system

Abstract

The system according to this embodiment aims to help users design their rooms easily and effectively. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos and text information provided by the user. The analysis unit analyzes the information received by the reception unit. The generation unit automatically generates color palettes, furniture arrangements, and decoration ideas based on the information analyzed by the analysis unit. The provision unit provides the design proposals generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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, the assistance for a user to effectively design their own room is not sufficient, and there is room for improvement.

[0005] The system according to the embodiment aims to assist a user in easily and effectively designing their own room.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos and text information provided by the user. The analysis unit analyzes the information received by the reception unit. The generation unit automatically generates color palettes, furniture arrangements, and decoration ideas based on the information analyzed by the analysis unit. The provision unit provides the design proposals generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can help users design their rooms easily and effectively. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An interior design proposal system according to an embodiment of the present invention is a system in which AI proposes interior designs from photos and text information provided by the user. This interior design proposal system automatically generates color palettes, furniture arrangements, and decoration ideas, helping the user to easily and effectively design their own room. For example, the user inputs photos and text information of their room into the system. For example, they input photos of their room and requests such as "I want a calm atmosphere." This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​analyzes the photos of the room using image recognition technology to identify the room layout and existing furniture. It also uses text analysis technology to understand the user's requests. For example, based on the request for a "calm atmosphere," it selects a color palette and furniture style. Based on the analysis results, the AI ​​automatically generates color palettes, furniture arrangements, and decoration ideas. For example, to create a calm atmosphere, it proposes a beige and brown color palette and arranges the furniture in a relaxing manner. It also proposes decoration ideas such as houseplants and indirect lighting. The generated design proposal is provided to the user. The user can review the proposed design and input requests for modifications or additional requests as needed. The AI ​​learns from user feedback and incorporates it into future suggestions. This system allows users to achieve professional-quality designs at a low cost without hiring a professional designer. Furthermore, its user-friendly interface makes it accessible to busy modern people interested in design. The AI ​​is continuously updated, improving its accuracy. This will increase user satisfaction and is expected to boost sales of related products. This system represents a market ripe for entry, given the growing interest in home interiors, advancements in AI technology, and improved accessibility. In the interior design market, particularly the DIY market, it aims to provide beautiful living spaces for everyone, promoting DIY culture and enhancing creativity. The interior design suggestion system allows the AI ​​to propose interior designs based on information provided by the user.

[0029] The interior design proposal system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos and text information provided by the user. The photos and text information provided by the user may include, but are not limited to, photos of a room, photos of furniture, and text information related to interiors. For example, the user can upload a photo of a room and input text information such as "I want a calm atmosphere." The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses an image analysis algorithm to analyze the photo of the room and identify the room layout and existing furniture. The analysis unit can also use a text analysis algorithm to understand the user's requests. For example, the analysis unit selects a color palette and furniture style based on the request for a "calm atmosphere." The generation unit automatically generates a color palette, furniture arrangement, and decoration ideas based on the information analyzed by the analysis unit. For example, the generation unit suggests a beige and brown color palette and arranges the furniture in a relaxing manner based on the analysis results. The generation unit can also suggest decoration ideas such as houseplants and indirect lighting. The provisioning unit provides the user with the design proposals generated by the generation unit. The provisioning unit, for example, displays the generated design proposals to the user, allowing the user to review the proposals and input requests for modifications or additions as needed. This enables the interior design proposal system according to the embodiment to have the AI ​​propose interior designs based on information provided by the user. Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and provisioning unit may be performed using AI, or not using AI. For example, the reception unit can input photos and text information provided by the user into the AI, which can then analyze the information and generate design proposals.

[0030] The reception section accepts photos and text information provided by users. This information may include, but is not limited to, photos of rooms, furniture, and text information about interior design. For example, a user could upload a photo of a room and enter text information such as "I want a calm atmosphere." Specifically, the reception section is designed to allow users to easily upload photos and enter text information through a user interface. The user interface is intuitive and easy to use, ensuring users can provide information without confusion. For example, a drag-and-drop function allows users to easily upload photos. Additionally, hints and sample sentences are displayed in the text input field to help users describe their requests specifically. Furthermore, the reception section automatically checks the format of the information provided by users and prompts for corrections as needed. For example, if the photo resolution is too low or the text information is insufficient, it displays an appropriate alert to the user and prompts them to re-enter the information. This ensures that the reception section maintains the quality of the information provided by users, allowing for smooth processing in the subsequent analysis and generation sections.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses an image analysis algorithm to analyze a photograph of a room to identify the room's layout and existing furniture. The analysis unit can also use a text analysis algorithm to understand the user's requests. For example, based on the request for a "calm atmosphere," the analysis unit can select a color palette and furniture style. Specifically, the image analysis algorithm uses deep learning technology to automatically recognize wall colors, floor materials, furniture placement, etc., from a photograph of the room. This allows for an accurate understanding of the overall layout of the room and existing interior elements. The text analysis algorithm uses natural language processing technology to analyze the user's requests in detail. For example, based on the request for a "calm atmosphere," it can select relaxing colors and materials based on color psychology. Furthermore, the analysis unit can consider the user's past interior design preferences and trend information to provide more personalized suggestions. This allows the analysis unit to accurately understand the user's requests and provide the basic information necessary to propose the optimal interior design.

[0032] The generation unit automatically generates color palettes, furniture arrangements, and decorative ideas based on information analyzed by the analysis unit. For example, based on the analysis results, the generation unit suggests a beige and brown color palette and arranges furniture to create a relaxing atmosphere. The generation unit can also suggest decorative ideas such as houseplants and indirect lighting. Specifically, the generation unit uses AI to generate designs that are optimal for the user's requests and the room layout. For example, the generation AI selects relaxing colors and materials based on the user's requests and proposes a color palette for the entire room. Regarding furniture arrangement, it calculates the optimal placement considering the size and shape of the room. Furthermore, the generation unit suggests decorative ideas such as houseplants, indirect lighting, and artwork to further enhance the room's atmosphere. As a result, the generation unit can automatically generate interior designs that meet the user's requests and provide concrete suggestions to the user. In addition, the generation unit can visualize the generated design suggestions as 3D models, allowing the user to grasp the actual room image more concretely. This enables the generation unit to provide users with high-quality interior design suggestions and improve user satisfaction.

[0033] The service provider provides users with design proposals generated by the generation unit. For example, the service provider displays the generated design proposals to the user, allowing the user to review them and input requests for modifications or additional information as needed. Specifically, the service provider visually displays the generated design proposals through the user interface. Users can review the proposed color palettes, furniture arrangements, and decoration ideas, and easily request modifications if there are parts they don't like or want to change. For example, if a user requests a slightly brighter color for a proposed color palette, the service provider accepts the request and requests the generation unit to generate a new proposal. The service provider can also use 3D models or virtual reality (VR) technology to allow users to experience the actual room more realistically when reviewing proposals. This allows users to concretely visualize how the proposed design will actually look. Furthermore, the service provider can collect user feedback and use it to improve the entire system. For example, by analyzing how users reacted to proposals and reflecting that in future proposals, the service provider can provide proposals that better meet user needs. This allows the service provider to offer users high-quality interior design proposals and improve user satisfaction.

[0034] The provisioning unit includes a learning unit that learns from user feedback and incorporates it into future proposals. For example, the provisioning unit allows users to input evaluations and comments on proposed designs. The learning unit collects user feedback and incorporates it into future proposals. For example, if a user comments, "I don't like this color palette," the learning unit learns from that feedback and proposes a different color palette in the next proposal. Also, if a user evaluates, "This furniture arrangement is good," the learning unit learns from that feedback and can propose a similar furniture arrangement in the next proposal. In this way, the accuracy of proposals is improved by learning from user feedback and incorporating it into future proposals. 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 input user feedback data into AI, which can analyze the feedback and incorporate it into the next proposal.

[0035] The generation unit analyzes a photograph of a room using image recognition technology to identify the room layout and existing furniture. For example, the generation unit analyzes a photograph of a room using an object detection algorithm to identify the room layout. For example, the generation unit identifies the positions of walls and floors from a photograph of a room to understand the room layout. The generation unit can also identify existing furniture using an image classification algorithm. For example, the generation unit identifies furniture such as sofas and tables from a photograph of a room to understand their placement. In this way, the room layout and existing furniture can be identified using image recognition technology. 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 input a photograph of a room into an AI, which can then use image recognition technology to identify the room layout and existing furniture.

[0036] The generation unit understands user requests using text analysis technology. The generation unit analyzes user requests using, for example, natural language processing technology. For example, the generation unit analyzes the user's request, "I want a calm atmosphere," and selects a color palette and furniture style to create a calm atmosphere. The generation unit can also understand user requests using keyword extraction technology. For example, the generation unit extracts keywords such as "calm" and "atmosphere" from the text information entered by the user and makes design proposals based on them. In this way, the user's requests can be understood by using text analysis technology. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user text information into AI, and the AI ​​can understand the user's requests using natural language processing technology.

[0037] The generation unit automatically generates color palettes, furniture arrangements, and decorative ideas based on the analysis results. For example, the generation unit might suggest a beige and brown color palette based on the analysis results. For instance, it might generate a color palette combining beige and brown to create a calm atmosphere. The generation unit can also suggest furniture arrangements based on the analysis results. For example, it might suggest arranging sofas and tables to create a relaxing atmosphere. The generation unit can also suggest decorative ideas such as houseplants and indirect lighting. For example, it might suggest placing houseplants to improve the room's atmosphere and using indirect lighting to create soft light. By automatically generating color palettes, furniture arrangements, and decorative ideas based on the analysis results, the system can provide the user with optimal design suggestions. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI, which can then automatically generate color palettes, furniture arrangements, and decorative ideas.

[0038] The service provider provides the generated design proposals to the user. The service provider, for example, displays the generated design proposals to the user. For example, the service provider allows the user to review the proposed designs and input requests for modifications or additional information as needed. The service provider can also provide the generated design proposals in image or text format. For example, the service provider can display color palettes and furniture placement suggestions as images and decorative ideas as text. By providing the generated design proposals to the user, the user can review the proposals and input requests for modifications or additional information as needed. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated design proposals into AI, and the AI ​​can provide them to the user.

[0039] The service provider offers an interface that anyone can use with simple operations. For example, the service provider allows users to easily review design proposals and input requests for revisions or additions using click and drag-and-drop operations. For example, the service provider allows users to click on a design proposal to view details and change the placement of furniture using drag-and-drop operations. The service provider can also provide an intuitive interface for users. For example, the service provider allows users to swipe through design proposals to see the next proposal. By providing an interface that anyone can use with simple operations, the system becomes easy to use even for busy modern people who are interested in design. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user operation data into AI, and the AI ​​can evaluate the ease of operation and optimize the interface.

[0040] The generation unit is continuously updated to improve accuracy. For example, the generation unit regularly improves its algorithms and adds new features. For example, the generation unit improves its algorithms based on user feedback and new design trends to provide more accurate design suggestions. The generation unit can also increase the variety of suggestions by adding new design elements and styles. For example, the generation unit can provide suggestions that incorporate the latest interior design trends. As a result, the generation unit is continuously updated, improving the accuracy of suggestions and increasing user satisfaction. Some or all of the above processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user feedback data and new design trend data into the AI, which can then improve its algorithms to enhance the accuracy of suggestions.

[0041] The reception desk analyzes the user's past design history and selects the optimal reception method. For example, based on the design style the user has used in the past, the reception desk prioritizes receiving photos and text information of a similar style. For example, the reception desk analyzes the color palettes and furniture arrangements the user has previously selected and prioritizes receiving information of a similar style. The reception desk can also analyze the format of information (photos, text, etc.) that the user has previously submitted and suggest the most suitable format for reception. For example, if the reception desk has submitted many photos in the past, it will prioritize receiving photos. The reception desk can also prioritize receiving information related to specific seasons or events based on the user's past design history. For example, if the reception desk has submitted many Christmas designs in the past, it will prioritize receiving Christmas-related information. In this way, the reception desk can select the optimal reception method by analyzing the user's past design 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 input the user's past design history data into AI, which can then select the optimal reception method.

[0042] The reception desk filters the received photos and text information based on the user's current interior style and preferences. For example, the reception desk prioritizes receiving relevant photos and text information based on the interior style the user is currently using. For example, the reception desk filters and receives relevant information based on the color palette and furniture arrangement the user is currently using. The reception desk can also filter and receive information containing specific colors or design elements based on the user's preferences. For example, the reception desk prioritizes receiving relevant information based on the colors and design elements the user likes. The reception desk can also exclude information that does not match the user's current interior style and accept only the most suitable information. For example, the reception desk excludes information containing colors or design elements that do not match the user's current interior style. This allows the reception desk to receive the most suitable information by filtering based on the user's current interior style and preferences. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current interior style and preferences into the AI, which can then perform the filtering.

[0043] The reception desk prioritizes receiving highly relevant information based on the user's geographical location when receiving photos and text information. For example, the reception desk prioritizes receiving interior design information suitable for the climate of the user's area. For instance, it might suggest color palettes and furniture arrangements that match the climate of the user's area. The reception desk can also prioritize receiving relevant information based on popular design styles in the user's area. For example, it might prioritize receiving information on interior designs that are trending in the user's area. Furthermore, the reception desk can prioritize receiving information that includes materials and design elements specific to the region, based on the user's geographical location. For example, it might prioritize receiving information that includes materials and design elements used in the user's area. By prioritizing highly relevant information based on the user's geographical location, the reception desk can receive more appropriate information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize receiving highly relevant information.

[0044] The reception department analyzes the user's social media activity when receiving photos and text information and receives relevant information. For example, the reception department can receive relevant information based on photos of interior designs shared by the user on social media. For example, the reception department analyzes photos of interior designs shared by the user on social media and receives relevant information preferentially. The reception department can also analyze the content of posts from design accounts that the user follows and receive relevant information. For example, the reception department can receive relevant information based on the content of posts from design accounts that the user follows. The reception department can also receive relevant information based on design styles that the user has "liked" on social media. For example, the reception department analyzes design styles that the user has "liked" and receives relevant information preferentially. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the user's social media activity data into AI, and the AI ​​can receive relevant information.

[0045] The analysis unit adjusts the level of detail of the analysis based on the importance of the photographic and textual information during the analysis. For example, the analysis unit performs a detailed analysis on photographic and textual information that contains important information. For example, the analysis unit performs a detailed analysis on information that the user considers particularly important. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit performs a concise analysis on information that the user does not consider particularly important. The analysis unit can also perform an analysis that focuses on specific information based on the user's request. For example, the analysis unit performs an analysis that focuses on information that the user considers particularly important. By adjusting the level of detail of the analysis based on the importance of the photographic and textual information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of the photographic and textual information into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0046] The analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a color analysis algorithm to information about a color palette. For example, when analyzing information about a color palette, the analysis unit applies a color analysis algorithm. The analysis unit can also apply a spatial analysis algorithm to information about furniture arrangement. For example, when analyzing information about furniture arrangement, the analysis unit applies a spatial analysis algorithm. The analysis unit can also apply a design analysis algorithm to information about decorative ideas. For example, when analyzing information about decorative ideas, the analysis unit applies a design analysis algorithm. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into the AI, and the AI ​​can apply different analysis algorithms.

[0047] The analysis unit determines the priority of analysis based on the timing of information submission. For example, the analysis unit prioritizes the analysis of the most recent information. For example, it prioritizes the analysis of photos and text information recently submitted by the user. The analysis unit can also determine the priority of analysis based on deadlines specified by the user. For example, it determines the priority of submitted information based on deadlines specified by the user. The analysis unit can also adjust the priority of analysis to match the user's schedule. For example, it adjusts the priority of submitted information based on the user's schedule. By determining the priority of analysis based on the timing of information submission, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission timing data into AI, and the AI ​​can determine the priority of analysis.

[0048] The analysis unit adjusts the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes analyzing the information most relevant to the user's requests. For example, the analysis unit prioritizes analyzing the information that the user considers particularly important. The analysis unit can also group highly relevant information for analysis. For example, the analysis unit groups highly relevant information for analysis. The analysis unit can also prioritize analyzing highly relevant information based on the user's past design history. For example, the analysis unit prioritizes analyzing highly relevant information based on the user's past design history. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into the AI, and the AI ​​can adjust the order of analysis.

[0049] The generation unit adjusts the level of detail of the generated design based on the importance of the analysis results. For example, the generation unit generates a detailed design based on important analysis results. For example, the generation unit generates a detailed design based on analysis results that the user considers particularly important. The generation unit can also generate a concise design based on general analysis results. For example, the generation unit generates a concise design based on analysis results that the user does not consider particularly important. The generation unit can also generate a design that emphasizes specific elements based on the user's requests. For example, the generation unit generates a design that emphasizes elements that the user considers particularly important. By adjusting the level of detail of the generated design based on the importance of the analysis results, a more appropriate design can be provided. 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 input the importance data of the analysis results into the AI, and the AI ​​can adjust the level of detail of the generated design.

[0050] The generation unit applies different generation algorithms depending on the category of the analysis results during generation. For example, the generation unit applies a color generation algorithm to the analysis results related to color palettes. For example, the generation unit generates a design by applying a color generation algorithm based on the analysis results related to color palettes. The generation unit can also apply a spatial generation algorithm to the analysis results related to furniture arrangement. For example, the generation unit generates a design by applying a spatial generation algorithm based on the analysis results related to furniture arrangements. The generation unit can also apply a design generation algorithm to the analysis results related to decorative ideas. For example, the generation unit generates a design by applying a design generation algorithm based on the analysis results related to decorative ideas. By applying different generation algorithms depending on the category of the analysis results, a more appropriate design can be provided. 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 input the category data of the analysis results into the AI, and the AI ​​can apply different generation algorithms.

[0051] The generation unit determines the generation priority based on the submission timing of the analysis results. For example, the generation unit prioritizes generating designs based on the latest analysis results. For example, the generation unit prioritizes generating designs based on the analysis results recently submitted by the user. The generation unit can also determine the generation priority based on the deadline specified by the user. For example, the generation unit can determine the generation priority based on the deadline specified by the user. The generation unit can also adjust the generation priority to match the user's schedule. For example, the generation unit can adjust the generation priority based on the user's schedule. This allows for the provision of more appropriate designs by determining the generation priority based on the submission timing of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the analysis result submission timing data into the AI, which can then determine the generation priority.

[0052] The generation unit adjusts the generation order based on the relevance of the analysis results during generation. For example, the generation unit prioritizes generating designs based on the analysis results most relevant to the user's requests. For example, the generation unit prioritizes generating designs based on the analysis results that the user considers particularly important. The generation unit can also group highly relevant analysis results and generate designs. For example, the generation unit can group highly relevant analysis results and generate designs. The generation unit can also generate designs based on highly relevant analysis results based on the user's past design history. For example, the generation unit can generate designs based on highly relevant analysis results based on the user's past design history. By adjusting the generation order based on the relevance of the analysis results, a more appropriate design can be provided. 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 input the relevance data of the analysis results into the AI, which can then adjust the generation order.

[0053] The service provider adjusts the level of detail in the provided design based on its importance. For example, the service provider provides detailed explanations for important design proposals. For example, the service provider provides detailed explanations for design proposals that the user considers particularly important. The service provider can also provide concise explanations for general design proposals. For example, the service provider provides concise explanations for design proposals that the user does not consider particularly important. The service provider can also provide explanations that focus on specific elements based on the user's requests. For example, the service provider provides explanations that focus on elements that the user considers particularly important. By adjusting the level of detail in the provided design based on its importance, the service provider can provide more appropriate designs. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the importance data of the generated designs into the AI, and the AI ​​can adjust the level of detail in the provided design.

[0054] The service provider applies different service algorithms depending on the category of the generated design at the time of delivery. For example, the service provider applies a color service algorithm to design proposals related to color palettes. For example, the service provider applies a color service algorithm when providing design proposals related to color palettes. The service provider can also apply a spatial service algorithm to design proposals related to furniture arrangement. For example, the service provider applies a spatial service algorithm when providing design proposals related to furniture arrangement. The service provider can also apply a design service algorithm to design proposals related to decorative ideas. For example, the service provider applies a design service algorithm when providing design proposals related to decorative ideas. By applying different service algorithms depending on the category of the generated design, more appropriate designs can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category data of the generated designs into the AI, and the AI ​​can apply different service algorithms.

[0055] The service provider determines the priority of deliveries based on the submission timing of the generated designs. For example, the service provider may prioritize the most recent design proposals. For example, the service provider may prioritize design proposals recently submitted by the user. The service provider may also determine the priority of deliveries based on deadlines specified by the user. For example, the service provider may determine the priority of deliveries based on deadlines specified by the user. The service provider may also adjust the priority of deliveries to match the user's schedule. For example, the service provider may adjust the priority of deliveries based on the user's schedule. This allows for the provision of more appropriate designs by determining the priority of deliveries based on the submission timing of the generated designs. Some or all of the above processes in the service provider may be performed using AI, or not using AI. For example, the service provider may input the submission timing data of the generated designs into an AI, which can then determine the priority of deliveries.

[0056] The delivery unit adjusts the order of delivery based on the relevance of the generated designs. For example, the delivery unit prioritizes providing design proposals that are most relevant to the user's requests. For example, the delivery unit prioritizes providing design proposals that the user considers particularly important. The delivery unit can also group and provide highly relevant design proposals. For example, the delivery unit can group and provide highly relevant design proposals. The delivery unit can also prioritize providing highly relevant design proposals based on the user's past design history. For example, the delivery unit can prioritize providing highly relevant design proposals based on the user's past design history. By adjusting the order of delivery based on the relevance of the generated designs, a more appropriate design can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance data of the generated designs into AI, and the AI ​​can adjust the order of delivery.

[0057] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and selects the optimal learning algorithm. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. For example, the learning unit adjusts the parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit improves the accuracy of the learning algorithm based on past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can optimize the learning algorithm.

[0058] The learning unit weights the training data based on the submission timing of design proposals during training. For example, the learning unit weights the training data based on the most recent design proposal. For example, the learning unit weights the training data based on the design proposal recently submitted by the user. The learning unit can also weight the training data based on a deadline specified by the user. For example, the learning unit weights the training data based on a deadline specified by the user. The learning unit can also adjust the weighting of the training data to match the user's schedule. For example, the learning unit adjusts the weighting of the training data based on the user's schedule. This allows for more appropriate training by weighting the training data based on the submission timing of design proposals. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input design proposal submission timing data into the AI, and the AI ​​can weight the training data.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception unit can accept voice memos in addition to photos and text information provided by users. For example, a user can upload a photo of their room and input text information such as "I want a calm atmosphere," but can also convey detailed requests in a voice memo. The analysis unit can analyze the voice memo to understand the user's requests in more detail. For example, if a user says in a voice memo, "I want to hang a picture on this wall," the analysis unit can analyze that request, and the generation unit can suggest an appropriate placement of the picture. This makes it possible to propose designs that more accurately reflect the user's requests.

[0061] The service provider can not only learn from user feedback but also customize suggestions based on the user's behavioral history. For example, it can analyze what design suggestions the user has adopted in the past and which suggestions they have given high ratings to. The learning unit learns from this behavioral history and reflects it in future suggestions. For instance, if a user has previously preferred a natural interior style, the next suggestion can prioritize a natural style. This allows for suggestions that match the user's preferences, leading to increased satisfaction.

[0062] The design generation unit can also consider the room's lighting conditions when analyzing a photograph of the room. For example, the unit can identify how natural light enters the room and the placement of lighting fixtures from the photograph, and then propose designs based on that. For instance, the unit can suggest a bright color palette for rooms with plenty of natural light, and a design that makes extensive use of indirect lighting for rooms with less lighting. This makes it possible to propose designs that are optimal for the room's lighting conditions.

[0063] The design generation unit can also consider the user's lifestyle when understanding their requests. For example, if a user inputs a request such as "I want a relaxing space," the unit will consider not only that request but also the user's lifestyle information (e.g., whether they have pets or children). This allows the unit to create furniture arrangements and select materials that are considerate of pets and children. For instance, it can suggest furniture made from durable materials for households with pets. This enables the design to be tailored to the user's lifestyle.

[0064] The generation unit can also take the user's budget into consideration when automatically generating color palettes, furniture arrangements, and decoration ideas based on the analysis results. For example, the generation unit can propose the optimal design within the budget based on the budget information entered by the user. For instance, it can propose cost-effective furniture and decoration ideas for low budgets, and high-quality furniture and decoration ideas for high budgets. This makes it possible to propose designs that match the user's budget.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives photos and text information provided by the user. This information may include, for example, photos of the room, photos of the furniture, and text information about the interior design. The reception desk can receive photos of the room and text information such as "I want a calm atmosphere." Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses image analysis algorithms to analyze photos of the room and identify the room layout and existing furniture. It also uses text analysis algorithms to understand the user's requests. For example, based on the request for a "calm atmosphere," it selects a color palette and furniture style. Step 3: The generation unit automatically generates color palettes, furniture arrangements, and decorative ideas based on the information analyzed by the analysis unit. Based on the analysis results, the generation unit suggests a beige and brown color palette and arranges furniture to create a relaxing atmosphere. It also suggests decorative ideas such as houseplants and indirect lighting. Step 4: The provisioning unit provides the user with the design proposals generated by the generation unit. The provisioning unit displays the generated design proposals to the user, allowing the user to review the proposals and input any necessary modifications or additional requests.

[0067] (Example of form 2) An interior design proposal system according to an embodiment of the present invention is a system in which AI proposes interior designs from photos and text information provided by the user. This interior design proposal system automatically generates color palettes, furniture arrangements, and decoration ideas, helping the user to easily and effectively design their own room. For example, the user inputs photos and text information of their room into the system. For example, they input photos of their room and requests such as "I want a calm atmosphere." This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​analyzes the photos of the room using image recognition technology to identify the room layout and existing furniture. It also uses text analysis technology to understand the user's requests. For example, based on the request for a "calm atmosphere," it selects a color palette and furniture style. Based on the analysis results, the AI ​​automatically generates color palettes, furniture arrangements, and decoration ideas. For example, to create a calm atmosphere, it proposes a beige and brown color palette and arranges the furniture in a relaxing manner. It also proposes decoration ideas such as houseplants and indirect lighting. The generated design proposal is provided to the user. The user can review the proposed design and input requests for modifications or additional requests as needed. The AI ​​learns from user feedback and incorporates it into future suggestions. This system allows users to achieve professional-quality designs at a low cost without hiring a professional designer. Furthermore, its user-friendly interface makes it accessible to busy modern people interested in design. The AI ​​is continuously updated, improving its accuracy. This will increase user satisfaction and is expected to boost sales of related products. This system represents a market ripe for entry, given the growing interest in home interiors, advancements in AI technology, and improved accessibility. In the interior design market, particularly the DIY market, it aims to provide beautiful living spaces for everyone, promoting DIY culture and enhancing creativity. The interior design suggestion system allows the AI ​​to propose interior designs based on information provided by the user.

[0068] The interior design proposal system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos and text information provided by the user. The photos and text information provided by the user may include, but are not limited to, photos of a room, photos of furniture, and text information related to interiors. For example, the user can upload a photo of a room and input text information such as "I want a calm atmosphere." The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses an image analysis algorithm to analyze the photo of the room and identify the room layout and existing furniture. The analysis unit can also use a text analysis algorithm to understand the user's requests. For example, the analysis unit selects a color palette and furniture style based on the request for a "calm atmosphere." The generation unit automatically generates a color palette, furniture arrangement, and decoration ideas based on the information analyzed by the analysis unit. For example, the generation unit suggests a beige and brown color palette and arranges the furniture in a relaxing manner based on the analysis results. The generation unit can also suggest decoration ideas such as houseplants and indirect lighting. The provisioning unit provides the user with the design proposals generated by the generation unit. The provisioning unit, for example, displays the generated design proposals to the user, allowing the user to review the proposals and input requests for modifications or additions as needed. This enables the interior design proposal system according to the embodiment to have the AI ​​propose interior designs based on information provided by the user. Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and provisioning unit may be performed using AI, or not using AI. For example, the reception unit can input photos and text information provided by the user into the AI, which can then analyze the information and generate design proposals.

[0069] The reception section accepts photos and text information provided by users. This information may include, but is not limited to, photos of rooms, furniture, and text information about interior design. For example, a user could upload a photo of a room and enter text information such as "I want a calm atmosphere." Specifically, the reception section is designed to allow users to easily upload photos and enter text information through a user interface. The user interface is intuitive and easy to use, ensuring users can provide information without confusion. For example, a drag-and-drop function allows users to easily upload photos. Additionally, hints and sample sentences are displayed in the text input field to help users describe their requests specifically. Furthermore, the reception section automatically checks the format of the information provided by users and prompts for corrections as needed. For example, if the photo resolution is too low or the text information is insufficient, it displays an appropriate alert to the user and prompts them to re-enter the information. This ensures that the reception section maintains the quality of the information provided by users, allowing for smooth processing in the subsequent analysis and generation sections.

[0070] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses an image analysis algorithm to analyze a photograph of a room to identify the room's layout and existing furniture. The analysis unit can also use a text analysis algorithm to understand the user's requests. For example, based on the request for a "calm atmosphere," the analysis unit can select a color palette and furniture style. Specifically, the image analysis algorithm uses deep learning technology to automatically recognize wall colors, floor materials, furniture placement, etc., from a photograph of the room. This allows for an accurate understanding of the overall layout of the room and existing interior elements. The text analysis algorithm uses natural language processing technology to analyze the user's requests in detail. For example, based on the request for a "calm atmosphere," it can select relaxing colors and materials based on color psychology. Furthermore, the analysis unit can consider the user's past interior design preferences and trend information to provide more personalized suggestions. This allows the analysis unit to accurately understand the user's requests and provide the basic information necessary to propose the optimal interior design.

[0071] The generation unit automatically generates color palettes, furniture arrangements, and decorative ideas based on information analyzed by the analysis unit. For example, based on the analysis results, the generation unit suggests a beige and brown color palette and arranges furniture to create a relaxing atmosphere. The generation unit can also suggest decorative ideas such as houseplants and indirect lighting. Specifically, the generation unit uses AI to generate designs that are optimal for the user's requests and the room layout. For example, the generation AI selects relaxing colors and materials based on the user's requests and proposes a color palette for the entire room. Regarding furniture arrangement, it calculates the optimal placement considering the size and shape of the room. Furthermore, the generation unit suggests decorative ideas such as houseplants, indirect lighting, and artwork to further enhance the room's atmosphere. As a result, the generation unit can automatically generate interior designs that meet the user's requests and provide concrete suggestions to the user. In addition, the generation unit can visualize the generated design suggestions as 3D models, allowing the user to grasp the actual room image more concretely. This enables the generation unit to provide users with high-quality interior design suggestions and improve user satisfaction.

[0072] The service provider provides users with design proposals generated by the generation unit. For example, the service provider displays the generated design proposals to the user, allowing the user to review them and input requests for modifications or additional information as needed. Specifically, the service provider visually displays the generated design proposals through the user interface. Users can review the proposed color palettes, furniture arrangements, and decoration ideas, and easily request modifications if there are parts they don't like or want to change. For example, if a user requests a slightly brighter color for a proposed color palette, the service provider accepts the request and requests the generation unit to generate a new proposal. The service provider can also use 3D models or virtual reality (VR) technology to allow users to experience the actual room more realistically when reviewing proposals. This allows users to concretely visualize how the proposed design will actually look. Furthermore, the service provider can collect user feedback and use it to improve the entire system. For example, by analyzing how users reacted to proposals and reflecting that in future proposals, the service provider can provide proposals that better meet user needs. This allows the service provider to offer users high-quality interior design proposals and improve user satisfaction.

[0073] The provisioning unit includes a learning unit that learns from user feedback and incorporates it into future proposals. For example, the provisioning unit allows users to input evaluations and comments on proposed designs. The learning unit collects user feedback and incorporates it into future proposals. For example, if a user comments, "I don't like this color palette," the learning unit learns from that feedback and proposes a different color palette in the next proposal. Also, if a user evaluates, "This furniture arrangement is good," the learning unit learns from that feedback and can propose a similar furniture arrangement in the next proposal. In this way, the accuracy of proposals is improved by learning from user feedback and incorporating it into future proposals. 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 input user feedback data into AI, which can analyze the feedback and incorporate it into the next proposal.

[0074] The generation unit analyzes a photograph of a room using image recognition technology to identify the room layout and existing furniture. For example, the generation unit analyzes a photograph of a room using an object detection algorithm to identify the room layout. For example, the generation unit identifies the positions of walls and floors from a photograph of a room to understand the room layout. The generation unit can also identify existing furniture using an image classification algorithm. For example, the generation unit identifies furniture such as sofas and tables from a photograph of a room to understand their placement. In this way, the room layout and existing furniture can be identified using image recognition technology. 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 input a photograph of a room into an AI, which can then use image recognition technology to identify the room layout and existing furniture.

[0075] The generation unit understands user requests using text analysis technology. The generation unit analyzes user requests using, for example, natural language processing technology. For example, the generation unit analyzes the user's request, "I want a calm atmosphere," and selects a color palette and furniture style to create a calm atmosphere. The generation unit can also understand user requests using keyword extraction technology. For example, the generation unit extracts keywords such as "calm" and "atmosphere" from the text information entered by the user and makes design proposals based on them. In this way, the user's requests can be understood by using text analysis technology. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user text information into AI, and the AI ​​can understand the user's requests using natural language processing technology.

[0076] The generation unit automatically generates color palettes, furniture arrangements, and decorative ideas based on the analysis results. For example, the generation unit might suggest a beige and brown color palette based on the analysis results. For instance, it might generate a color palette combining beige and brown to create a calm atmosphere. The generation unit can also suggest furniture arrangements based on the analysis results. For example, it might suggest arranging sofas and tables to create a relaxing atmosphere. The generation unit can also suggest decorative ideas such as houseplants and indirect lighting. For example, it might suggest placing houseplants to improve the room's atmosphere and using indirect lighting to create soft light. By automatically generating color palettes, furniture arrangements, and decorative ideas based on the analysis results, the system can provide the user with optimal design suggestions. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI, which can then automatically generate color palettes, furniture arrangements, and decorative ideas.

[0077] The service provider provides the generated design proposals to the user. The service provider, for example, displays the generated design proposals to the user. For example, the service provider allows the user to review the proposed designs and input requests for modifications or additional information as needed. The service provider can also provide the generated design proposals in image or text format. For example, the service provider can display color palettes and furniture placement suggestions as images and decorative ideas as text. By providing the generated design proposals to the user, the user can review the proposals and input requests for modifications or additional information as needed. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated design proposals into AI, and the AI ​​can provide them to the user.

[0078] The service provider offers an interface that anyone can use with simple operations. For example, the service provider allows users to easily review design proposals and input requests for revisions or additions using click and drag-and-drop operations. For example, the service provider allows users to click on a design proposal to view details and change the placement of furniture using drag-and-drop operations. The service provider can also provide an intuitive interface for users. For example, the service provider allows users to swipe through design proposals to see the next proposal. By providing an interface that anyone can use with simple operations, the system becomes easy to use even for busy modern people who are interested in design. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user operation data into AI, and the AI ​​can evaluate the ease of operation and optimize the interface.

[0079] The generation unit is continuously updated to improve accuracy. For example, the generation unit regularly improves its algorithms and adds new features. For example, the generation unit improves its algorithms based on user feedback and new design trends to provide more accurate design suggestions. The generation unit can also increase the variety of suggestions by adding new design elements and styles. For example, the generation unit can provide suggestions that incorporate the latest interior design trends. As a result, the generation unit is continuously updated, improving the accuracy of suggestions and increasing user satisfaction. Some or all of the above processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user feedback data and new design trend data into the AI, which can then improve its algorithms to enhance the accuracy of suggestions.

[0080] The reception unit estimates the user's emotions and adjusts the timing of receiving photos and text information based on the estimated emotions. The reception unit can estimate the user's emotions using, for example, facial recognition technology. For example, the reception unit can capture the user's facial expression with a camera when they input photos or text information and estimate the emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can record the user's voice when they input photos or text information and estimate the emotions using voice analysis technology. Furthermore, the reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit can estimate emotions from the text information entered by the user. By adjusting the reception timing based on the user's emotions, information can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, 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 unit may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into an AI, which can then estimate the emotion and adjust the reception timing accordingly.

[0081] The reception desk analyzes the user's past design history and selects the optimal reception method. For example, based on the design style the user has used in the past, the reception desk prioritizes receiving photos and text information of a similar style. For example, the reception desk analyzes the color palettes and furniture arrangements the user has previously selected and prioritizes receiving information of a similar style. The reception desk can also analyze the format of information (photos, text, etc.) that the user has previously submitted and suggest the most suitable format for reception. For example, if the reception desk has submitted many photos in the past, it will prioritize receiving photos. The reception desk can also prioritize receiving information related to specific seasons or events based on the user's past design history. For example, if the reception desk has submitted many Christmas designs in the past, it will prioritize receiving Christmas-related information. In this way, the reception desk can select the optimal reception method by analyzing the user's past design 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 input the user's past design history data into AI, which can then select the optimal reception method.

[0082] The reception desk filters the received photos and text information based on the user's current interior style and preferences. For example, the reception desk prioritizes receiving relevant photos and text information based on the interior style the user is currently using. For example, the reception desk filters and receives relevant information based on the color palette and furniture arrangement the user is currently using. The reception desk can also filter and receive information containing specific colors or design elements based on the user's preferences. For example, the reception desk prioritizes receiving relevant information based on the colors and design elements the user likes. The reception desk can also exclude information that does not match the user's current interior style and accept only the most suitable information. For example, the reception desk excludes information containing colors or design elements that do not match the user's current interior style. This allows the reception desk to receive the most suitable information by filtering based on the user's current interior style and preferences. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current interior style and preferences into the AI, which can then perform the filtering.

[0083] The reception unit estimates the user's emotions and determines the priority of the information to be received based on the estimated emotions. The reception unit can estimate the user's emotions using, for example, facial recognition technology. For example, the reception unit can capture the user's facial expressions with a camera when they input photos or text information and estimate their emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can record the user's voice when they input photos or text information and estimate their emotions using voice analysis technology. Furthermore, the reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit can estimate emotions from the text information entered by the user. By prioritizing information based on the user's emotions, more appropriate information can be received preferentially. Emotion estimation is implemented using an emotion estimation function, for example, 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 unit may be performed using, for example, AI, or not using AI. For example, the reception desk can input user emotion data into an AI, which can then estimate the emotion and determine the priority of the information.

[0084] The reception desk prioritizes receiving highly relevant information based on the user's geographical location when receiving photos and text information. For example, the reception desk prioritizes receiving interior design information suitable for the climate of the user's area. For instance, it might suggest color palettes and furniture arrangements that match the climate of the user's area. The reception desk can also prioritize receiving relevant information based on popular design styles in the user's area. For example, it might prioritize receiving information on interior designs that are trending in the user's area. Furthermore, the reception desk can prioritize receiving information that includes materials and design elements specific to the region, based on the user's geographical location. For example, it might prioritize receiving information that includes materials and design elements used in the user's area. By prioritizing highly relevant information based on the user's geographical location, the reception desk can receive more appropriate information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize receiving highly relevant information.

[0085] The reception department analyzes the user's social media activity when receiving photos and text information and receives relevant information. For example, the reception department can receive relevant information based on photos of interior designs shared by the user on social media. For example, the reception department analyzes photos of interior designs shared by the user on social media and receives relevant information preferentially. The reception department can also analyze the content of posts from design accounts that the user follows and receive relevant information. For example, the reception department can receive relevant information based on the content of posts from design accounts that the user follows. The reception department can also receive relevant information based on design styles that the user has "liked" on social media. For example, the reception department analyzes design styles that the user has "liked" and receives relevant information preferentially. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the user's social media activity data into AI, and the AI ​​can receive relevant information.

[0086] The analysis unit estimates the user's emotions and adjusts the method of expressing the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using facial recognition technology. For example, the analysis unit captures the user's facial expressions with a camera when they input photos or text information and estimates the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using speech analysis technology. For example, the analysis unit records the user's voice when they input photos or text information and estimates the emotions using speech analysis technology. Furthermore, the analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit estimates emotions from text information entered by the user. By adjusting the method of expressing the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user emotion data into the AI, which can then estimate the emotion and adjust the way the analysis is expressed.

[0087] The analysis unit adjusts the level of detail of the analysis based on the importance of the photographic and textual information during the analysis. For example, the analysis unit performs a detailed analysis on photographic and textual information that contains important information. For example, the analysis unit performs a detailed analysis on information that the user considers particularly important. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit performs a concise analysis on information that the user does not consider particularly important. The analysis unit can also perform an analysis that focuses on specific information based on the user's request. For example, the analysis unit performs an analysis that focuses on information that the user considers particularly important. By adjusting the level of detail of the analysis based on the importance of the photographic and textual information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of the photographic and textual information into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0088] The analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a color analysis algorithm to information about a color palette. For example, when analyzing information about a color palette, the analysis unit applies a color analysis algorithm. The analysis unit can also apply a spatial analysis algorithm to information about furniture arrangement. For example, when analyzing information about furniture arrangement, the analysis unit applies a spatial analysis algorithm. The analysis unit can also apply a design analysis algorithm to information about decorative ideas. For example, when analyzing information about decorative ideas, the analysis unit applies a design analysis algorithm. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into the AI, and the AI ​​can apply different analysis algorithms.

[0089] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. The analysis unit estimates the user's emotions using, for example, facial recognition technology. For example, the analysis unit captures the user's facial expressions with a camera when they input photos or text information and estimates the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using speech analysis technology. For example, the analysis unit records the user's voice when they input photos or text information and estimates the emotions using speech analysis technology. Furthermore, the analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit estimates emotions from text information entered by the user. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user emotion data into the AI, which can then estimate the emotion and adjust the length of the analysis.

[0090] The analysis unit determines the priority of analysis based on the timing of information submission. For example, the analysis unit prioritizes the analysis of the most recent information. For example, it prioritizes the analysis of photos and text information recently submitted by the user. The analysis unit can also determine the priority of analysis based on deadlines specified by the user. For example, it determines the priority of submitted information based on deadlines specified by the user. The analysis unit can also adjust the priority of analysis to match the user's schedule. For example, it adjusts the priority of submitted information based on the user's schedule. By determining the priority of analysis based on the timing of information submission, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission timing data into AI, and the AI ​​can determine the priority of analysis.

[0091] The analysis unit adjusts the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes analyzing the information most relevant to the user's requests. For example, the analysis unit prioritizes analyzing the information that the user considers particularly important. The analysis unit can also group highly relevant information for analysis. For example, the analysis unit groups highly relevant information for analysis. The analysis unit can also prioritize analyzing highly relevant information based on the user's past design history. For example, the analysis unit prioritizes analyzing highly relevant information based on the user's past design history. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into the AI, and the AI ​​can adjust the order of analysis.

[0092] The generation unit estimates the user's emotions and adjusts the expression method of the generated design based on the estimated user emotions. The generation unit estimates the user's emotions using, for example, facial recognition technology. For example, the generation unit captures the user's facial expressions with a camera when they input photos or text information and estimates the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit records the user's voice when they input photos or text information and estimates the emotions using voice analysis technology. Furthermore, the generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit estimates emotions from text information entered by the user. By adjusting the expression method of the generated design based on the user's emotions, a more appropriate design can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user emotion data into the AI, which can then estimate the emotion and adjust the design's expression accordingly.

[0093] The generation unit adjusts the level of detail of the generated design based on the importance of the analysis results. For example, the generation unit generates a detailed design based on important analysis results. For example, the generation unit generates a detailed design based on analysis results that the user considers particularly important. The generation unit can also generate a concise design based on general analysis results. For example, the generation unit generates a concise design based on analysis results that the user does not consider particularly important. The generation unit can also generate a design that emphasizes specific elements based on the user's requests. For example, the generation unit generates a design that emphasizes elements that the user considers particularly important. By adjusting the level of detail of the generated design based on the importance of the analysis results, a more appropriate design can be provided. 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 input the importance data of the analysis results into the AI, and the AI ​​can adjust the level of detail of the generated design.

[0094] The generation unit applies different generation algorithms depending on the category of the analysis results during generation. For example, the generation unit applies a color generation algorithm to the analysis results related to color palettes. For example, the generation unit generates a design by applying a color generation algorithm based on the analysis results related to color palettes. The generation unit can also apply a spatial generation algorithm to the analysis results related to furniture arrangement. For example, the generation unit generates a design by applying a spatial generation algorithm based on the analysis results related to furniture arrangements. The generation unit can also apply a design generation algorithm to the analysis results related to decorative ideas. For example, the generation unit generates a design by applying a design generation algorithm based on the analysis results related to decorative ideas. By applying different generation algorithms depending on the category of the analysis results, a more appropriate design can be provided. 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 input the category data of the analysis results into the AI, and the AI ​​can apply different generation algorithms.

[0095] The generation unit estimates the user's emotions and adjusts the length of the generated design based on the estimated user emotions. The generation unit estimates the user's emotions using, for example, facial recognition technology. For example, the generation unit captures the user's facial expressions with a camera when they input photos or text information and estimates the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using speech analysis technology. For example, the generation unit records the user's voice when they input photos or text information and estimates the emotions using speech analysis technology. Furthermore, the generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit estimates emotions from text information entered by the user. This allows for the provision of more appropriate designs by adjusting the length of the generated design based on the user's emotions. Emotion estimation is achieved using, for example, 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-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user emotion data into the AI, which can then estimate the emotion and adjust the length of the design.

[0096] The generation unit determines the generation priority based on the submission timing of the analysis results. For example, the generation unit prioritizes generating designs based on the latest analysis results. For example, the generation unit prioritizes generating designs based on the analysis results recently submitted by the user. The generation unit can also determine the generation priority based on the deadline specified by the user. For example, the generation unit can determine the generation priority based on the deadline specified by the user. The generation unit can also adjust the generation priority to match the user's schedule. For example, the generation unit can adjust the generation priority based on the user's schedule. This allows for the provision of more appropriate designs by determining the generation priority based on the submission timing of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the analysis result submission timing data into the AI, which can then determine the generation priority.

[0097] The generation unit adjusts the generation order based on the relevance of the analysis results during generation. For example, the generation unit prioritizes generating designs based on the analysis results most relevant to the user's requests. For example, the generation unit prioritizes generating designs based on the analysis results that the user considers particularly important. The generation unit can also group highly relevant analysis results and generate designs. For example, the generation unit can group highly relevant analysis results and generate designs. The generation unit can also generate designs based on highly relevant analysis results based on the user's past design history. For example, the generation unit can generate designs based on highly relevant analysis results based on the user's past design history. By adjusting the generation order based on the relevance of the analysis results, a more appropriate design can be provided. 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 input the relevance data of the analysis results into the AI, which can then adjust the generation order.

[0098] The service provider estimates the user's emotions and adjusts the presentation of the design based on the estimated emotions. For example, the service provider estimates the user's emotions using facial recognition technology. For example, the service provider captures the user's facial expressions with a camera when they input photos or text information and estimates their emotions using an emotion estimation algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, the service provider records the user's voice when they input photos or text information and estimates their emotions using voice analysis technology. Furthermore, the service provider can also estimate the user's emotions using text analysis technology. For example, the service provider estimates emotions from text information entered by the user. By adjusting the presentation of the design based on the user's emotions, a more appropriate design can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into an AI, which can then estimate the emotion and adjust the design's expression accordingly.

[0099] The service provider adjusts the level of detail in the provided design based on its importance. For example, the service provider provides detailed explanations for important design proposals. For example, the service provider provides detailed explanations for design proposals that the user considers particularly important. The service provider can also provide concise explanations for general design proposals. For example, the service provider provides concise explanations for design proposals that the user does not consider particularly important. The service provider can also provide explanations that focus on specific elements based on the user's requests. For example, the service provider provides explanations that focus on elements that the user considers particularly important. By adjusting the level of detail in the provided design based on its importance, the service provider can provide more appropriate designs. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the importance data of the generated designs into the AI, and the AI ​​can adjust the level of detail in the provided design.

[0100] The service provider applies different service algorithms depending on the category of the generated design at the time of delivery. For example, the service provider applies a color service algorithm to design proposals related to color palettes. For example, the service provider applies a color service algorithm when providing design proposals related to color palettes. The service provider can also apply a spatial service algorithm to design proposals related to furniture arrangement. For example, the service provider applies a spatial service algorithm when providing design proposals related to furniture arrangement. The service provider can also apply a design service algorithm to design proposals related to decorative ideas. For example, the service provider applies a design service algorithm when providing design proposals related to decorative ideas. By applying different service algorithms depending on the category of the generated design, more appropriate designs can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category data of the generated designs into the AI, and the AI ​​can apply different service algorithms.

[0101] The service provider estimates the user's emotions and adjusts the length of the design provided based on the estimated emotions. The service provider estimates the user's emotions using, for example, facial recognition technology. For example, the service provider captures the user's facial expressions with a camera when they input photos or text information and estimates their emotions using an emotion estimation algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, the service provider records the user's voice when they input photos or text information and estimates their emotions using voice analysis technology. Furthermore, the service provider can also estimate the user's emotions using text analysis technology. For example, the service provider estimates emotions from the text information entered by the user. By adjusting the length of the design provided based on the user's emotions, a more appropriate design can be provided. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user emotion data into an AI, which can then estimate the emotion and adjust the length of the design accordingly.

[0102] The service provider determines the priority of deliveries based on the submission timing of the generated designs. For example, the service provider may prioritize the most recent design proposals. For example, the service provider may prioritize design proposals recently submitted by the user. The service provider may also determine the priority of deliveries based on deadlines specified by the user. For example, the service provider may determine the priority of deliveries based on deadlines specified by the user. The service provider may also adjust the priority of deliveries to match the user's schedule. For example, the service provider may adjust the priority of deliveries based on the user's schedule. This allows for the provision of more appropriate designs by determining the priority of deliveries based on the submission timing of the generated designs. Some or all of the above processes in the service provider may be performed using AI, or not using AI. For example, the service provider may input the submission timing data of the generated designs into an AI, which can then determine the priority of deliveries.

[0103] The delivery unit adjusts the order of delivery based on the relevance of the generated designs. For example, the delivery unit prioritizes providing design proposals that are most relevant to the user's requests. For example, the delivery unit prioritizes providing design proposals that the user considers particularly important. The delivery unit can also group and provide highly relevant design proposals. For example, the delivery unit can group and provide highly relevant design proposals. The delivery unit can also prioritize providing highly relevant design proposals based on the user's past design history. For example, the delivery unit can prioritize providing highly relevant design proposals based on the user's past design history. By adjusting the order of delivery based on the relevance of the generated designs, a more appropriate design can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance data of the generated designs into AI, and the AI ​​can adjust the order of delivery.

[0104] The learning unit estimates the user's emotions and selects training data based on the estimated user emotions. For example, the learning unit estimates the user's emotions using facial recognition technology. For example, the learning unit captures the user's facial expressions with a camera when they input photos or text information and estimates their emotions using an emotion estimation algorithm. The learning unit can also estimate the user's emotions using speech analysis technology. For example, the learning unit records the user's voice when they input photos or text information and estimates their emotions using speech analysis technology. Furthermore, the learning unit can also estimate the user's emotions using text analysis technology. For example, the learning unit estimates emotions from text information entered by the user. This allows for more appropriate learning by selecting training data based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into the AI, which can then estimate the emotion and select training data.

[0105] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and selects the optimal learning algorithm. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. For example, the learning unit adjusts the parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit improves the accuracy of the learning algorithm based on past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can optimize the learning algorithm.

[0106] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. The learning unit can estimate the user's emotions using, for example, facial recognition technology. For example, the learning unit can capture the user's facial expressions with a camera when they input photos or text information and estimate their emotions using an emotion estimation algorithm. The learning unit can also estimate the user's emotions using speech analysis technology. For example, the learning unit can record the user's voice when they input photos or text information and estimate their emotions using speech analysis technology. Furthermore, the learning unit can also estimate the user's emotions using text analysis technology. For example, the learning unit can estimate emotions from text information entered by the user. This allows for more appropriate learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input user emotion data into the AI, which can then estimate the emotion and adjust the learning frequency.

[0107] The learning unit weights the training data based on the submission timing of design proposals during training. For example, the learning unit weights the training data based on the most recent design proposal. For example, the learning unit weights the training data based on the design proposal recently submitted by the user. The learning unit can also weight the training data based on a deadline specified by the user. For example, the learning unit weights the training data based on a deadline specified by the user. The learning unit can also adjust the weighting of the training data to match the user's schedule. For example, the learning unit adjusts the weighting of the training data based on the user's schedule. This allows for more appropriate training by weighting the training data based on the submission timing of design proposals. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input design proposal submission timing data into the AI, and the AI ​​can weight the training data.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The reception unit can accept voice memos in addition to photos and text information provided by users. For example, a user can upload a photo of their room and input text information such as "I want a calm atmosphere," but can also convey detailed requests in a voice memo. The analysis unit can analyze the voice memo to understand the user's requests in more detail. For example, if a user says in a voice memo, "I want to hang a picture on this wall," the analysis unit can analyze that request, and the generation unit can suggest an appropriate placement of the picture. This makes it possible to propose designs that more accurately reflect the user's requests.

[0110] The service provider can not only learn from user feedback but also customize suggestions based on the user's behavioral history. For example, it can analyze what design suggestions the user has adopted in the past and which suggestions they have given high ratings to. The learning unit learns from this behavioral history and reflects it in future suggestions. For instance, if a user has previously preferred a natural interior style, the next suggestion can prioritize a natural style. This allows for suggestions that match the user's preferences, leading to increased satisfaction.

[0111] The design generation unit can also consider the room's lighting conditions when analyzing a photograph of the room. For example, the unit can identify how natural light enters the room and the placement of lighting fixtures from the photograph, and then propose designs based on that. For instance, the unit can suggest a bright color palette for rooms with plenty of natural light, and a design that makes extensive use of indirect lighting for rooms with less lighting. This makes it possible to propose designs that are optimal for the room's lighting conditions.

[0112] The design generation unit can also consider the user's lifestyle when understanding their requests. For example, if a user inputs a request such as "I want a relaxing space," the unit will consider not only that request but also the user's lifestyle information (e.g., whether they have pets or children). This allows the unit to create furniture arrangements and select materials that are considerate of pets and children. For instance, it can suggest furniture made from durable materials for households with pets. This enables the design to be tailored to the user's lifestyle.

[0113] The generation unit can also take the user's budget into consideration when automatically generating color palettes, furniture arrangements, and decoration ideas based on the analysis results. For example, the generation unit can propose the optimal design within the budget based on the budget information entered by the user. For instance, it can propose cost-effective furniture and decoration ideas for low budgets, and high-quality furniture and decoration ideas for high budgets. This makes it possible to propose designs that match the user's budget.

[0114] When providing generated design proposals to users, the system can estimate the user's emotions and adjust the presentation of the proposals based on those emotions. For example, the system can capture the user's facial expression as they review the proposal and use an emotion estimation algorithm to estimate their emotions. If the user shows positive emotions towards the proposal, the system can display the proposal as is; if they show negative emotions, it can change the presentation of the proposal and display it again. This enables flexible proposals that respond to the user's emotions.

[0115] The service provider can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, it can analyze the user's facial expressions and voice when they review a suggestion and display it when the user is relaxed. If the user is stressed, the display of the suggestion can be delayed. This allows the user to receive suggestions in the optimal state, improving their likelihood of acceptance.

[0116] The service provider can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, the service provider can analyze the user's facial expressions and voice when they review the suggestions. If the user shows positive emotions, it can display the suggestions as they are; if they show negative emotions, it can modify the suggestions and display them again. For instance, if the user shows negative emotions towards a suggested color palette, the service provider can suggest a different color palette. This enables flexible suggestions that respond to the user's emotions.

[0117] The service provider can estimate the user's emotions and adjust the level of detail in the suggestions based on those emotions. For example, the service provider can analyze the user's facial expressions and voice when they review the suggestions, displaying detailed suggestions if the user shows positive emotions and concise suggestions if they show negative emotions. This enables the provision of suggestions with an appropriate level of detail according to the user's emotions.

[0118] The service provider can estimate the user's emotions and adjust the order of suggestions based on those emotions. For example, the service provider can analyze the user's facial expressions and voice as they review the suggestions. If the user shows positive emotions, it can display the suggestions in the original order; if they show negative emotions, it can change the order of the suggestions and display them again. This enables flexible suggestions that respond to the user's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives photos and text information provided by the user. This information may include, for example, photos of the room, photos of the furniture, and text information about the interior design. The reception desk can receive photos of the room and text information such as "I want a calm atmosphere." Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses image analysis algorithms to analyze photos of the room and identify the room layout and existing furniture. It also uses text analysis algorithms to understand the user's requests. For example, based on the request for a "calm atmosphere," it selects a color palette and furniture style. Step 3: The generation unit automatically generates color palettes, furniture arrangements, and decorative ideas based on the information analyzed by the analysis unit. Based on the analysis results, the generation unit suggests a beige and brown color palette and arranges furniture to create a relaxing atmosphere. It also suggests decorative ideas such as houseplants and indirect lighting. Step 4: The provisioning unit provides the user with the design proposals generated by the generation unit. The provisioning unit displays the generated design proposals to the user, allowing the user to review the proposals and input any necessary modifications or additional requests.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives photos and text information provided by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates color palettes, furniture arrangements, and decoration ideas based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated design proposals to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns user feedback and reflects it in future proposals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives photos and text information provided by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates color palettes, furniture arrangements, and decoration ideas based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated design proposals to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns user feedback and reflects it in the next proposal. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives photos and text information provided by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates color palettes, furniture arrangements, and decoration ideas based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated design proposals to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns user feedback and reflects it in future proposals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives photos and text information provided by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates color palettes, furniture arrangements, and decoration ideas based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated design proposals to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns user feedback and reflects it in future proposals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A reception desk that accepts photos and text information provided by users, An analysis unit that analyzes the information received by the reception unit, A generation unit that automatically generates a color palette, furniture arrangement, and decorative ideas based on the information analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the user with the design proposals generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, It includes a learning unit that learns from user feedback and incorporates it into future suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Using image recognition technology, we analyze photos of the room to identify the room's layout and existing furniture. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Using text analysis technology to understand user requests The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the analysis results, it automatically generates color palettes, furniture arrangements, and decorative ideas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the generated design proposals to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, It provides an interface that anyone can use with simple operation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It is continuously updated and its accuracy improves. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving photos and text information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past design history and select the optimal submission method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving photos and text information, the system filters them based on the user's current interior style and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving photos and text 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 14) The aforementioned reception unit is When receiving photos and text information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the photographic and textual information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the way the generated design is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the level of detail of the generated output is adjusted based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, different generation algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and adjusts the length of the generated design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the generation priority is determined based on the timing of the submission of analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the generation order is adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we present the design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the design, adjust the level of detail based on the importance of the generated design. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the design, a different providing algorithm is applied depending on the category of the generated design. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we will prioritize the delivery based on the submission timing of the generated designs. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When delivering, the delivery order will be adjusted based on the relevance of the generated designs. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned learning unit, During training, the training data is weighted based on the timing of design proposal submissions. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts photos and text information provided by users, An analysis unit that analyzes the information received by the reception unit, A generation unit that automatically generates a color palette, furniture arrangement, and decorative ideas based on the information analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the user with the design proposals generated by the generation unit. A system characterized by the following features.

2. The aforementioned supply unit is, It includes a learning unit that learns from user feedback and incorporates it into future suggestions. The system according to feature 1.

3. The generating unit is Using image recognition technology, we analyze photos of the room to identify the room's layout and existing furniture. The system according to feature 1.

4. The generating unit is Using text analysis technology to understand user requests The system according to feature 1.

5. The generating unit is Based on the analysis results, it automatically generates color palettes, furniture arrangements, and decorative ideas. The system according to feature 1.

6. The aforementioned supply unit is, Provide the generated design proposals to the user. The system according to feature 1.

7. The aforementioned supply unit is, It provides an interface that anyone can use with simple operation. The system according to feature 1.

8. The generating unit is It is continuously updated and its accuracy improves. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving photos and text information based on those estimated emotions. The system according to feature 1.

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