suggested furniture arrangement in the 3D model and grasp the overall atmosphere of the room. Step 4: The adjustment unit collects feedback provided by the user regarding the suggestions displayed by the display unit and adjusts the suggestions. For example, the user might provide feedback such as, "I want to change the color of this sofa." This feedback is input into the generating AI, which then adjusts the suggestions to match the user's preferences. Step 5: The link provider provides online shopping links based on the suggestions adjusted by the adjustment unit. For example, if the user likes a suggested sofa, a link to purchase that sofa is provided. This allows the user to easily purchase the item they need. Step 6: The management team tracks user projects and manages progress, budgets, purchase lists, etc. For example, they can track which items users have purchased and how much budget remains. Step 7: The learning unit collects user selections and feedback to improve the generative AI's AI model. This allows the generative AI to make more appropriate suggestions over time. For example, based on user feedback, the generative AI can tailor future suggestions to better suit the user's preferences.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] For example, the reception unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the display unit is implemented by the display 40A of the smart device 14. For example, the adjustment unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the link provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the management unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0153] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the display unit is implemented by the display of the smart glasses 214. For example, the adjustment unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the link provision unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the management unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0169] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the display unit is implemented by the display 343 of the headset terminal 314. For example, the adjustment unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the link provision unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the management unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0185] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] For example, the reception unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the display unit is implemented by the display of the robot 414. For example, the adjustment unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the link provision unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the management unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] (Note 1) A reception desk that receives information on user preferences, room usage, budget, color scheme, style, and furniture preferences, A generation unit that generates room coordination proposals based on the information received by the reception unit, A display unit that displays the proposal generated by the generation unit as a visual preview, An adjustment unit collects feedback provided by the user regarding the suggestions displayed by the aforementioned display unit and adjusts the suggestions. A link providing unit that provides online shopping links based on the proposal adjusted by the aforementioned adjustment unit, The management department tracks user projects and manages progress, budget, and purchase lists. It includes a learning unit that collects user selections and feedback to improve the AI ​​model of the generated AI. A system characterized by the following features. (Note 2) The generating unit is We propose furniture arrangement, lighting, wall coverings, artwork, and accent items. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is Display the room's exterior in real time through a 3D model or image. The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, We adjust suggestions based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned link provider unit, Provides online shopping links for users to purchase design elements or furniture they like. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Manage the user's project progress, budget, and purchase list. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, Collect user choices and feedback to improve the AI ​​model of the generative AI. 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 adjusts how information is received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past interaction history and select the optimal method for receiving information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving information, filtering is performed based on the user's current lifestyle 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 information to accept 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 receiving information, the system prioritizes receiving information that is highly relevant 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 receiving information, relevant information is collected based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the user's emotions and adjusts the way coordination suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating coordination suggestions, adjust the level of detail in the suggestions based on the room's purpose and budget. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating coordination suggestions, different generation algorithms are applied depending on the room style and color scheme. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit 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 generating unit is When generating coordination suggestions, the priority of suggestions is determined based on how often the rooms are used. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating coordination suggestions, the order of suggestions is adjusted based on the relevance of the rooms. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is It estimates the user's emotions and adjusts how the visual preview is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying a visual preview, the system selects the optimal display method by referring to the user's past design history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displaying a visual preview, the displayed content is customized based on the size and shape of the room. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is It estimates the user's emotions and prioritizes visual previews based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying a visual preview, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying a visual preview, the system analyzes the user's social media activity and displays relevant previews. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, We estimate the user's emotions and adjust the way we adjust suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, When adjusting a proposal, refer to the user's past feedback history to select the most suitable adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, When adjusting the proposal, customize the adjustments based on the room's intended use and budget. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, It estimates the user's emotions and determines the priority of adjusting suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, When adjusting the proposal, the optimal adjustment method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, When refining a proposal, we analyze the user's social media activity and propose adjustments accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned link provider unit, We estimate user sentiment and adjust how online shopping links are provided based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned link provider unit, When providing online shopping links, the system selects the most suitable link by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned link provider unit, When providing online shopping links, customize the link content based on the user's budget and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned link provider unit, It estimates user sentiment and determines the priority of link provision based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned link provider unit, When providing online shopping links, the system selects the most suitable link by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned link provider unit, When providing online shopping links, we analyze the user's social media activity and provide relevant links. The system according to Appendix 1, characterized in that... (Appendix 38) The management department estimates the user's emotion and adjusts the project management method based on the estimated user emotion The system according to Appendix 1, characterized in that... (Appendix 39) The management department selects an optimal management method by referring to the user's past project history during project management The system according to Appendix 1, characterized in that... (Appendix 40) The management department customizes the management content based on the user's budget and progress status during project management The system according to Appendix 1, characterized in that... [[ID=Z5]] (Appendix 41) The management department estimates the user's emotion and determines the priority of project management based on the estimated user emotion The system according to Appendix 1, characterized in that... (Appendix 42) The management department selects an optimal management method by considering the user's geographical location information during project management The system according to Appendix 1, characterized in that... (Appendix 43) [[ID=A3]] The management department analyzes the user's social media activities and proposes management content during project management The system according to Appendix 1, characterized in that... (Appendix 44) The learning department estimates the user's emotion and selects learning data based on the estimated user emotion The system according to Appendix 1, characterized in that... (Appendix 45) The learning department optimizes the learning algorithm by referring to past learning data during learning The system according to appended claim 1, characterized in that... (Appended claim 46) The learning unit During learning, analyzes the user's feedback history to customize the learning content The system according to appended claim 1, characterized in that... (Appended claim 47) The learning unit Estimates the user's emotion and adjusts the learning frequency based on the estimated user emotion The system according to appended claim 1, characterized in that... (Appended claim 48) The learning unit During learning, weights the learning data considering the user's geographical location information The system according to appended claim 1, characterized in that... (Appended claim 49) The learning unit During learning, analyzes the user's social media activities to optimize the learning content The system according to appended claim 1, characterized in that...

Explanation of reference signs

[0221] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that receives information on user preferences, room usage, budget, color scheme, style, and furniture preferences, A generation unit that generates room coordination proposals based on the information received by the reception unit, A display unit that displays the proposal generated by the generation unit as a visual preview, An adjustment unit collects feedback provided by the user regarding the suggestions displayed by the aforementioned display unit and adjusts the suggestions. A link providing unit that provides online shopping links based on the proposal adjusted by the aforementioned adjustment unit, The management department tracks user projects and manages progress, budget, and purchase lists. It includes a learning unit that collects user selections and feedback to improve the AI ​​model of the generative AI. A system characterized by the following features.

2. The generating unit is We propose furniture arrangement, lighting, wall coverings, artwork, and accent items. The system according to feature 1.

3. The aforementioned display unit is Display the room's exterior in real time through a 3D model or image. The system according to feature 1.

4. The adjustment unit is, We adjust suggestions based on user feedback. The system according to feature 1.

5. The aforementioned link provider unit, Provides online shopping links for users to purchase design elements or furniture they like. The system according to feature 1.

6. The aforementioned management department, Manage the user's project progress, budget, and purchase list. The system according to feature 1.

7. The aforementioned learning unit, We collect user selections and feedback to improve the AI ​​model of our generative AI. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts how information is received based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze the user's past interaction history and select the optimal method for receiving information. The system according to feature 1.

10. The aforementioned reception unit is When receiving information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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