System

Abstract

An object of a system according to an embodiment is to propose furniture and a layout that match a user's preference and a room and to realize an optimal arrangement.SOLUTION: A system includes an input unit, an analysis unit, a proposal unit, and an arrangement unit. The input unit inputs a user's preference and room information. The analysis unit analyzes the information input by the input unit. The proposal unit proposes furniture and a layout on the basis of an analysis result obtained by the analysis unit. The arrangement unit optimizes the arrangement of the furniture based on the furniture and the layout proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for users to select furniture and layouts that suit their preferences and rooms, preventing them from making optimal choices.

[0005] The system according to the embodiment aims to propose furniture and layouts that suit the user's preferences and rooms, and to realize optimal arrangements. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a proposal unit, and an arrangement unit. The input unit inputs user preferences and room information. The analysis unit analyzes the information input by the input unit. The proposal unit proposes furniture and layouts based on the analysis results obtained by the analysis unit. The arrangement unit optimizes the furniture arrangement based on the furniture and layouts proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose furniture and layouts that suit the user's preferences and the room, and can realize optimal arrangements. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI interior coordinator system according to an embodiment of the present invention proposes optimal furniture and layouts based on a user's preferences and room information, optimizing furniture placement. In the AI ​​interior coordinator system, a user inputs their preferences and room information, and the AI ​​analyzes the information to propose optimal furniture and layouts. For example, if a user inputs "I want a stylish room," the AI ​​proposes optimal furniture and layouts based on that request. The AI ​​also optimizes furniture placement based on the size and shape of the room. This allows users to easily select furniture that best suits their preferences and room. For example, a user inputs their preferences and room information. The user inputs the size, shape, and preferred style of the room. For example, the user might input "My room is 10 tatami mats in size, and I like a modern style." This information is input into the AI. The AI ​​then analyzes the input information. The AI ​​understands the user's preferences and room characteristics and proposes optimal furniture and layouts based on that. For example, if a user inputs "I want the room to look spacious," the AI ​​proposes furniture and layouts that will make the room look spacious based on that request. The AI ​​also optimizes furniture placement based on the size and shape of the room. For example, if a room is rectangular, the AI ​​will arrange the furniture to fit that shape and suggest a layout that will make the room appear larger. This allows users to easily choose furniture that best suits their preferences and their room. The AI ​​interior coordinator system can then suggest the best furniture and layout based on the user's preferences and room information, optimizing the furniture arrangement. For example, when a user inputs their preferences and room information, the AI ​​analyzes that information and suggests the best furniture and layout. The AI ​​can also optimize the furniture arrangement based on the size and shape of the room. This allows users to easily choose furniture that best suits their preferences and their room.

[0029] The AI ​​interior coordinator system according to the embodiment includes an input unit, an analysis unit, a proposal unit, and a layout unit. The input unit inputs a user's preferences and room information. The user's preferences include, but are not limited to, for example, color, design, and functionality. The room information includes, but are not limited to, for example, the size, shape, and use of the room. The input unit allows the user to input, for example, the size, shape, and preferred style of the room. The analysis unit analyzes the information input by the input unit to understand the user's preferences and room characteristics. The analysis is performed using, for example, but is not limited to, a data analysis method or algorithm. For example, the analysis unit analyzes the user's preferences and room characteristics based on the information input by the user. The proposal unit proposes optimal furniture and layouts based on the analysis results obtained by the analysis unit. The proposal is performed based on, for example, but is not limited to, the type of furniture and layout patterns to be proposed. For example, the proposal unit proposes furniture and layouts that match the user's preferences based on the analysis results. The arrangement unit optimizes the furniture arrangement to suit the size and shape of the room based on the furniture and layout proposed by the proposal unit. The arrangement is performed, for example, based on layout efficiency and effective use of space, but is not limited to such examples. For example, the arrangement unit optimizes the furniture arrangement to suit the size and shape of the room based on the proposed furniture and layout. In this way, the AI ​​interior coordinator system according to the embodiment can propose optimal furniture and layout based on the user's preferences and room information, and optimize the furniture arrangement.

[0030] The input unit can input information about the user's preferences and the size, shape, and style of the room. Examples of user preferences include, but are not limited to, color, design, and functionality. Examples of room sizes include, but are not limited to, square meters and cubic meters. Examples of room shapes include, but are not limited to, rectangular, square, and L-shaped. Examples of styles include, but are not limited to, modern, classic, and minimalist. The input unit can, for example, allow the user to input the size, shape, and preferred style of the room. This allows for more accurate suggestions by inputting the user's detailed preferences and room information. Some or all of the above-described processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the information input by the user to a generation AI, which can analyze the information and complete the input content.

[0031] The analysis unit analyzes the information input by the input unit to understand the user's preferences and room characteristics. The analysis is performed using, for example, a data analysis method or an algorithm, but is not limited to these examples. For example, the analysis unit analyzes the user's preferences and room characteristics based on the information input by the user. The user's preferences include, for example, but are not limited to, color, design, functionality, etc. The room characteristics include, for example, but are not limited to, the size, shape, and use of the room. This allows for understanding the user's preferences and room characteristics, enabling more appropriate suggestions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the information input by the user to a generation AI, which analyzes the information to understand the user's preferences and room characteristics.

[0032] The suggestion unit can suggest optimal furniture and layouts based on the analysis results obtained by the analysis unit. The suggestions are made based on, for example, but not limited to, the types of furniture and layout patterns to be suggested. For example, the suggestion unit suggests furniture and layouts that suit the user's preferences based on the analysis results. The analysis results include, for example, but not limited to, data trends and patterns. The optimal furniture and layouts include, for example, but not limited to, effective space utilization and design harmony. This allows suggestions that meet the user's needs by suggesting optimal furniture and layouts based on the analysis results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI, which then suggests optimal furniture and layouts.

[0033] The arrangement unit can optimize the furniture arrangement based on the furniture and layout proposed by the proposal unit to suit the size and shape of the room. The arrangement is performed, for example, based on arrangement efficiency and effective use of space, but is not limited to such examples. For example, the arrangement unit optimizes the furniture arrangement based on the proposed furniture and layout to suit the size and shape of the room. Room sizes include, for example, square meters and cubic meters, but are not limited to such examples. Room shapes include, for example, rectangles, squares, L-shapes, etc., but are not limited to such examples. Specific methods for adjusting the arrangement include, for example, furniture arrangement patterns and effective use of space, but are not limited to such examples. By optimizing the furniture arrangement to suit the size and shape of the room, the room can be made to appear larger. Some or all of the above-described processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can input the proposed furniture and layout to a generation AI, which can then optimize the furniture arrangement.

[0034] The suggestion unit can suggest furniture and layouts that indicate specific criteria for making a room appear larger, based on the user's requests. Examples of the user's requests include, but are not limited to, wanting the room to appear larger or a specific design. Examples of specific criteria include, but are not limited to, a sense of spaciousness and visual effects. The suggestion unit, for example, suggests furniture and layouts that make a room appear larger, based on the user's requests. This makes it possible to make suggestions for making a room appear larger in accordance with the user's requests. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's requests into a generation AI, which then suggests furniture and layouts that make the room appear larger.

[0035] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit automatically displays preferences and room information that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest information to be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history to a generation AI, which then suggests the optimal input method.

[0036] The input unit can customize input items based on the user's current living situation and areas of interest at the time of input. For example, if the user has just moved into a new home, the input unit can prioritize displaying input items related to the move. Furthermore, if the user is interested in a particular interior style, the input unit can also suggest input items related to that style. Furthermore, if the user changes their family composition, the input unit can customize the input items based on the change. This allows for more appropriate input by customizing the input items based on the user's living situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's living situation and areas of interest into a generation AI, which can then customize the input items.

[0037] The input unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user prefers voice input, the input unit can provide voice input preferentially. Furthermore, if the user prefers text input, the input unit can also provide text input preferentially. Furthermore, if the user prefers image input, the input unit can enable the user to input information by uploading an image. This enables more efficient input by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's input method to a generation AI, which can select the optimal input means.

[0038] During input, the input unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information. For example, if the user lives in a specific region, the input unit can prioritize displaying interior styles related to that region. Furthermore, if the user lives in a specific city, the input unit can also suggest input items based on the trends of that city. Furthermore, if the user lives in a specific country, the input unit can prioritize displaying input items based on the culture of that country. This makes it possible to provide more relevant input items by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to a generation AI, which can then prioritize displaying highly relevant input items.

[0039] The input unit can analyze the user's social media activity at the time of input and suggest related input items. The input unit can suggest related input items based on, for example, interior images shared by the user on social media. The input unit can also analyze the content of the user's social media posts and suggest related interior styles. The input unit can also suggest related input items by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant input items can be provided. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity to a generation AI, which can then suggest related input items.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting. The input unit can improve the input interface, for example, based on feedback provided by the user in the past. The input unit can also avoid input methods that the user has been dissatisfied with in the past and preferentially provide preferred input methods. The input unit can also analyze the user's past feedback and suggest an optimal input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the user's past feedback into a generation AI, which can then customize the input method.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. The analysis unit improves the accuracy of the analysis, for example, based on preferences and room information previously input by the user. The analysis unit can also perform a more accurate analysis by referring to the user's past selection history. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. In this way, by referring to the user's past data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past data into the generation AI, which can improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's input data. For example, if the data input by the user is related to interior style, the analysis unit can apply an analysis method specialized for that category. Furthermore, if the data input by the user is related to the size and shape of a room, the analysis unit can also apply an analysis method specialized for that category. Furthermore, if the data input by the user is related to furniture arrangement, the analysis unit can also apply an analysis method specialized for that category. By applying an analysis method according to the category of the user's input data, more appropriate analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI, which then applies an analysis method according to the category.

[0043] During analysis, the analysis unit can optimize the analysis results by taking into account the user's lifestyle rhythm. For example, if the user is a night owl, the analysis unit can provide analysis results that match that rhythm. Furthermore, if the user is a morning person, the analysis unit can also provide analysis results that match that rhythm. Furthermore, the analysis unit can suggest optimal furniture and layout based on the user's lifestyle rhythm. In this way, by taking the user's lifestyle rhythm into consideration, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's lifestyle rhythm into the generation AI, which can then optimize the analysis results.

[0044] The analysis unit can perform the analysis taking into account the user's geographical information. For example, if the user lives in a specific region, the analysis unit can perform the analysis taking into account the trends of that region. Furthermore, if the user lives in a specific city, the analysis unit can perform the analysis taking into account the culture of that city. Furthermore, if the user lives in a specific country, the analysis unit can perform the analysis taking into account the climate of that country. This allows for more appropriate analysis by taking into account the user's geographical information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's geographical information into a generation AI, and the generation AI can perform the analysis taking into account the geographical information.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the user. For example, the analysis unit can improve the accuracy of the analysis by referring to interior-related literature that the user has read in the past. The analysis unit can also improve the accuracy of the analysis based on design guidelines that the user has previously referred to. The analysis unit can also improve the accuracy of the analysis based on interior magazines that the user has previously viewed. In this way, the accuracy of the analysis is improved by referring to literature related to the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's literature related to the generation AI, and the generation AI can improve the accuracy of the analysis by referring to the related literature.

[0046] The analysis unit can perform the analysis taking into account the market value of the user. For example, if the user prefers expensive furniture, the analysis unit can perform the analysis taking into account its market value. Furthermore, if the user prefers affordable furniture, the analysis unit can perform the analysis taking into account its market value. Furthermore, if the user prefers a particular brand, the analysis unit can perform the analysis taking into account its market value. This enables more appropriate analysis by taking into account the user's market value. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's market value into the generation AI, and the generation AI can perform the analysis taking into account the market value.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the furniture and layout when making a suggestion. For example, the suggestion unit provides detailed suggestions for important furniture and layouts. The suggestion unit can also provide concise suggestions for less important furniture and layouts. The suggestion unit can also prioritize suggestions for more important furniture and layouts based on the user's request. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the furniture and layout. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the furniture and layout to a generation AI, which can then adjust the level of detail of the suggestion.

[0048] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the furniture or layout category. For example, when making a proposal regarding an interior style, the suggestion unit can apply an algorithm specialized for that category. Furthermore, when making a proposal regarding the size or shape of a room, the suggestion unit can apply an algorithm specialized for that category. Furthermore, when making a proposal regarding the arrangement of furniture, the suggestion unit can apply an algorithm specialized for that category. In this way, by applying a suggestion algorithm according to the furniture or layout category, more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the furniture or layout category to the generation AI, which can then apply a suggestion algorithm according to the category.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit can provide similar proposals based on proposals that the user has accepted in the past. The suggestion unit can also avoid proposals that the user has rejected in the past and provide more appropriate proposals. The suggestion unit can also analyze the user's past proposal results and apply an optimal suggestion algorithm. This improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past proposal results into the generation AI, which can improve the accuracy of the proposal.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposals based on the time of submission of the furniture and layout. For example, when the user is in a hurry, the suggestion unit can prioritize the most important proposals. The suggestion unit can also provide detailed proposals when the user has time to spare. The suggestion unit can also provide proposals according to the time of submission based on the user's request. This enables more appropriate proposals to be made by determining the priority of the proposals based on the time of submission of the furniture and layout. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of submission to the generation AI, and the generation AI can determine the priority of the proposals based on the time of submission.

[0051] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the furniture and layout. For example, the suggestion unit can first propose important furniture and layouts and postpone less relevant ones. The suggestion unit can also prioritize highly relevant proposals based on a user's request. The suggestion unit can also analyze the relevance of the furniture and layouts and provide proposals in an optimal order. This enables more appropriate proposals by adjusting the order of proposals based on the relevance of the furniture and layouts. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the relevance of the furniture and layouts to a generation AI, which can then adjust the order of proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user has expertise in interior design, the suggestion unit can provide a proposal that uses a lot of technical terms. Furthermore, if the user is a beginner in interior design, the suggestion unit can provide a proposal that explains things in simple terms. Furthermore, the suggestion unit can analyze the user's level of expertise and provide a proposal using optimal wording. This allows for more appropriate proposals by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terms.

[0053] At the time of placement, the placement unit can analyze the user's past placement history and select the optimal placement method. The placement unit, for example, suggests the optimal placement method based on placement methods that the user has preferred in the past. The placement unit can also avoid placement methods that the user has avoided in the past and suggest a more appropriate placement method. The placement unit can also analyze the user's past placement history and select the optimal placement method. In this way, the optimal placement method can be selected by analyzing the user's past placement history. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's past placement history into a generation AI, which can select the optimal placement method.

[0054] The placement unit can customize the placement means based on the user's current living situation at the time of placement. For example, if the user has just moved to a new home, the placement unit can propose a placement means that suits the situation. Furthermore, if the user has changed their family composition, the placement unit can also customize the placement means based on the change. Furthermore, the placement unit can also propose an optimal placement means based on the user's living situation. This enables more appropriate placement by customizing the placement means based on the user's current living situation. Some or all of the above-mentioned processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's living situation into a generation AI, which can then customize the placement means.

[0055] The placement unit can improve the placement method by reflecting user feedback during placement. The placement unit improves the placement method, for example, based on feedback provided by the user in the past. The placement unit can also avoid placement methods that the user has been dissatisfied with in the past and provide a preferred placement method. The placement unit can also analyze the user's past feedback and propose an optimal placement method. In this way, a more appropriate placement method can be provided by reflecting the user's feedback. Some or all of the above-mentioned processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input user feedback into a generation AI, which can then improve the placement method.

[0056] The placement unit can select the optimal placement method by taking into account the user's geographical location information when placing. For example, if the user lives in a specific area, the placement unit can prioritize displaying placement methods related to that area. Furthermore, if the user lives in a specific city, the placement unit can also suggest placement methods based on the trends of that city. Furthermore, if the user lives in a specific country, the placement unit can prioritize displaying placement methods based on the culture of that country. This makes it possible to provide a more appropriate placement method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's geographical location information into a generation AI, which can select the optimal placement method.

[0057] At the time of placement, the placement unit can analyze the user's social media activity and suggest placement methods. For example, the placement unit can suggest related placement methods based on interior images shared by the user on social media. The placement unit can also analyze the content of the user's social media posts and suggest related placement methods. The placement unit can also suggest related placement methods by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate placement methods can be provided. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's social media activity into a generation AI, which can then suggest related placement methods.

[0058] The placement unit can customize the placement method by reflecting the user's past feedback during placement. The placement unit customizes the placement method based on, for example, feedback provided by the user in the past. The placement unit can also avoid placement methods that the user has been dissatisfied with in the past and provide a preferred placement method. The placement unit can also analyze the user's past feedback and propose an optimal placement method. In this way, a more appropriate placement method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's feedback into a generation AI, which can then customize the placement method.

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

[0060] The input unit can input the user's health condition in addition to their preferences and room information. For example, if the user has allergies, it can suggest furniture and materials that are allergy-friendly. Also, if the user has a specific health problem, it can suggest furniture and layouts that address that problem. This makes it possible to suggest optimal interior designs based on the user's health condition. Furthermore, the input unit can input the user's health condition into the generation AI, which can then suggest optimal furniture and layouts based on the user's health condition.

[0061] The analysis unit can analyze the user's lifestyle in addition to the user's preferences and room characteristics. For example, if the user likes outdoor activities, it can suggest furniture and layouts that suit that lifestyle. Also, if the user has a pet, it can suggest furniture and layouts that are pet-friendly. This makes it possible to suggest optimal interior designs that suit the user's lifestyle. Furthermore, the analysis unit can input the user's lifestyle into the generation AI, which can then suggest optimal furniture and layouts based on the lifestyle.

[0062] In addition to the proposed furniture and layout, the placement unit can optimize the placement by taking into account the user's lifestyle. For example, if the user is a night owl, the placement unit can suggest a placement that suits that rhythm. Also, if the user is a morning person, the placement unit can suggest a placement that suits that rhythm. This makes it possible to create an optimal placement that suits the user's lifestyle. Furthermore, the placement unit can input the user's lifestyle into the generation AI, which can then suggest an optimal placement based on the lifestyle.

[0063] The analysis unit can analyze the user's hobbies and interests in addition to the user's preferences and room characteristics. For example, if the user likes music, it can suggest furniture and layouts that match that hobby. Also, if the user likes reading, it can suggest furniture and layouts that are suitable for reading. This makes it possible to suggest optimal interior designs that match the user's hobbies and interests. Furthermore, the analysis unit can input the user's hobbies and interests into the generation AI, which can then suggest optimal furniture and layouts based on the hobbies and interests.

[0064] In addition to the proposed furniture and layout, the placement unit can optimize the placement by taking into account the user's energy level. For example, if the user is tired, it can suggest a relaxing placement. Also, if the user is energetic, it can suggest an active placement. This enables the optimal placement according to the user's energy level. Furthermore, the placement unit can input the user's energy level into the generation AI, which can then suggest the optimal placement based on the energy level.

[0065] In addition to the proposed furniture and layout, the placement unit can optimize the placement by taking into account the user's family composition. For example, if the user has children, it can suggest placement that takes children into consideration. Also, if the user lives with elderly people, it can suggest placement that takes the elderly into consideration. This makes it possible to create an optimal placement according to the user's family composition. Furthermore, the placement unit can input the user's family composition into the generation AI, which can then suggest an optimal placement based on the family composition.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The input unit inputs the user's preferences and room information. The user's preferences include color, design, functionality, etc., and the room information includes the size, shape, and use of the room. For example, the user can input the size, shape, and preferred style of the room. Step 2: The analysis unit analyzes the information input by the input unit to understand the user's preferences and room characteristics. The analysis is performed using data analysis methods and algorithms. For example, the user's preferences and room characteristics are analyzed based on the information input by the user. Step 3: The suggestion unit proposes optimal furniture and layouts based on the analysis results obtained by the analysis unit. The proposals are made based on the type of furniture and layout patterns to be proposed. For example, based on the analysis results, furniture and layouts that suit the user's preferences are proposed. Step 4: The placement unit optimizes the furniture placement based on the furniture and layout proposed by the proposal unit, according to the size and shape of the room. The placement is performed based on the efficiency of the placement and the effective use of space. For example, the placement of the furniture is optimized based on the proposed furniture and layout, according to the size and shape of the room.

[0068] (Example 2) An AI interior coordinator system according to an embodiment of the present invention proposes optimal furniture and layouts based on a user's preferences and room information, optimizing furniture placement. In the AI ​​interior coordinator system, a user inputs their preferences and room information, and the AI ​​analyzes the information to propose optimal furniture and layouts. For example, if a user inputs "I want a stylish room," the AI ​​proposes optimal furniture and layouts based on that request. The AI ​​also optimizes furniture placement based on the size and shape of the room. This allows users to easily select furniture that best suits their preferences and room. For example, a user inputs their preferences and room information. The user inputs the size, shape, and preferred style of the room. For example, the user might input "My room is 10 tatami mats in size, and I like a modern style." This information is input into the AI. The AI ​​then analyzes the input information. The AI ​​understands the user's preferences and room characteristics and proposes optimal furniture and layouts based on that. For example, if a user inputs "I want the room to look spacious," the AI ​​proposes furniture and layouts that will make the room look spacious based on that request. The AI ​​also optimizes furniture placement based on the size and shape of the room. For example, if a room is rectangular, the AI ​​will arrange the furniture to fit that shape and suggest a layout that will make the room appear larger. This allows users to easily choose furniture that best suits their preferences and their room. The AI ​​interior coordinator system can then suggest the best furniture and layout based on the user's preferences and room information, optimizing the furniture arrangement. For example, when a user inputs their preferences and room information, the AI ​​analyzes that information and suggests the best furniture and layout. The AI ​​can also optimize the furniture arrangement based on the size and shape of the room. This allows users to easily choose furniture that best suits their preferences and their room.

[0069] The AI ​​interior coordinator system according to the embodiment includes an input unit, an analysis unit, a proposal unit, and a layout unit. The input unit inputs a user's preferences and room information. The user's preferences include, but are not limited to, for example, color, design, and functionality. The room information includes, but are not limited to, for example, the size, shape, and use of the room. The input unit allows the user to input, for example, the size, shape, and preferred style of the room. The analysis unit analyzes the information input by the input unit to understand the user's preferences and room characteristics. The analysis is performed using, for example, but is not limited to, a data analysis method or algorithm. For example, the analysis unit analyzes the user's preferences and room characteristics based on the information input by the user. The proposal unit proposes optimal furniture and layouts based on the analysis results obtained by the analysis unit. The proposal is performed based on, for example, but is not limited to, the type of furniture and layout patterns to be proposed. For example, the proposal unit proposes furniture and layouts that match the user's preferences based on the analysis results. The arrangement unit optimizes the furniture arrangement to suit the size and shape of the room based on the furniture and layout proposed by the proposal unit. The arrangement is performed, for example, based on layout efficiency and effective use of space, but is not limited to such examples. For example, the arrangement unit optimizes the furniture arrangement to suit the size and shape of the room based on the proposed furniture and layout. In this way, the AI ​​interior coordinator system according to the embodiment can propose optimal furniture and layout based on the user's preferences and room information, and optimize the furniture arrangement.

[0070] The input unit can input information about the user's preferences and the size, shape, and style of the room. Examples of user preferences include, but are not limited to, color, design, and functionality. Examples of room sizes include, but are not limited to, square meters and cubic meters. Examples of room shapes include, but are not limited to, rectangular, square, and L-shaped. Examples of styles include, but are not limited to, modern, classic, and minimalist. The input unit can, for example, allow the user to input the size, shape, and preferred style of the room. This allows for more accurate suggestions by inputting the user's detailed preferences and room information. Some or all of the above-described processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the information input by the user to a generation AI, which can analyze the information and complete the input content.

[0071] The analysis unit analyzes the information input by the input unit to understand the user's preferences and room characteristics. The analysis is performed using, for example, a data analysis method or an algorithm, but is not limited to these examples. For example, the analysis unit analyzes the user's preferences and room characteristics based on the information input by the user. The user's preferences include, for example, but are not limited to, color, design, functionality, etc. The room characteristics include, for example, but are not limited to, the size, shape, and use of the room. This allows for understanding the user's preferences and room characteristics, enabling more appropriate suggestions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the information input by the user to a generation AI, which analyzes the information to understand the user's preferences and room characteristics.

[0072] The suggestion unit can suggest optimal furniture and layouts based on the analysis results obtained by the analysis unit. The suggestions are made based on, for example, but not limited to, the types of furniture and layout patterns to be suggested. For example, the suggestion unit suggests furniture and layouts that suit the user's preferences based on the analysis results. The analysis results include, for example, but not limited to, data trends and patterns. The optimal furniture and layouts include, for example, but not limited to, effective space utilization and design harmony. This allows suggestions that meet the user's needs by suggesting optimal furniture and layouts based on the analysis results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI, which then suggests optimal furniture and layouts.

[0073] The arrangement unit can optimize the furniture arrangement based on the furniture and layout proposed by the proposal unit to suit the size and shape of the room. The arrangement is performed, for example, based on arrangement efficiency and effective use of space, but is not limited to such examples. For example, the arrangement unit optimizes the furniture arrangement based on the proposed furniture and layout to suit the size and shape of the room. Room sizes include, for example, square meters and cubic meters, but are not limited to such examples. Room shapes include, for example, rectangles, squares, L-shapes, etc., but are not limited to such examples. Specific methods for adjusting the arrangement include, for example, furniture arrangement patterns and effective use of space, but are not limited to such examples. By optimizing the furniture arrangement to suit the size and shape of the room, the room can be made to appear larger. Some or all of the above-described processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can input the proposed furniture and layout to a generation AI, which can then optimize the furniture arrangement.

[0074] The suggestion unit can suggest furniture and layouts that indicate specific criteria for making a room appear larger, based on the user's requests. Examples of the user's requests include, but are not limited to, wanting the room to appear larger or a specific design. Examples of specific criteria include, but are not limited to, a sense of spaciousness and visual effects. The suggestion unit, for example, suggests furniture and layouts that make a room appear larger, based on the user's requests. This makes it possible to make suggestions for making a room appear larger in accordance with the user's requests. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's requests into a generation AI, which then suggests furniture and layouts that make the room appear larger.

[0075] The input unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, when the user is stressed, the input unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the input unit can prioritize voice input to enable quick information input. This allows for a more comfortable input experience by adjusting the display method of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's emotions into a generation AI, which can then estimate the emotions and adjust the display method of the input interface.

[0076] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit automatically displays preferences and room information that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest information to be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history to a generation AI, which then suggests the optimal input method.

[0077] The input unit can customize input items based on the user's current living situation and areas of interest at the time of input. For example, if the user has just moved into a new home, the input unit can prioritize displaying input items related to the move. Furthermore, if the user is interested in a particular interior style, the input unit can also suggest input items related to that style. Furthermore, if the user changes their family composition, the input unit can customize the input items based on the change. This allows for more appropriate input by customizing the input items based on the user's living situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's living situation and areas of interest into a generation AI, which can then customize the input items.

[0078] The input unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user prefers voice input, the input unit can provide voice input preferentially. Furthermore, if the user prefers text input, the input unit can also provide text input preferentially. Furthermore, if the user prefers image input, the input unit can enable the user to input information by uploading an image. This enables more efficient input by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's input method to a generation AI, which can select the optimal input means.

[0079] The input unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, when the user is stressed, the input unit can prioritize displaying important input items and postpone displaying other items. Furthermore, when the user is relaxed, the input unit can prioritize displaying detailed input items. Furthermore, when the user is in a hurry, the input unit can display the most important input items first. This allows for more appropriate input by prioritizing input items according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's emotions into a generation AI, which can then estimate the emotions and prioritize the input items.

[0080] During input, the input unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information. For example, if the user lives in a specific region, the input unit can prioritize displaying interior styles related to that region. Furthermore, if the user lives in a specific city, the input unit can also suggest input items based on the trends of that city. Furthermore, if the user lives in a specific country, the input unit can prioritize displaying input items based on the culture of that country. This makes it possible to provide more relevant input items by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to a generation AI, which can then prioritize displaying highly relevant input items.

[0081] The input unit can analyze the user's social media activity at the time of input and suggest related input items. The input unit can suggest related input items based on, for example, interior images shared by the user on social media. The input unit can also analyze the content of the user's social media posts and suggest related interior styles. The input unit can also suggest related input items by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant input items can be provided. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity to a generation AI, which can then suggest related input items.

[0082] The input unit can customize the input method by reflecting the user's past feedback when inputting. The input unit can improve the input interface, for example, based on feedback provided by the user in the past. The input unit can also avoid input methods that the user has been dissatisfied with in the past and preferentially provide preferred input methods. The input unit can also analyze the user's past feedback and suggest an optimal input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the user's past feedback into a generation AI, which can then customize the input method.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more suggestions. If the user is in a hurry, the analysis unit can also perform a quick analysis and prioritize the most important suggestions. If the user is stressed, the analysis unit can perform a simple analysis and provide simple suggestions. This enables more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotions into the generation AI, which can then estimate the emotions and adjust the analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. The analysis unit improves the accuracy of the analysis, for example, based on preferences and room information previously input by the user. The analysis unit can also perform a more accurate analysis by referring to the user's past selection history. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. In this way, by referring to the user's past data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past data into the generation AI, which can improve the accuracy of the analysis.

[0085] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's input data. For example, if the data input by the user is related to interior style, the analysis unit can apply an analysis method specialized for that category. Furthermore, if the data input by the user is related to the size and shape of a room, the analysis unit can also apply an analysis method specialized for that category. Furthermore, if the data input by the user is related to furniture arrangement, the analysis unit can also apply an analysis method specialized for that category. By applying an analysis method according to the category of the user's input data, more appropriate analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI, which then applies an analysis method according to the category.

[0086] During analysis, the analysis unit can optimize the analysis results by taking into account the user's lifestyle rhythm. For example, if the user is a night owl, the analysis unit can provide analysis results that match that rhythm. Furthermore, if the user is a morning person, the analysis unit can also provide analysis results that match that rhythm. Furthermore, the analysis unit can suggest optimal furniture and layout based on the user's lifestyle rhythm. In this way, by taking the user's lifestyle rhythm into consideration, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's lifestyle rhythm into the generation AI, which can then optimize the analysis results.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotions into the generation AI, which can then estimate the emotions and adjust the display method of the analysis results.

[0088] The analysis unit can perform the analysis taking into account the user's geographical information. For example, if the user lives in a specific region, the analysis unit can perform the analysis taking into account the trends of that region. Furthermore, if the user lives in a specific city, the analysis unit can perform the analysis taking into account the culture of that city. Furthermore, if the user lives in a specific country, the analysis unit can perform the analysis taking into account the climate of that country. This allows for more appropriate analysis by taking into account the user's geographical information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's geographical information into a generation AI, and the generation AI can perform the analysis taking into account the geographical information.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the user. For example, the analysis unit can improve the accuracy of the analysis by referring to interior-related literature that the user has read in the past. The analysis unit can also improve the accuracy of the analysis based on design guidelines that the user has previously referred to. The analysis unit can also improve the accuracy of the analysis based on interior magazines that the user has previously viewed. In this way, the accuracy of the analysis is improved by referring to literature related to the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's literature related to the generation AI, and the generation AI can improve the accuracy of the analysis by referring to the related literature.

[0090] The analysis unit can perform the analysis taking into account the market value of the user. For example, if the user prefers expensive furniture, the analysis unit can perform the analysis taking into account its market value. Furthermore, if the user prefers affordable furniture, the analysis unit can perform the analysis taking into account its market value. Furthermore, if the user prefers a particular brand, the analysis unit can perform the analysis taking into account its market value. This enables more appropriate analysis by taking into account the user's market value. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's market value into the generation AI, and the generation AI can perform the analysis taking into account the market value.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This enables more appropriate suggestions by adjusting the way suggestions are expressed according to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotions into the generation AI, which can then estimate the emotions and adjust the way suggestions are expressed.

[0092] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the furniture and layout when making a suggestion. For example, the suggestion unit provides detailed suggestions for important furniture and layouts. The suggestion unit can also provide concise suggestions for less important furniture and layouts. The suggestion unit can also prioritize suggestions for more important furniture and layouts based on the user's request. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the furniture and layout. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the furniture and layout to a generation AI, which can then adjust the level of detail of the suggestion.

[0093] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the furniture or layout category. For example, when making a proposal regarding an interior style, the suggestion unit can apply an algorithm specialized for that category. Furthermore, when making a proposal regarding the size or shape of a room, the suggestion unit can apply an algorithm specialized for that category. Furthermore, when making a proposal regarding the arrangement of furniture, the suggestion unit can apply an algorithm specialized for that category. In this way, by applying a suggestion algorithm according to the furniture or layout category, more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the furniture or layout category to the generation AI, which can then apply a suggestion algorithm according to the category.

[0094] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit can provide similar proposals based on proposals that the user has accepted in the past. The suggestion unit can also avoid proposals that the user has rejected in the past and provide more appropriate proposals. The suggestion unit can also analyze the user's past proposal results and apply an optimal suggestion algorithm. This improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past proposal results into the generation AI, which can improve the accuracy of the proposal.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can provide quick, concise suggestions. This allows for more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotions into the generation AI, which can then estimate the emotions and adjust the length of the suggestions.

[0096] When making a proposal, the suggestion unit can determine the priority of the proposals based on the time of submission of the furniture and layout. For example, when the user is in a hurry, the suggestion unit can prioritize the most important proposals. The suggestion unit can also provide detailed proposals when the user has time to spare. The suggestion unit can also provide proposals according to the time of submission based on the user's request. This enables more appropriate proposals to be made by determining the priority of the proposals based on the time of submission of the furniture and layout. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of submission to the generation AI, and the generation AI can determine the priority of the proposals based on the time of submission.

[0097] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the furniture and layout. For example, the suggestion unit can first propose important furniture and layouts and postpone less relevant ones. The suggestion unit can also prioritize highly relevant proposals based on a user's request. The suggestion unit can also analyze the relevance of the furniture and layouts and provide proposals in an optimal order. This enables more appropriate proposals by adjusting the order of proposals based on the relevance of the furniture and layouts. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the relevance of the furniture and layouts to a generation AI, which can then adjust the order of proposals.

[0098] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user has expertise in interior design, the suggestion unit can provide a proposal that uses a lot of technical terms. Furthermore, if the user is a beginner in interior design, the suggestion unit can provide a proposal that explains things in simple terms. Furthermore, the suggestion unit can analyze the user's level of expertise and provide a proposal using optimal wording. This allows for more appropriate proposals by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terms.

[0099] The placement unit can estimate the user's emotions and adjust the placement method based on the estimated user's emotions. For example, the placement unit can provide a detailed placement method when the user is relaxed. The placement unit can also provide a concise placement method when the user is in a hurry. The placement unit can also provide a simple placement method when the user is stressed. This allows for more appropriate placement by adjusting the placement method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 processing in the placement unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the placement unit can input the user's emotions into the generation AI, which can then estimate the emotions and adjust the placement method.

[0100] At the time of placement, the placement unit can analyze the user's past placement history and select the optimal placement method. The placement unit, for example, suggests the optimal placement method based on placement methods that the user has preferred in the past. The placement unit can also avoid placement methods that the user has avoided in the past and suggest a more appropriate placement method. The placement unit can also analyze the user's past placement history and select the optimal placement method. In this way, the optimal placement method can be selected by analyzing the user's past placement history. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's past placement history into a generation AI, which can select the optimal placement method.

[0101] The placement unit can customize the placement means based on the user's current living situation at the time of placement. For example, if the user has just moved to a new home, the placement unit can propose a placement means that suits the situation. Furthermore, if the user has changed their family composition, the placement unit can also customize the placement means based on the change. Furthermore, the placement unit can also propose an optimal placement means based on the user's living situation. This enables more appropriate placement by customizing the placement means based on the user's current living situation. Some or all of the above-mentioned processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's living situation into a generation AI, which can then customize the placement means.

[0102] The placement unit can improve the placement method by reflecting user feedback during placement. The placement unit improves the placement method, for example, based on feedback provided by the user in the past. The placement unit can also avoid placement methods that the user has been dissatisfied with in the past and provide a preferred placement method. The placement unit can also analyze the user's past feedback and propose an optimal placement method. In this way, a more appropriate placement method can be provided by reflecting the user's feedback. Some or all of the above-mentioned processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input user feedback into a generation AI, which can then improve the placement method.

[0103] The placement unit can estimate the user's emotions and determine placement priorities based on the estimated user emotions. For example, when the user is feeling stressed, the placement unit can prioritize displaying important placement items and postpone displaying other items. Furthermore, when the user is relaxed, the placement unit can prioritize displaying detailed placement items. Furthermore, when the user is in a hurry, the placement unit can display the most important placement items first. This enables more appropriate placement by determining placement priorities according to the user's emotions. Estimation of emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the placement unit may be performed using AI, or may be performed without AI. For example, the placement unit can input the user's emotions into the generation AI, which can then estimate the emotions and determine placement priorities.

[0104] The placement unit can select the optimal placement method by taking into account the user's geographical location information when placing. For example, if the user lives in a specific area, the placement unit can prioritize displaying placement methods related to that area. Furthermore, if the user lives in a specific city, the placement unit can also suggest placement methods based on the trends of that city. Furthermore, if the user lives in a specific country, the placement unit can prioritize displaying placement methods based on the culture of that country. This makes it possible to provide a more appropriate placement method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's geographical location information into a generation AI, which can select the optimal placement method.

[0105] At the time of placement, the placement unit can analyze the user's social media activity and suggest placement methods. For example, the placement unit can suggest related placement methods based on interior images shared by the user on social media. The placement unit can also analyze the content of the user's social media posts and suggest related placement methods. The placement unit can also suggest related placement methods by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate placement methods can be provided. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's social media activity into a generation AI, which can then suggest related placement methods.

[0106] The placement unit can customize the placement method by reflecting the user's past feedback during placement. The placement unit customizes the placement method based on, for example, feedback provided by the user in the past. The placement unit can also avoid placement methods that the user has been dissatisfied with in the past and provide a preferred placement method. The placement unit can also analyze the user's past feedback and propose an optimal placement method. In this way, a more appropriate placement method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's feedback into a generation AI, which can then customize the placement method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned input unit, analysis unit, proposal unit, and arrangement unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input user preferences and room information using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes optimal furniture and layout based on the analysis results. The arrangement unit is realized by the control unit 46A of the smart device 14 and optimizes furniture arrangement based on the proposed furniture and layout. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, suggestion unit, and arrangement unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input user preferences and room information using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal furniture and layout based on the analysis results. The arrangement unit is realized by the control unit 46A of the smart glasses 214 and optimizes furniture arrangement based on the suggested furniture and layout. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, proposal unit, and placement unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit can input user preferences and room information using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes optimal furniture and layout based on the analysis results. The placement unit is realized by the control unit 46A of the headset type terminal 314 and optimizes furniture placement based on the proposed furniture and layout. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, proposal unit, and placement unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input the user's preferences and room information using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes optimal furniture and layout based on the analysis results. The placement unit is realized by the control unit 46A of the robot 414 and optimizes the furniture placement based on the proposed furniture and layout.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The input unit can input the user's health condition in addition to their preferences and room information. For example, if the user has allergies, it can suggest furniture and materials that are allergy-friendly. Also, if the user has a specific health problem, it can suggest furniture and layouts that address that problem. This makes it possible to suggest optimal interior designs based on the user's health condition. Furthermore, the input unit can input the user's health condition into the generation AI, which can then suggest optimal furniture and layouts based on the user's health condition.

[0109] The analysis unit can analyze the user's lifestyle in addition to the user's preferences and room characteristics. For example, if the user likes outdoor activities, it can suggest furniture and layouts that suit that lifestyle. Also, if the user has a pet, it can suggest furniture and layouts that are pet-friendly. This makes it possible to suggest optimal interior designs that suit the user's lifestyle. Furthermore, the analysis unit can input the user's lifestyle into the generation AI, which can then suggest optimal furniture and layouts based on the lifestyle.

[0110] The suggestion unit can estimate the user's emotions based on the analysis results and adjust the content of the suggestions based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing furniture and layouts. Alternatively, if the user is excited, it can suggest furniture and layouts with an energetic design. This makes it possible to suggest optimal interior design based on the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The suggestion unit inputs the user's emotions into the generation AI, which can then estimate the emotions and adjust the content of the suggestions.

[0111] In addition to the proposed furniture and layout, the placement unit can optimize the placement by taking into account the user's lifestyle. For example, if the user is a night owl, the placement unit can suggest a placement that suits that rhythm. Also, if the user is a morning person, the placement unit can suggest a placement that suits that rhythm. This makes it possible to create an optimal placement that suits the user's lifestyle. Furthermore, the placement unit can input the user's lifestyle into the generation AI, which can then suggest an optimal placement based on the lifestyle.

[0112] The input unit can estimate the user's emotions and adjust the color and design of the input interface based on the estimated emotions. For example, if the user is relaxed, it can provide an interface with calm colors. On the other hand, if the user is excited, it can provide an interface with bright colors. This makes it possible to provide a comfortable input experience that matches the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The input unit inputs the user's emotions into the generative AI, which can then estimate the emotions and adjust the color and design of the input interface.

[0113] The analysis unit can analyze the user's hobbies and interests in addition to the user's preferences and room characteristics. For example, if the user likes music, it can suggest furniture and layouts that match that hobby. Also, if the user likes reading, it can suggest furniture and layouts that are suitable for reading. This makes it possible to suggest optimal interior designs that match the user's hobbies and interests. Furthermore, the analysis unit can input the user's hobbies and interests into the generation AI, which can then suggest optimal furniture and layouts based on the hobbies and interests.

[0114] The suggestion unit can estimate the user's emotions based on the analysis results and adjust the timing of suggestions based on the estimated emotions. For example, if the user is relaxed, detailed suggestions can be provided. On the other hand, if the user is in a hurry, brief suggestions can be provided. This enables optimal timing of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The suggestion unit inputs the user's emotions into the generation AI, which can estimate the emotions and adjust the timing of suggestions.

[0115] In addition to the proposed furniture and layout, the placement unit can optimize the placement by taking into account the user's energy level. For example, if the user is tired, it can suggest a relaxing placement. Also, if the user is energetic, it can suggest an active placement. This enables the optimal placement according to the user's energy level. Furthermore, the placement unit can input the user's energy level into the generation AI, which can then suggest the optimal placement based on the energy level.

[0116] The suggestion unit can estimate the user's emotions based on the analysis results and adjust the order of suggestions based on the estimated emotions. For example, if the user is nervous, important suggestions can be provided first. Also, if the user is relaxed, detailed suggestions can be postponed. This enables the optimal order of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The suggestion unit inputs the user's emotions into the generation AI, which can then estimate the emotions and adjust the order of suggestions.

[0117] In addition to the proposed furniture and layout, the placement unit can optimize the placement by taking into account the user's family composition. For example, if the user has children, it can suggest placement that takes children into consideration. Also, if the user lives with elderly people, it can suggest placement that takes the elderly into consideration. This makes it possible to create an optimal placement according to the user's family composition. Furthermore, the placement unit can input the user's family composition into the generation AI, which can then suggest an optimal placement based on the family composition.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The input unit inputs the user's preferences and room information. The user's preferences include color, design, functionality, etc., and the room information includes the size, shape, and use of the room. For example, the user can input the size, shape, and preferred style of the room. Step 2: The analysis unit analyzes the information input by the input unit to understand the user's preferences and room characteristics. The analysis is performed using data analysis methods and algorithms. For example, the user's preferences and room characteristics are analyzed based on the information input by the user. Step 3: The suggestion unit proposes optimal furniture and layouts based on the analysis results obtained by the analysis unit. The proposals are made based on the type of furniture and layout patterns to be proposed. For example, based on the analysis results, furniture and layouts that suit the user's preferences are proposed. Step 4: The placement unit optimizes the furniture placement based on the furniture and layout proposed by the proposal unit, according to the size and shape of the room. The placement is performed based on the efficiency of the placement and the effective use of space. For example, the placement of the furniture is optimized based on the proposed furniture and layout, according to the size and shape of the room.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting user preferences and room information; an analysis unit that analyzes the information input by the input unit; a proposal unit that proposes furniture and layouts based on the analysis results obtained by the analysis unit; an arrangement unit that optimizes furniture arrangement based on the furniture and layout proposed by the proposal unit; Equipped with A system characterized by:

2. The input unit Enter information about your preferences and the size, shape, and style of your room 2. The system of claim 1.

3. The analysis unit Analyzing the information input by the input unit to understand the user's preferences and room characteristics 2. The system of claim 1.

4. The proposal unit Based on the analysis results obtained by the analysis unit, the system proposes optimal furniture and layout.

2. The system of claim 1.

5. The placement unit Based on the furniture and layout proposed by the proposal unit, the arrangement of the furniture is adjusted to fit the size and shape of the room.

2. The system of claim 1.

6. The proposal unit Based on the user's requests, the system proposes furniture and layouts that provide specific criteria for making a room appear larger.

2. The system of claim 1.

7. The input unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

Images