System

Abstract

An object of a system according to an embodiment is to propose interior coordination on the basis of floor plan information of a room and search for an optimal product.SOLUTION: A system according to an embodiment includes a floor plan information acquisition unit, an analysis unit, a coordination proposal unit, a product search unit, an integrated presentation unit, and a feedback analysis unit. The floor plan information acquisition unit acquires floor plan information of a room. The analysis unit analyzes the floor plan information acquired by the floor plan information acquisition unit. A coordination proposal part proposes interior coordination on the basis of the floor plan information analyzed by the analysis part. A commodity retrieval part retrieves the commodity of the lowest price from an Internet shopping site on the basis of the interior proposed by the coordination proposal part. The integrated presentation part integrates the proposal contents obtained by the coordination proposal part and the article retrieval part and presents them to the user. The feedback analysis unit analyzes the user's feedback and makes a re-proposal.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 the drawback of proposing interior coordination based on room layout information and then searching for the most suitable products, which is a complicated process that is time-consuming for users.

[0005] The system according to the embodiment aims to propose interior coordination based on room layout information and search for optimal products. [Means for solving the problem]

[0006] The system according to the embodiment includes a floor plan information acquisition unit, an analysis unit, a coordination suggestion unit, a product search unit, an integrated presentation unit, and a feedback analysis unit. The floor plan information acquisition unit acquires room floor plan information. The analysis unit analyzes the floor plan information acquired by the floor plan information acquisition unit. The coordination suggestion unit proposes interior coordination based on the floor plan information analyzed by the analysis unit. The product search unit searches online shopping sites for the lowest priced products based on the interior suggested by the coordination suggestion unit. The integrated presentation unit integrates the suggestions obtained by the coordination suggestion unit and the product search unit and presents them to the user. The feedback analysis unit analyzes the user's feedback and makes new suggestions. [Effects of the Invention]

[0007] The system according to the embodiment can propose interior coordination based on the layout information of a room and search for the most suitable products. [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) The interior coordination suggestion system according to an embodiment of the present invention is a system in which a user simply sends a floor plan of a room, and a generation AI makes one-stop suggestions from interior coordination to searching for the cheapest products on online shopping sites. As a result, the interior coordination suggestion system allows a user to do everything from interior coordination to searching for the cheapest products on online shopping sites in one stop, simply by sending a floor plan of a room.

[0029] An interior coordination suggestion system according to an embodiment includes a floor plan information acquisition unit, an analysis unit, a coordination suggestion unit, a product search unit, an integrated presentation unit, and a feedback analysis unit. The floor plan information acquisition unit acquires room floor plan information. For example, it can receive blueprints, photos, text data, etc. provided by a user. The floor plan information acquisition unit can also receive photos of a room taken by a user with a smartphone and extract floor plan information using image analysis technology. The floor plan information acquisition unit can also acquire room dimensions and layout information entered by a user as text data. The analysis unit analyzes the floor plan information acquired by the floor plan information acquisition unit. For example, a generation AI analyzes the size and shape of a room and the positions of windows and doors to identify space available for furniture placement. The analysis unit can also automatically estimate the purpose of the room and perform analysis based on that. The analysis unit can also perform analysis taking into account light entry and ventilation. The coordination suggestion unit proposes interior coordination based on the floor plan information analyzed by the analysis unit. For example, the generation AI proposes optimal furniture and decorative item placement based on the user's desired interior style and budget. The coordination suggestion unit can also automatically suggest changes to interior coordination according to the season and weather. Furthermore, the coordination suggestion unit can also suggest interior coordination that reflects the user's past purchase history and preferences. The product search unit searches for the lowest-priced products from online shopping sites based on the interior suggested by the coordination suggestion unit. For example, the generation AI searches multiple online shopping sites for the suggested furniture and decorative items and identifies the products offered at the lowest prices. The product search unit can also prioritize suggesting high-quality products by taking into account product reviews and ratings. Furthermore, the product search unit can suggest the best overall value products by considering not only the product price but also delivery time and shipping costs. The integrated presentation unit integrates the suggestions obtained by the coordination suggestion unit and the product search unit and presents them to the user. For example, the generation AI combines the interior coordination suggestions and the results of the lowest-priced product search into a single proposal and presents it to the user.The integrated presentation unit can also visualize the proposed content as a 3D model, making it easier for the user to visualize the actual room. Furthermore, the integrated presentation unit can provide an interface that allows the user to customize the proposed content. The feedback analysis unit analyzes the user's feedback and makes new proposals. For example, the generation AI analyzes the user's feedback and performs new interior coordination and searches for the lowest price products according to the user's requests. The feedback analysis unit can also analyze the user's emotions and provide an interface that elicits positive emotions. Furthermore, the feedback analysis unit can compare the feedback with that of other users and identify common areas for improvement. As a result, the interior coordination proposal system according to the embodiment can provide one-stop proposals, from interior coordination to searching for the lowest price products on online shopping sites, simply by the user submitting their room layout. For example, the system can eliminate the need for the user to redesign the living room interior of their new home from scratch and easily find the optimal coordination and bargain products. Furthermore, the system can provide flexible proposals tailored to the user's requests, thereby providing a highly satisfying service.

[0030] When analyzing floor plan information, the analysis unit can take into account not only the space available for furniture placement, but also the way light enters and ventilation. For example, when analyzing a room's floor plan, the analysis unit's generation AI takes into account the position and size of windows and simulates how natural light enters. For example, it analyzes how light from a window spreads throughout the room and proposes the optimal furniture layout. The analysis unit also takes ventilation into account when analyzing a room's floor plan. For example, it simulates the flow of air based on the position of windows and doors and proposes a furniture layout that provides good ventilation. The analysis unit also takes into account how light enters and ventilation simultaneously when analyzing a room's floor plan. For example, it simulates the flow of light and air from windows and proposes the optimal furniture layout and curtain selection. This makes it possible to propose the optimal furniture layout that takes into account the way light enters and ventilation.

[0031] When analyzing floor plan information, the analysis unit can automatically estimate the room's purpose and perform analysis based on that. For example, when the generation AI analyzes floor plan information, the analysis unit develops an algorithm that automatically estimates the room's purpose. For example, it estimates the room's purpose, such as living room or bedroom, based on the room's size, shape, and the placement of windows and doors. The analysis unit also automatically estimates the room's purpose and suggests the optimal furniture layout based on that estimate. For example, if the room is estimated to be a living room, it suggests the placement of a sofa and television, and if it is estimated to be a bedroom, it suggests the placement of a bed and closet. The analysis unit also takes into account user input information when the generation AI estimates the room's purpose. For example, if the user inputs "office," it would suggest the placement of a desk and bookshelf based on that information. This makes it possible to suggest the optimal furniture layout according to the room's purpose.

[0032] The floor plan information acquisition unit can automatically generate floor plans based on the user's voice input. The floor plan information acquisition unit develops a system in which a generation AI automatically generates floor plans simply by the user describing the room layout verbally. For example, the system analyzes voice input such as "The living room is 10 tatami mats in size and has two windows" to generate a floor plan. The floor plan information acquisition unit also uses voice recognition technology to convert the user's voice description into text data, and the generation AI automatically generates a floor plan based on that data. For example, the voice input information is analyzed and the size and shape of the room are reflected in the drawing. The floor plan information acquisition unit also generates a floor plan in real time as the user describes the room layout verbally, providing feedback to the user. For example, the system provides an interface in which the floor plan is dynamically updated in response to voice input. This allows floor plans to be automatically generated simply by the user entering floor plan information verbally.

[0033] The floor plan information acquisition unit can input floor plan information for different rooms simultaneously and integrate it to propose overall interior coordination. We are developing a system in which the floor plan information acquisition unit inputs floor plan information for different rooms simultaneously, and the generation AI integrates it to propose overall interior coordination. For example, floor plan information for the living room and dining room is input simultaneously to propose an integrated coordination. The floor plan information acquisition unit also allows the generation AI to analyze floor plan information for different rooms and integrate it to propose optimal furniture arrangements. For example, it makes a proposal to unify the overall interior style based on floor plan information for the living room and bedroom. Furthermore, when floor plan information for different rooms is input simultaneously, the generation AI automatically analyzes the relationships between the rooms and proposes integrated interior coordination. For example, it makes proposals that take into account the flow of people between the rooms and visual continuity. This makes it possible to integrate floor plan information for multiple rooms to propose overall interior coordination.

[0034] The coordination suggestion unit can automatically suggest changes to interior coordination according to the season and weather. For example, the coordination suggestion unit develops a system that automatically suggests changes to the interior coordination proposed by the generation AI according to the season and weather. For example, in summer, it suggests furniture with cool colors and light materials, and in winter, it suggests furniture with warm colors and heavy materials. In addition, the generation AI analyzes weather data to automatically suggest changes to interior coordination according to the season and weather. For example, it suggests the selection of optimal curtains and rugs based on the current weather and season. In addition, when the generation AI suggests changes to interior coordination according to the season and weather, the coordination suggestion unit also takes into account the user's preferences and past selection history. For example, it makes optimal suggestions for each season based on the interior style the user has previously chosen. This makes it possible to suggest optimal interior coordination according to the season and weather.

[0035] The coordination suggestion unit can propose interior coordination that reflects the user's past purchase history and preferences. For example, the coordination suggestion unit develops a system that reflects the user's past purchase history in the interior coordination proposed by the generation AI. For example, it proposes optimal coordination based on furniture and decorative items purchased in the past. In addition, the generation AI analyzes the user's past selection history to propose interior coordination that reflects the user's preferences. For example, it proposes optimal furniture placement based on the user's preferred colors and styles. In addition, the coordination suggestion unit takes user feedback into consideration when the generation AI proposes interior coordination that reflects the user's past purchase history and preferences. For example, it makes more appropriate suggestions based on the user's evaluation of past suggestions. This makes it possible to propose interior coordination based on the user's past purchase history and preferences.

[0036] The coordination suggestion unit can propose interior coordination that combines the user's existing furniture and decorations. For example, the coordination suggestion unit develops a system that combines the user's existing furniture and decorations with the interior coordination proposed by the generation AI. For example, it proposes the optimal coordination based on the sofas and tables the user owns. In addition, the generation AI analyzes the user's list of possessions to propose interior coordination that combines the user's existing furniture and decorations. For example, it proposes the optimal layout based on the color and style of the owned furniture. In addition, when the generation AI proposes interior coordination that combines the user's existing furniture and decorations, the coordination suggestion unit also takes into account the user's preferences and past selection history. For example, it makes suggestions that make use of the user's existing furniture based on the user's preferred style. This makes it possible to propose interior coordination that utilizes the furniture and decorations the user already owns.

[0037] The coordination suggestion unit can propose hybrid coordination that combines different interior styles. For example, the coordination suggestion unit develops a system in which a generation AI proposes hybrid coordination that combines different interior styles. For example, it proposes coordination that combines modern and classic elements. In addition, in order to propose hybrid coordination that combines different interior styles, the generation AI analyzes the characteristics of the styles. For example, it proposes the optimal combination based on the characteristics of the colors and materials of each style. In addition, the coordination suggestion unit proposes hybrid coordination that combines different interior styles by taking into account the user's preferences and past selection history. For example, it proposes the optimal combination based on the style that the user prefers. This makes it possible to propose hybrid coordination that combines different interior styles.

[0038] The product search unit takes into account product reviews and ratings and is able to prioritize suggesting high-quality products. For example, when the generation AI searches for the cheapest product, the product search unit will develop a system that analyzes product reviews and ratings and prioritizes suggesting high-quality products. For example, it will select the most suitable product based on review star ratings and comments. The product search unit also takes into account product reviews and ratings and prioritizes suggesting high-quality products when the generation AI searches for the cheapest product. For example, it will analyze the content of reviews and select products with many positive ratings. The product search unit also takes into account the user's preferences and past selection history when the generation AI analyzes product reviews and ratings and prioritizes suggesting high-quality products. For example, it will suggest the most suitable product based on products that the user has given high ratings to in the past. This allows high-quality products to be prioritized.

[0039] The product search unit considers not only product price but also delivery time and shipping costs, allowing it to suggest the most cost-effective product overall. For example, when the generation AI searches for the cheapest product, the product search unit develops a system that considers not only product price but also delivery time and shipping costs to suggest the most cost-effective product overall. For example, the product price and shipping costs are added together to select the cheapest product. Furthermore, when the generation AI searches for the cheapest product, taking delivery time and shipping costs into consideration, the product search unit proposes the most cost-effective product overall. For example, it prioritizes selecting products with short delivery times and low shipping costs. Furthermore, when the generation AI searches for the cheapest product, the product search unit considers user preferences and past selection history when proposing the most cost-effective product overall, taking delivery time and shipping costs into consideration. For example, it proposes the optimal product based on delivery options the user has previously selected. This allows it to propose the most cost-effective product overall, taking into consideration not only price but also delivery time and shipping costs.

[0040] The product search unit can search inventory information not only from multiple online shopping sites but also from local stores. For example, when the generation AI searches for the lowest-priced item, the product search unit will develop a system that searches inventory information from local stores as well as from multiple online shopping sites. For example, it will compare online and offline prices and suggest the best value item. The product search unit will also add a function to search inventory information from local stores, so that when the generation AI searches for the lowest-priced item, it will comprehensively compare online and offline prices. For example, it will take into account discount information at local stores to suggest the most suitable item. The product search unit will also take into account inventory information at local stores when the generation AI searches for the lowest-priced item, improving user convenience. For example, it will prioritize suggesting items that can be picked up on the same day at a nearby store. This will enable it to comprehensively compare online and offline prices and suggest the best value item.

[0041] The product search unit can prioritize searches for products from specific brands or manufacturers specified by the user. For example, the product search unit will develop a system that prioritizes searches for products from specific brands or manufacturers specified by the user when the generation AI searches for the lowest-priced products. For example, if a user specifies "a sofa from a specific brand," products from that brand will be searched for first. The product search unit will also add a function to prioritize searches for products from specific brands or manufacturers specified by the user, allowing the generation AI to reflect the user's preferences when searching for the lowest-priced products. For example, the unit will suggest optimal products based on products from brands that the user has previously purchased. The product search unit will also add a function to prioritize searches for products from specific brands or manufacturers specified by the user when the generation AI searches for the lowest-priced products, improving user satisfaction. For example, the unit will prioritize suggestions for products from brands that the user trusts. This allows the generation AI to prioritize searches for products from the brand or manufacturer specified by the user.

[0042] The integrated presentation unit can integrate the proposals taking into account the user's lifestyle and family composition. For example, the integrated presentation unit develops a system that takes into account the user's lifestyle and family composition when the generation AI integrates the proposals. For example, it may propose a furniture layout that prioritizes safety for a household with children. The integrated presentation unit also takes into account the user's lifestyle and family composition, allowing the generation AI to make optimal proposals. For example, it may propose a compact and functional furniture layout for a user living alone, and a spacious living room layout for a large family. The integrated presentation unit also takes into account the user's lifestyle and family composition when the generation AI integrates the proposals, making proposals tailored to the user's needs. For example, it may propose a pet-friendly furniture layout for a household with pets. This makes it possible to make optimal proposals according to the user's lifestyle and family composition.

[0043] The integrated presentation unit visualizes the proposed content as a 3D model, making it easier for users to visualize the actual room. For example, the integrated presentation unit develops a system that visualizes the proposed content as a 3D model when the generation AI integrates the proposed content. For example, it allows users to check the proposed interior coordination in a 3D model. The integrated presentation unit also visualizes the proposed content as a 3D model, making it easier for users to visualize the actual room. For example, it allows users to check the furniture arrangement and color combination in a 3D model and makes a proposal that satisfies the user. The integrated presentation unit also visualizes the proposed content as a 3D model when the generation AI integrates the proposed content, providing an interface that makes it easier for users to visualize the actual room. For example, it allows users to operate the 3D model and check the furniture arrangement. In this way, visualizing the proposed content as a 3D model makes it easier for users to visualize the actual room.

[0044] The integrated presentation unit can provide an interface that allows the user to customize the proposal content. For example, the integrated presentation unit develops a system that provides an interface that allows the user to customize the proposal content when the generation AI integrates the proposal content. For example, the integrated presentation unit allows the user to freely change the furniture arrangement and color combination. The integrated presentation unit also provides an interface that allows the user to customize the proposal content, allowing the user to adjust the proposal content to suit their own preferences. For example, the integrated presentation unit adds a function that allows the user to edit the proposed interior coordination themselves. The integrated presentation unit also provides an interface that allows the user to customize the proposal content when the generation AI integrates the proposal content, improving user satisfaction. For example, the integrated presentation unit allows the user to change the proposal content in real time and check the results. In this way, by providing an interface that allows the user to customize the proposal content, user satisfaction can be improved.

[0045] The integrated presentation unit can present the proposals as multiple variations, allowing the user to select from them. For example, the integrated presentation unit will develop a system in which, when the generation AI integrates the proposals, it presents the proposals as multiple variations. For example, it will present variations of different interior styles or furniture arrangements to the user. The integrated presentation unit will also present the proposals as multiple variations, allowing the user to select according to their preferences. For example, it will allow the user to select their favorite from the proposed interior coordinations. The integrated presentation unit will also present the proposals as multiple variations, when the generation AI integrates the proposals, to expand the user's choices. For example, it will allow the user to compare different color and material combinations. This will expand the user's choices by presenting the proposals as multiple variations.

[0046] When analyzing user feedback, the feedback analysis unit can take into account not only the content of the feedback but also the user's past behavioral history. For example, the feedback analysis unit develops a system in which, when the generation AI analyzes user feedback, it takes into account not only the content of the feedback but also the user's past behavioral history. For example, it makes optimal re-suggestions based on past purchase history and interior coordination selection history. Furthermore, when analyzing user feedback, the feedback analysis unit takes into account past behavioral history so that the generation AI can make more appropriate re-suggestions. For example, it makes re-suggestions based on styles and colors that the user has previously selected. Furthermore, when the generation AI analyzes user feedback, the feedback analysis unit takes into account past behavioral history so that it can make re-suggestions that are tailored to the user's preferences and needs. For example, it makes re-suggestions based on suggestions that the user has previously given high ratings. This makes it possible to make more appropriate re-suggestions by taking into account the user's past behavioral history.

[0047] The feedback analysis unit can compare the feedback with that of other users and identify common areas for improvement. For example, the feedback analysis unit develops a system in which, when the generation AI analyzes a user's feedback, it compares it with the feedback of other users. For example, it analyzes feedback from multiple users and identifies common areas for improvement. The feedback analysis unit also compares it with the feedback of other users, allowing the generation AI to identify common areas for improvement and make specific improvement suggestions. For example, it identifies areas where multiple users are dissatisfied and proposes improvements. The feedback analysis unit also compares the user's feedback with that of other users and identifies common areas for improvement. For example, if multiple users point out the same problem, it makes suggestions to solve the problem. This allows the generation AI to identify common areas for improvement by comparing it with the feedback of other users and make more effective improvement suggestions.

[0048] The feedback analysis unit can automatically classify the content of the feedback and make re-suggestions for each different category. For example, the feedback analysis unit develops a system that automatically classifies the content of the feedback when the generation AI analyzes user feedback. For example, it classifies the feedback into positive feedback, negative feedback, improvement suggestions, etc. Furthermore, the feedback analysis unit automatically classifies the content of the feedback and makes specific suggestions so that the generation AI can make re-suggestions for each different category. For example, it proposes improvement measures for negative feedback and makes additional suggestions for positive feedback. Furthermore, the feedback analysis unit automatically classifies the content of the feedback when the generation AI analyzes user feedback and makes re-suggestions for each different category. For example, it identifies areas that need improvement and makes specific improvement suggestions. In this way, by automatically classifying the content of the feedback and making re-suggestions for each different category, more appropriate improvement suggestions can be made.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The floor plan information acquisition unit can automatically generate floor plans based on the user's voice input. For example, we will develop a system in which the generation AI automatically generates floor plans simply by the user describing the room layout verbally. For example, the system analyzes voice input such as, "The living room is 10 tatami mats in size and has two windows" to generate a floor plan. The floor plan information acquisition unit also uses voice recognition technology to convert the user's voice description into text data, and the generation AI automatically generates a floor plan based on that data. For example, it analyzes the voice input information and reflects the size and shape of the room in the drawing. The floor plan information acquisition unit also generates a floor plan in real time as the user describes the room layout verbally, providing feedback to the user. For example, it provides an interface in which the floor plan is dynamically updated in response to voice input. This allows floor plans to be automatically generated simply by the user entering floor plan information verbally.

[0051] The floor plan information acquisition unit can input floor plan information for different rooms simultaneously and integrate it to propose overall interior coordination. For example, we are developing a system in which floor plan information for different rooms is input simultaneously and the generation AI integrates it to propose overall interior coordination. For example, floor plan information for the living room and dining room is input simultaneously to propose an integrated coordination. The floor plan information acquisition unit also allows the generation AI to analyze floor plan information for different rooms and integrate it to propose optimal furniture arrangements. For example, it can propose a unified overall interior style based on floor plan information for the living room and bedroom. Furthermore, when floor plan information for different rooms is input simultaneously, the generation AI automatically analyzes the relationships between the rooms and proposes integrated interior coordination. For example, it makes proposals that take into account the flow of people between the rooms and visual continuity. This makes it possible to integrate floor plan information for multiple rooms and propose overall interior coordination.

[0052] The coordination suggestion unit can automatically suggest changes to interior coordination according to the season and weather. For example, we will develop a system that automatically suggests changes to the interior coordination proposed by the generation AI according to the season and weather. For example, in summer, cool colors and furniture with light materials are suggested, and in winter, warm colors and furniture with heavy materials are suggested. In addition, the generation AI analyzes weather data to automatically suggest changes to interior coordination according to the season and weather. For example, it suggests the optimal selection of curtains and rugs based on the current weather and season. In addition, when the generation AI suggests changes to interior coordination according to the season and weather, the coordination suggestion unit also takes into account the user's preferences and past selection history. For example, it makes the optimal suggestions for each season based on the interior styles the user has previously chosen. This makes it possible to suggest the optimal interior coordination according to the season and weather.

[0053] The coordination suggestion unit can propose interior coordination that reflects the user's past purchase history and preferences. For example, we will develop a system that reflects the user's past purchase history in the interior coordination proposed by the generation AI. For example, it will propose the optimal coordination based on furniture and decorative items purchased in the past. In addition, in order to propose interior coordination that reflects the user's preferences, the generation AI will analyze the user's past selection history. For example, it will propose the optimal furniture arrangement based on the user's preferred colors and styles. In addition, the coordination suggestion unit will take user feedback into consideration when the generation AI proposes interior coordination that reflects the user's past purchase history and preferences. For example, it will make more appropriate suggestions based on the user's evaluation of past suggestions. This will enable it to propose interior coordination based on the user's past purchase history and preferences.

[0054] The coordination suggestion unit can propose interior coordination that combines the user's existing furniture and decorations. For example, we will develop a system that combines the user's existing furniture and decorations with the interior coordination proposed by the generation AI. For example, it will propose the optimal coordination based on the sofas and tables the user owns. In addition, the coordination suggestion unit analyzes the user's list of possessions to propose interior coordination that combines the user's existing furniture and decorations. For example, it will propose the optimal layout based on the color and style of the owned furniture. In addition, when the generation AI proposes interior coordination that combines the user's existing furniture and decorations, the coordination suggestion unit also takes into account the user's preferences and past selection history. For example, it will make suggestions that make use of the user's existing furniture based on the user's preferred style. This makes it possible to propose interior coordination that makes use of the furniture and decorations the user already owns.

[0055] The coordination suggestion unit can propose hybrid coordination that combines different interior styles. For example, we will develop a system in which the generation AI proposes hybrid coordination that combines different interior styles. For example, we will propose a coordination that combines modern and classic elements. In addition, in order to propose hybrid coordination that combines different interior styles, the generation AI analyzes the characteristics of the styles. For example, it will propose the optimal combination based on the characteristics of the colors and materials of each style. In addition, the coordination suggestion unit will propose hybrid coordination that combines different interior styles by taking into account the user's preferences and past selection history. For example, it will propose the optimal combination based on the style that the user prefers. This makes it possible to propose hybrid coordination that combines different interior styles.

[0056] The product search unit can search inventory information not only from multiple online shopping sites but also from local stores. For example, when the generation AI searches for the lowest price product, we will develop a system that searches inventory information from local stores as well as from multiple online shopping sites. For example, it will compare online and offline prices and suggest the best value product. In addition, the product search unit will add a function to search inventory information from local stores, so that when the generation AI searches for the lowest price product, it will comprehensively compare online and offline prices. For example, it will take into account discount information at local stores to suggest the most suitable product. In addition, when the generation AI searches for the lowest price product, the product search unit will also take into account inventory information at local stores, improving user convenience. For example, it will prioritize suggesting products that can be picked up on the same day at a nearby store. This will enable it to comprehensively compare online and offline prices and suggest the best value product.

[0057] The product search unit can prioritize searches for products from specific brands or manufacturers specified by the user. For example, we will develop a system in which, when the generation AI searches for the lowest-priced products, it prioritizes searches for products from specific brands or manufacturers specified by the user. For example, if the user specifies "a specific brand of sofa," it will prioritize searches for products from that brand. We will also add a function to the product search unit to prioritize searches for products from specific brands or manufacturers specified by the user, so that the generation AI will reflect the user's preferences when searching for the lowest-priced products. For example, it will suggest optimal products based on products from brands that the user has previously purchased. We will also add a function to the product search unit to prioritize searches for products from specific brands or manufacturers specified by the user when the generation AI searches for the lowest-priced products, improving user satisfaction. For example, it will prioritize suggestions for products from brands that the user trusts. This will allow it to prioritize searches for products from the brands or manufacturers specified by the user.

[0058] The integrated presentation unit can integrate suggestions taking into account the user's lifestyle and family composition. For example, we will develop a system in which the generation AI takes into account the user's lifestyle and family composition when integrating suggestions. For example, it will suggest furniture layouts that prioritize safety for households with children. The integrated presentation unit also takes into account the user's lifestyle and family composition, allowing the generation AI to make optimal suggestions. For example, it will suggest a compact and functional furniture layout for a user living alone, and a spacious living room layout for a large family. The integrated presentation unit also takes into account the user's lifestyle and family composition when integrating suggestions, making suggestions tailored to the user's needs. For example, it will suggest pet-friendly furniture layouts for households with pets. This makes it possible to make optimal suggestions based on the user's lifestyle and family composition.

[0059] The integrated presentation unit visualizes the proposals as a 3D model, making it easier for users to visualize the actual room. For example, we will develop a system that visualizes the proposals as a 3D model when the generation AI integrates the proposals. For example, we will allow users to check the proposed interior coordination in a 3D model. The integrated presentation unit also visualizes the proposals as a 3D model, making it easier for users to visualize the actual room. For example, the furniture arrangement and color combinations can be checked in a 3D model, making a proposal that satisfies the user. The integrated presentation unit also visualizes the proposals as a 3D model when the generation AI integrates the proposals, providing an interface that makes it easier for users to visualize the actual room. For example, the user can operate the 3D model to check the furniture arrangement. In this way, visualizing the proposals as a 3D model makes it easier for users to visualize the actual room.

[0060] The integrated presentation unit can provide an interface that allows users to customize the proposals. For example, we will develop a system that provides an interface that allows users to customize the proposals when the generation AI integrates the proposals. For example, we will allow users to freely change the furniture arrangement and color combinations. The integrated presentation unit also provides an interface that allows users to customize the proposals, allowing them to adjust the proposals to suit their own preferences. For example, we will add a function that allows users to edit the proposed interior coordination themselves. The integrated presentation unit also provides an interface that allows users to customize the proposals when the generation AI integrates the proposals, improving user satisfaction. For example, we will allow users to change the proposals in real time and check the results. In this way, by providing an interface that allows users to customize the proposals, user satisfaction can be improved.

[0061] The integrated presentation unit can present the proposals as multiple variations, allowing the user to select from them. For example, we will develop a system in which, when the generation AI integrates the proposals, it presents the proposals as multiple variations. For example, it may present variations of different interior styles or furniture arrangements to the user. The integrated presentation unit may also present the proposals as multiple variations, allowing the user to select according to their preferences. For example, it may allow the user to select their favorite interior coordination from the proposed interior coordinations. The integrated presentation unit may also present the proposals as multiple variations, expanding the user's options when the generation AI integrates the proposals. For example, it may allow the user to compare different color or material combinations. This will expand the user's options by presenting the proposals as multiple variations.

[0062] When analyzing user feedback, the feedback analysis unit can take into account not only the content of the feedback but also the user's past behavioral history. For example, we will develop a system in which the generation AI analyzes user feedback and takes into account not only the content of the feedback but also the user's past behavioral history. For example, it makes optimal re-suggestions based on past purchase history and interior coordination selection history. Furthermore, when analyzing user feedback, the feedback analysis unit takes into account past behavioral history so that the generation AI can make more appropriate re-suggestions. For example, it makes re-suggestions based on styles and colors that the user has selected in the past. Furthermore, when the generation AI analyzes user feedback, the feedback analysis unit takes into account past behavioral history so that it can make re-suggestions that are tailored to the user's preferences and needs. For example, it makes re-suggestions based on suggestions that the user has given high ratings to in the past. This allows it to make more appropriate re-suggestions by taking into account the user's past behavioral history.

[0063] The feedback analysis unit can compare the feedback with that of other users and identify common areas for improvement. For example, a system is developed in which, when the generation AI analyzes a user's feedback, it compares it with the feedback of other users. For example, feedback from multiple users is analyzed and common areas for improvement are identified. The feedback analysis unit also compares it with the feedback of other users, allowing the generation AI to identify common areas for improvement and make specific improvement suggestions. For example, it identifies areas where multiple users are dissatisfied and proposes improvement measures. The feedback analysis unit also compares the user's feedback with that of other users and identifies common areas for improvement. For example, if multiple users point out the same problem, it makes suggestions to solve the problem. This allows the generation AI to identify common areas for improvement by comparing it with the feedback of other users and make more effective improvement suggestions.

[0064] The feedback analysis unit can automatically classify the content of the feedback and make re-suggestions for each different category. For example, a system is developed that automatically classifies the content of the feedback when the generation AI analyzes user feedback. For example, it may classify the feedback into positive feedback, negative feedback, improvement suggestions, etc. Furthermore, the feedback analysis unit automatically classifies the content of the feedback and makes specific suggestions so that the generation AI can make re-suggestions for each different category. For example, it may propose improvement measures for negative feedback and make additional suggestions for positive feedback. Furthermore, the feedback analysis unit automatically classifies the content of the feedback when the generation AI analyzes user feedback and makes re-suggestions for each different category. For example, it may identify areas that need improvement and make specific improvement suggestions. In this way, by automatically classifying the content of the feedback and making re-suggestions for each different category, more appropriate improvement suggestions can be made.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The floor plan information acquisition unit acquires the floor plan information of the room. For example, it can receive blueprints, photos, text data, etc. provided by the user. It can also receive photos of the room taken by the user with a smartphone and extract floor plan information using image analysis technology. It can also acquire the room dimensions and layout information entered by the user as text data. Step 2: The analysis unit analyzes the floor plan information acquired by the floor plan information acquisition unit. For example, the generation AI analyzes the size and shape of the room, as well as the position of windows and doors, to identify the space where furniture can be placed. It can also automatically estimate the purpose of the room and perform analysis based on that. It can also perform analysis taking into account the way light enters and ventilation. Step 3: The coordination suggestion unit proposes interior coordination based on the floor plan information analyzed by the analysis unit. For example, the generation AI proposes the optimal arrangement of furniture and decorations based on the user's desired interior style and budget. It can also automatically suggest changes to interior coordination according to the season and weather. It can also propose interior coordination that reflects the user's past purchase history and preferences. Step 4: The product search unit searches online shopping sites for the lowest priced products based on the interior design suggested by the coordination suggestion unit. For example, the generation AI searches multiple online shopping sites for the suggested furniture and decorative items and identifies the products offered at the lowest prices. It can also take into account product reviews and ratings to prioritize the suggestions of high-quality products. Furthermore, it can also consider not only the product price but also delivery time and shipping costs to suggest the best overall value products. Step 5: The integrated presentation unit integrates the proposals obtained by the coordination proposal unit and the product search unit and presents them to the user. For example, the generation AI can combine the interior coordination proposals and the results of the lowest price product search into a single proposal and present it to the user. The proposals can also be visualized as 3D models, making it easier for users to visualize the actual room. Furthermore, an interface can be provided that allows users to customize the proposals. Step 6: The feedback analysis unit analyzes the user's feedback and makes new suggestions. For example, the generation AI analyzes the user's feedback and creates new interior coordination and searches for the cheapest products based on the user's requests. It can also analyze the user's emotions and provide an interface to elicit positive emotions. It can also compare the feedback with that of other users to identify common areas for improvement.

[0067] (Example 2) The interior coordination suggestion system according to an embodiment of the present invention is a system in which a user simply sends a floor plan of a room, and a generation AI makes one-stop suggestions from interior coordination to searching for the cheapest products on online shopping sites. As a result, the interior coordination suggestion system allows a user to do everything from interior coordination to searching for the cheapest products on online shopping sites in one stop, simply by sending a floor plan of a room.

[0068] An interior coordination suggestion system according to an embodiment includes a floor plan information acquisition unit, an analysis unit, a coordination suggestion unit, a product search unit, an integrated presentation unit, and a feedback analysis unit. The floor plan information acquisition unit acquires room floor plan information. For example, it can receive blueprints, photos, text data, etc. provided by a user. The floor plan information acquisition unit can also receive photos of a room taken by a user with a smartphone and extract floor plan information using image analysis technology. The floor plan information acquisition unit can also acquire room dimensions and layout information entered by a user as text data. The analysis unit analyzes the floor plan information acquired by the floor plan information acquisition unit. For example, a generation AI analyzes the size and shape of a room and the positions of windows and doors to identify space available for furniture placement. The analysis unit can also automatically estimate the purpose of the room and perform analysis based on that. The analysis unit can also perform analysis taking into account light entry and ventilation. The coordination suggestion unit proposes interior coordination based on the floor plan information analyzed by the analysis unit. For example, the generation AI proposes optimal furniture and decorative item placement based on the user's desired interior style and budget. The coordination suggestion unit can also automatically suggest changes to interior coordination according to the season and weather. Furthermore, the coordination suggestion unit can also suggest interior coordination that reflects the user's past purchase history and preferences. The product search unit searches for the lowest-priced products from online shopping sites based on the interior suggested by the coordination suggestion unit. For example, the generation AI searches multiple online shopping sites for the suggested furniture and decorative items and identifies the products offered at the lowest prices. The product search unit can also prioritize suggesting high-quality products by taking into account product reviews and ratings. Furthermore, the product search unit can suggest the best overall value products by considering not only the product price but also delivery time and shipping costs. The integrated presentation unit integrates the suggestions obtained by the coordination suggestion unit and the product search unit and presents them to the user. For example, the generation AI combines the interior coordination suggestions and the results of the lowest-priced product search into a single proposal and presents it to the user.The integrated presentation unit can also visualize the proposed content as a 3D model, making it easier for the user to visualize the actual room. Furthermore, the integrated presentation unit can provide an interface that allows the user to customize the proposed content. The feedback analysis unit analyzes the user's feedback and makes new proposals. For example, the generation AI analyzes the user's feedback and performs new interior coordination and searches for the lowest price products according to the user's requests. The feedback analysis unit can also analyze the user's emotions and provide an interface that elicits positive emotions. Furthermore, the feedback analysis unit can compare the feedback with that of other users and identify common areas for improvement. As a result, the interior coordination proposal system according to the embodiment can provide one-stop proposals, from interior coordination to searching for the lowest price products on online shopping sites, simply by the user submitting their room layout. For example, the system can eliminate the need for the user to redesign the living room interior of their new home from scratch and easily find the optimal coordination and bargain products. Furthermore, the system can provide flexible proposals tailored to the user's requests, thereby providing a highly satisfying service.

[0069] When analyzing floor plan information, the analysis unit can take into account not only the space available for furniture placement, but also the way light enters and ventilation. For example, when analyzing a room's floor plan, the analysis unit's generation AI takes into account the position and size of windows and simulates how natural light enters. For example, it analyzes how light from a window spreads throughout the room and proposes the optimal furniture layout. The analysis unit also takes ventilation into account when analyzing a room's floor plan. For example, it simulates the flow of air based on the position of windows and doors and proposes a furniture layout that provides good ventilation. The analysis unit also takes into account how light enters and ventilation simultaneously when analyzing a room's floor plan. For example, it simulates the flow of light and air from windows and proposes the optimal furniture layout and curtain selection. This makes it possible to propose the optimal furniture layout that takes into account the way light enters and ventilation.

[0070] When analyzing floor plan information, the analysis unit can automatically estimate the room's purpose and perform analysis based on that. For example, when the generation AI analyzes floor plan information, the analysis unit develops an algorithm that automatically estimates the room's purpose. For example, it estimates the room's purpose, such as living room or bedroom, based on the room's size, shape, and the placement of windows and doors. The analysis unit also automatically estimates the room's purpose and suggests the optimal furniture layout based on that estimate. For example, if the room is estimated to be a living room, it suggests the placement of a sofa and television, and if it is estimated to be a bedroom, it suggests the placement of a bed and closet. The analysis unit also takes into account user input information when the generation AI estimates the room's purpose. For example, if the user inputs "office," it would suggest the placement of a desk and bookshelf based on that information. This makes it possible to suggest the optimal furniture layout according to the room's purpose.

[0071] The analysis unit analyzes the user's emotions and can simplify the input method if the user is feeling stressed. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotions in real time when entering room layout information. For example, it analyzes the user's facial expressions and voice and suggests a simplified input method if the user is feeling stressed. Furthermore, if the user is feeling stressed, the analysis unit allows the generation AI to automatically provide a simplified input method. For example, the analysis unit omits the input of a detailed floor plan and collects information in the form of simple questions. Furthermore, if the user is feeling stressed, the analysis unit uses the emotion estimation function to allow the generation AI to provide an input assistance function. For example, it uses voice input or an auto-complete function to reduce the user's input burden. This reduces the user's stress and input burden.

[0072] The floor plan information acquisition unit can automatically generate floor plans based on the user's voice input. The floor plan information acquisition unit develops a system in which a generation AI automatically generates floor plans simply by the user describing the room layout verbally. For example, the system analyzes voice input such as "The living room is 10 tatami mats in size and has two windows" to generate a floor plan. The floor plan information acquisition unit also uses voice recognition technology to convert the user's voice description into text data, and the generation AI automatically generates a floor plan based on that data. For example, the voice input information is analyzed and the size and shape of the room are reflected in the drawing. The floor plan information acquisition unit also generates a floor plan in real time as the user describes the room layout verbally, providing feedback to the user. For example, the system provides an interface in which the floor plan is dynamically updated in response to voice input. This allows floor plans to be automatically generated simply by the user entering floor plan information verbally.

[0073] The floor plan information acquisition unit can input floor plan information for different rooms simultaneously and integrate it to propose overall interior coordination. We are developing a system in which the floor plan information acquisition unit inputs floor plan information for different rooms simultaneously, and the generation AI integrates it to propose overall interior coordination. For example, floor plan information for the living room and dining room is input simultaneously to propose an integrated coordination. The floor plan information acquisition unit also allows the generation AI to analyze floor plan information for different rooms and integrate it to propose optimal furniture arrangements. For example, it makes a proposal to unify the overall interior style based on floor plan information for the living room and bedroom. Furthermore, when floor plan information for different rooms is input simultaneously, the generation AI automatically analyzes the relationships between the rooms and proposes integrated interior coordination. For example, it makes proposals that take into account the flow of people between the rooms and visual continuity. This makes it possible to integrate floor plan information for multiple rooms to propose overall interior coordination.

[0074] The analysis unit can analyze the user's emotions and provide an interface for eliciting positive emotions. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotions in real time when inputting floor plan information and provides an interface for eliciting positive emotions. For example, the analysis unit analyzes the user's facial expressions and voice and displays positive feedback. The analysis unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when the user inputs floor plan information. For example, if the user is feeling stressed, the analysis unit displays relaxing music or images. The analysis unit also uses the emotion estimation function to analyze the user's emotions when inputting floor plan information and provides advice for eliciting positive emotions. For example, if the user is feeling anxious, the analysis unit displays an easy input method or a support message. This makes it possible to provide an interface for eliciting positive emotions from the user.

[0075] The coordination suggestion unit can automatically suggest changes to interior coordination according to the season and weather. For example, the coordination suggestion unit develops a system that automatically suggests changes to the interior coordination proposed by the generation AI according to the season and weather. For example, in summer, it suggests furniture with cool colors and light materials, and in winter, it suggests furniture with warm colors and heavy materials. In addition, the generation AI analyzes weather data to automatically suggest changes to interior coordination according to the season and weather. For example, it suggests the selection of optimal curtains and rugs based on the current weather and season. In addition, when the generation AI suggests changes to interior coordination according to the season and weather, the coordination suggestion unit also takes into account the user's preferences and past selection history. For example, it makes optimal suggestions for each season based on the interior style the user has previously chosen. This makes it possible to suggest optimal interior coordination according to the season and weather.

[0076] The coordination suggestion unit can propose interior coordination that reflects the user's past purchase history and preferences. For example, the coordination suggestion unit develops a system that reflects the user's past purchase history in the interior coordination proposed by the generation AI. For example, it proposes optimal coordination based on furniture and decorative items purchased in the past. In addition, the generation AI analyzes the user's past selection history to propose interior coordination that reflects the user's preferences. For example, it proposes optimal furniture placement based on the user's preferred colors and styles. In addition, the coordination suggestion unit takes user feedback into consideration when the generation AI proposes interior coordination that reflects the user's past purchase history and preferences. For example, it makes more appropriate suggestions based on the user's evaluation of past suggestions. This makes it possible to propose interior coordination based on the user's past purchase history and preferences.

[0077] The coordination suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the proposed interior coordination and make adjustments to elicit positive emotions. The coordination suggestion unit, for example, uses the emotion estimation function to analyze the emotions the user feels toward the proposed interior coordination in real time. For example, it analyzes the user's facial expressions and voice and makes adjustments to elicit positive emotions. The coordination suggestion unit also analyzes the emotions the user feels toward the proposed interior coordination and makes specific adjustments to elicit positive emotions. For example, if the user feels dissatisfied, it makes a suggestion to change the color or placement. The coordination suggestion unit also uses the emotion estimation function to analyze the emotions the user feels toward the proposed interior coordination and provides advice to elicit positive emotions. For example, it makes a suggestion to emphasize elements that the user finds enjoyable. In this way, it is possible to adjust the interior coordination based on the user's emotions and elicit positive emotions.

[0078] The coordination suggestion unit can propose interior coordination that combines the user's existing furniture and decorations. For example, the coordination suggestion unit develops a system that combines the user's existing furniture and decorations with the interior coordination proposed by the generation AI. For example, it proposes the optimal coordination based on the sofas and tables the user owns. In addition, the generation AI analyzes the user's list of possessions to propose interior coordination that combines the user's existing furniture and decorations. For example, it proposes the optimal layout based on the color and style of the owned furniture. In addition, when the generation AI proposes interior coordination that combines the user's existing furniture and decorations, the coordination suggestion unit also takes into account the user's preferences and past selection history. For example, it makes suggestions that make use of the user's existing furniture based on the user's preferred style. This makes it possible to propose interior coordination that utilizes the furniture and decorations the user already owns.

[0079] The coordination suggestion unit can propose hybrid coordination that combines different interior styles. For example, the coordination suggestion unit develops a system in which a generation AI proposes hybrid coordination that combines different interior styles. For example, it proposes coordination that combines modern and classic elements. In addition, in order to propose hybrid coordination that combines different interior styles, the generation AI analyzes the characteristics of the styles. For example, it proposes the optimal combination based on the characteristics of the colors and materials of each style. In addition, the coordination suggestion unit proposes hybrid coordination that combines different interior styles by taking into account the user's preferences and past selection history. For example, it proposes the optimal combination based on the style that the user prefers. This makes it possible to propose hybrid coordination that combines different interior styles.

[0080] The coordination suggestion unit can use the emotion estimation function to analyze the user's emotions regarding the desired interior style and propose an optimal style based on the emotions. The coordination suggestion unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the desired interior style in real time. For example, it analyzes the user's facial expressions and voice and proposes a style that will elicit positive emotions. Furthermore, a system is developed in which the coordination suggestion unit analyzes the user's emotions regarding the desired interior style and proposes an optimal style based on the emotions. For example, if the user is seeking relaxation, it proposes calm colors and materials. Furthermore, the coordination suggestion unit uses the emotion estimation function to analyze the user's emotions regarding the desired interior style and makes specific suggestions to elicit positive emotions. For example, it proposes a style that emphasizes elements that bring joy to the user. In this way, it is possible to propose an optimal interior style based on the user's emotions.

[0081] The product search unit takes into account product reviews and ratings and is able to prioritize suggesting high-quality products. For example, when the generation AI searches for the cheapest product, the product search unit will develop a system that analyzes product reviews and ratings and prioritizes suggesting high-quality products. For example, it will select the most suitable product based on review star ratings and comments. The product search unit also takes into account product reviews and ratings and prioritizes suggesting high-quality products when the generation AI searches for the cheapest product. For example, it will analyze the content of reviews and select products with many positive ratings. The product search unit also takes into account the user's preferences and past selection history when the generation AI analyzes product reviews and ratings and prioritizes suggesting high-quality products. For example, it will suggest the most suitable product based on products that the user has given high ratings to in the past. This allows high-quality products to be prioritized.

[0082] The product search unit considers not only product price but also delivery time and shipping costs, allowing it to suggest the most cost-effective product overall. For example, when the generation AI searches for the cheapest product, the product search unit develops a system that considers not only product price but also delivery time and shipping costs to suggest the most cost-effective product overall. For example, the product price and shipping costs are added together to select the cheapest product. Furthermore, when the generation AI searches for the cheapest product, taking delivery time and shipping costs into consideration, the product search unit proposes the most cost-effective product overall. For example, it prioritizes selecting products with short delivery times and low shipping costs. Furthermore, when the generation AI searches for the cheapest product, the product search unit considers user preferences and past selection history when proposing the most cost-effective product overall, taking delivery time and shipping costs into consideration. For example, it proposes the optimal product based on delivery options the user has previously selected. This allows it to propose the most cost-effective product overall, taking into consideration not only price but also delivery time and shipping costs.

[0083] The product search unit can use the emotion estimation function to analyze the emotions the user has toward the lowest priced product and make adjustments to elicit positive emotions. The product search unit, for example, uses the emotion estimation function to analyze the emotions the user has toward the lowest priced product in real time. For example, it analyzes the user's facial expressions and voice and makes adjustments to elicit positive emotions. The product search unit also analyzes the emotions the user has toward the lowest priced product and makes specific adjustments to elicit positive emotions. For example, if the user is dissatisfied, it suggests changing the product description or image. The product search unit also uses the emotion estimation function to analyze the emotions the user has toward the lowest priced product and provides advice to elicit positive emotions. For example, it suggests products that emphasize elements that the user finds enjoyable. In this way, it is possible to adjust the lowest priced product based on the user's emotions and elicit positive emotions.

[0084] The product search unit can search inventory information not only from multiple online shopping sites but also from local stores. For example, when the generation AI searches for the lowest-priced item, the product search unit will develop a system that searches inventory information from local stores as well as from multiple online shopping sites. For example, it will compare online and offline prices and suggest the best value item. The product search unit will also add a function to search inventory information from local stores, so that when the generation AI searches for the lowest-priced item, it will comprehensively compare online and offline prices. For example, it will take into account discount information at local stores to suggest the most suitable item. The product search unit will also take into account inventory information at local stores when the generation AI searches for the lowest-priced item, improving user convenience. For example, it will prioritize suggesting items that can be picked up on the same day at a nearby store. This will enable it to comprehensively compare online and offline prices and suggest the best value item.

[0085] The product search unit can prioritize searches for products from specific brands or manufacturers specified by the user. For example, the product search unit will develop a system that prioritizes searches for products from specific brands or manufacturers specified by the user when the generation AI searches for the lowest-priced products. For example, if a user specifies "a sofa from a specific brand," products from that brand will be searched for first. The product search unit will also add a function to prioritize searches for products from specific brands or manufacturers specified by the user, allowing the generation AI to reflect the user's preferences when searching for the lowest-priced products. For example, the unit will suggest optimal products based on products from brands that the user has previously purchased. The product search unit will also add a function to prioritize searches for products from specific brands or manufacturers specified by the user when the generation AI searches for the lowest-priced products, improving user satisfaction. For example, the unit will prioritize suggestions for products from brands that the user trusts. This allows the generation AI to prioritize searches for products from the brand or manufacturer specified by the user.

[0086] The product search unit can use the emotion estimation function to analyze the emotions of a user when searching for the lowest priced product in real time and provide an interface for eliciting positive emotions. The product search unit, for example, uses the emotion estimation function to analyze the emotions of a user when searching for the lowest priced product in real time and provide an interface for eliciting positive emotions. For example, the product search unit analyzes the user's facial expressions and voice and displays positive feedback. The product search unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when the user searches for the lowest priced product. For example, if the user is feeling stressed, the product search unit displays relaxing music or images. The product search unit also uses the emotion estimation function to analyze the emotions of a user when searching for the lowest priced product and provides advice for eliciting positive emotions. For example, if the user is feeling anxious, the product search unit displays a simple search method or a support message. This makes it possible to search for the lowest priced product based on the user's emotions and elicit positive emotions.

[0087] The integrated presentation unit can integrate the proposals taking into account the user's lifestyle and family composition. For example, the integrated presentation unit develops a system that takes into account the user's lifestyle and family composition when the generation AI integrates the proposals. For example, it may propose a furniture layout that prioritizes safety for a household with children. The integrated presentation unit also takes into account the user's lifestyle and family composition, allowing the generation AI to make optimal proposals. For example, it may propose a compact and functional furniture layout for a user living alone, and a spacious living room layout for a large family. The integrated presentation unit also takes into account the user's lifestyle and family composition when the generation AI integrates the proposals, making proposals tailored to the user's needs. For example, it may propose a pet-friendly furniture layout for a household with pets. This makes it possible to make optimal proposals according to the user's lifestyle and family composition.

[0088] The integrated presentation unit visualizes the proposed content as a 3D model, making it easier for users to visualize the actual room. For example, the integrated presentation unit develops a system that visualizes the proposed content as a 3D model when the generation AI integrates the proposed content. For example, it allows users to check the proposed interior coordination in a 3D model. The integrated presentation unit also visualizes the proposed content as a 3D model, making it easier for users to visualize the actual room. For example, it allows users to check the furniture arrangement and color combination in a 3D model and makes a proposal that satisfies the user. The integrated presentation unit also visualizes the proposed content as a 3D model when the generation AI integrates the proposed content, providing an interface that makes it easier for users to visualize the actual room. For example, it allows users to operate the 3D model and check the furniture arrangement. In this way, visualizing the proposed content as a 3D model makes it easier for users to visualize the actual room.

[0089] The integrated presentation unit can use the emotion estimation function to analyze the emotion of the user when reviewing the proposal content and make adjustments to elicit positive emotions. The integrated presentation unit, for example, uses the emotion estimation function to analyze the emotion of the user when reviewing the proposal content in real time. For example, it analyzes the user's facial expressions and voice and makes adjustments to elicit positive emotions. The integrated presentation unit also analyzes the emotion of the user when reviewing the proposal content and makes specific adjustments to elicit positive emotions. For example, if the user is feeling dissatisfied, it makes a suggestion to change the proposal content. The integrated presentation unit also uses the emotion estimation function to analyze the emotion of the user when reviewing the proposal content and provides advice to elicit positive emotions. For example, it makes a suggestion to emphasize elements that the user finds enjoyable. In this way, it is possible to adjust the proposal content based on the user's emotions and elicit positive emotions.

[0090] The integrated presentation unit can provide an interface that allows the user to customize the proposal content. For example, the integrated presentation unit develops a system that provides an interface that allows the user to customize the proposal content when the generation AI integrates the proposal content. For example, the integrated presentation unit allows the user to freely change the furniture arrangement and color combination. The integrated presentation unit also provides an interface that allows the user to customize the proposal content, allowing the user to adjust the proposal content to suit their own preferences. For example, the integrated presentation unit adds a function that allows the user to edit the proposed interior coordination themselves. The integrated presentation unit also provides an interface that allows the user to customize the proposal content when the generation AI integrates the proposal content, improving user satisfaction. For example, the integrated presentation unit allows the user to change the proposal content in real time and check the results. In this way, by providing an interface that allows the user to customize the proposal content, user satisfaction can be improved.

[0091] The integrated presentation unit can present the proposals as multiple variations, allowing the user to select from them. For example, the integrated presentation unit will develop a system in which, when the generation AI integrates the proposals, it presents the proposals as multiple variations. For example, it will present variations of different interior styles or furniture arrangements to the user. The integrated presentation unit will also present the proposals as multiple variations, allowing the user to select according to their preferences. For example, it will allow the user to select their favorite from the proposed interior coordinations. The integrated presentation unit will also present the proposals as multiple variations, when the generation AI integrates the proposals, to expand the user's choices. For example, it will allow the user to compare different color and material combinations. This will expand the user's choices by presenting the proposals as multiple variations.

[0092] The integrated presentation unit can use the emotion estimation function to analyze the emotion of the user when reviewing the proposal content in real time and provide an interface for eliciting positive emotion. The integrated presentation unit, for example, uses the emotion estimation function to analyze the emotion of the user when reviewing the proposal content in real time and provide an interface for eliciting positive emotion. For example, the integrated presentation unit analyzes the user's facial expression and voice and displays positive feedback. The integrated presentation unit also uses the emotion estimation function to provide an interface for eliciting positive emotion when the user reviews the proposal content. For example, if the user is feeling stressed, it displays relaxing music or images. The integrated presentation unit also uses the emotion estimation function to analyze the emotion of the user when reviewing the proposal content and provides advice for eliciting positive emotion. For example, if the user is feeling anxious, it displays simple operating instructions or a support message. In this way, an interface for reviewing the proposal content based on the user's emotion can be provided and positive emotion can be elicited.

[0093] When analyzing user feedback, the feedback analysis unit can take into account not only the content of the feedback but also the user's past behavioral history. For example, the feedback analysis unit develops a system in which, when the generation AI analyzes user feedback, it takes into account not only the content of the feedback but also the user's past behavioral history. For example, it makes optimal re-suggestions based on past purchase history and interior coordination selection history. Furthermore, when analyzing user feedback, the feedback analysis unit takes into account past behavioral history so that the generation AI can make more appropriate re-suggestions. For example, it makes re-suggestions based on styles and colors that the user has previously selected. Furthermore, when the generation AI analyzes user feedback, the feedback analysis unit takes into account past behavioral history so that it can make re-suggestions that are tailored to the user's preferences and needs. For example, it makes re-suggestions based on suggestions that the user has previously given high ratings. This makes it possible to make more appropriate re-suggestions by taking into account the user's past behavioral history.

[0094] The feedback analysis unit analyzes the emotional tone of the feedback and can make improvements, particularly for negative feedback. For example, the feedback analysis unit develops a system that analyzes the emotional tone of the feedback in real time when the generation AI analyzes user feedback. For example, it analyzes user comments and ratings and makes improvements, particularly for negative feedback. The feedback analysis unit also analyzes the emotional tone of the feedback, and the generation AI makes specific improvement suggestions to make improvements, particularly for negative feedback. For example, it identifies areas where the user is dissatisfied and proposes improvement measures. The feedback analysis unit also takes the emotional tone into consideration when the generation AI analyzes user feedback and makes improvements, particularly for negative feedback. For example, if the user is dissatisfied, it makes alternative or additional suggestions. This makes it possible to make improvements, particularly for negative feedback, and improve user satisfaction.

[0095] The feedback analysis unit can use the emotion estimation function to analyze the emotion a user has when providing feedback and provide an interface for eliciting positive emotions. The feedback analysis unit, for example, uses the emotion estimation function to analyze the emotion a user has when providing feedback in real time and provide an interface for eliciting positive emotions. For example, the feedback analysis unit analyzes the user's facial expressions and voice and displays positive feedback. The feedback analysis unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when the user provides feedback. For example, if the user is feeling stressed, relaxing music or images are displayed. The feedback analysis unit also uses the emotion estimation function to analyze the emotion a user has when providing feedback and provide advice for eliciting positive emotions. For example, if the user is feeling anxious, simple operating instructions or a support message is displayed. In this way, an interface for providing feedback based on the user's emotions can be provided and positive emotions can be elicited.

[0096] The feedback analysis unit can compare the feedback with that of other users and identify common areas for improvement. For example, the feedback analysis unit develops a system in which, when the generation AI analyzes a user's feedback, it compares it with the feedback of other users. For example, it analyzes feedback from multiple users and identifies common areas for improvement. The feedback analysis unit also compares it with the feedback of other users, allowing the generation AI to identify common areas for improvement and make specific improvement suggestions. For example, it identifies areas where multiple users are dissatisfied and proposes improvements. The feedback analysis unit also compares the user's feedback with that of other users and identifies common areas for improvement. For example, if multiple users point out the same problem, it makes suggestions to solve the problem. This allows the generation AI to identify common areas for improvement by comparing it with the feedback of other users and make more effective improvement suggestions.

[0097] The feedback analysis unit can automatically classify the content of the feedback and make re-suggestions for each different category. For example, the feedback analysis unit develops a system that automatically classifies the content of the feedback when the generation AI analyzes user feedback. For example, it classifies the feedback into positive feedback, negative feedback, improvement suggestions, etc. Furthermore, the feedback analysis unit automatically classifies the content of the feedback and makes specific suggestions so that the generation AI can make re-suggestions for each different category. For example, it proposes improvement measures for negative feedback and makes additional suggestions for positive feedback. Furthermore, the feedback analysis unit automatically classifies the content of the feedback when the generation AI analyzes user feedback and makes re-suggestions for each different category. For example, it identifies areas that need improvement and makes specific improvement suggestions. In this way, by automatically classifying the content of the feedback and making re-suggestions for each different category, more appropriate improvement suggestions can be made.

[0098] The feedback analysis unit can use the emotion estimation function to analyze the emotion a user has when providing feedback in real time and provide an interface for eliciting positive emotions. The feedback analysis unit, for example, uses the emotion estimation function to analyze the emotion a user has when providing feedback in real time and provide an interface for eliciting positive emotions. For example, the feedback analysis unit analyzes the user's facial expressions and voice and displays positive feedback. The feedback analysis unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when the user provides feedback. For example, if the user is feeling stressed, relaxing music or images are displayed. The feedback analysis unit also uses the emotion estimation function to analyze the emotion a user has when providing feedback and provide advice for eliciting positive emotions. For example, if the user is feeling anxious, simple operating instructions or a support message is displayed. In this way, an interface for providing feedback based on the user's emotions can be provided and positive emotions can be elicited.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The analysis unit analyzes the user's emotions and can simplify the input method if the user is feeling stressed. For example, the emotion estimation function is used to analyze the user's emotions in real time when they are entering room layout information. For example, the emotion estimation function is used to analyze the user's facial expressions and voice and suggest a simplified input method if the user is feeling stressed. Furthermore, if the user is feeling stressed, the analysis unit allows the generation AI to automatically provide a simplified input method. For example, the system omits the input of a detailed floor plan and collects information in the form of simple questions. Furthermore, the analysis unit uses the emotion estimation function to allow the generation AI to provide an input assistance function if the user is feeling stressed. For example, the system uses voice input or auto-complete functions to reduce the input burden on the user. This reduces the user's stress and input burden.

[0101] The floor plan information acquisition unit can automatically generate floor plans based on the user's voice input. For example, we will develop a system in which the generation AI automatically generates floor plans simply by the user describing the room layout verbally. For example, the system analyzes voice input such as, "The living room is 10 tatami mats in size and has two windows" to generate a floor plan. The floor plan information acquisition unit also uses voice recognition technology to convert the user's voice description into text data, and the generation AI automatically generates a floor plan based on that data. For example, it analyzes the voice input information and reflects the size and shape of the room in the drawing. The floor plan information acquisition unit also generates a floor plan in real time as the user describes the room layout verbally, providing feedback to the user. For example, it provides an interface in which the floor plan is dynamically updated in response to voice input. This allows floor plans to be automatically generated simply by the user entering floor plan information verbally.

[0102] The floor plan information acquisition unit can input floor plan information for different rooms simultaneously and integrate it to propose overall interior coordination. For example, we are developing a system in which floor plan information for different rooms is input simultaneously and the generation AI integrates it to propose overall interior coordination. For example, floor plan information for the living room and dining room is input simultaneously to propose an integrated coordination. The floor plan information acquisition unit also allows the generation AI to analyze floor plan information for different rooms and integrate it to propose optimal furniture arrangements. For example, it can propose a unified overall interior style based on floor plan information for the living room and bedroom. Furthermore, when floor plan information for different rooms is input simultaneously, the generation AI automatically analyzes the relationships between the rooms and proposes integrated interior coordination. For example, it makes proposals that take into account the flow of people between the rooms and visual continuity. This makes it possible to integrate floor plan information for multiple rooms and propose overall interior coordination.

[0103] The analysis unit can analyze the user's emotions and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze the user's emotions in real time when entering floor plan information, and an interface for eliciting positive emotions can be provided. For example, the user's facial expressions and voice can be analyzed and positive feedback can be displayed. The analysis unit can also use the emotion estimation function to provide an interface for eliciting positive emotions when the user enters floor plan information. For example, if the user is feeling stressed, relaxing music or images can be displayed. The analysis unit can also use the emotion estimation function to analyze the user's emotions when entering floor plan information and provide advice for eliciting positive emotions. For example, if the user is feeling anxious, an easy entry method or a support message can be displayed. This makes it possible to provide an interface for eliciting positive emotions from the user.

[0104] The coordination suggestion unit can automatically suggest changes to interior coordination according to the season and weather. For example, we will develop a system that automatically suggests changes to the interior coordination proposed by the generation AI according to the season and weather. For example, in summer, cool colors and furniture with light materials are suggested, and in winter, warm colors and furniture with heavy materials are suggested. In addition, the generation AI analyzes weather data to automatically suggest changes to interior coordination according to the season and weather. For example, it suggests the optimal selection of curtains and rugs based on the current weather and season. In addition, when the generation AI suggests changes to interior coordination according to the season and weather, the coordination suggestion unit also takes into account the user's preferences and past selection history. For example, it makes the optimal suggestions for each season based on the interior styles the user has previously chosen. This makes it possible to suggest the optimal interior coordination according to the season and weather.

[0105] The coordination suggestion unit can propose interior coordination that reflects the user's past purchase history and preferences. For example, we will develop a system that reflects the user's past purchase history in the interior coordination proposed by the generation AI. For example, it will propose the optimal coordination based on furniture and decorative items purchased in the past. In addition, in order to propose interior coordination that reflects the user's preferences, the generation AI will analyze the user's past selection history. For example, it will propose the optimal furniture arrangement based on the user's preferred colors and styles. In addition, the coordination suggestion unit will take user feedback into consideration when the generation AI proposes interior coordination that reflects the user's past purchase history and preferences. For example, it will make more appropriate suggestions based on the user's evaluation of past suggestions. This will enable it to propose interior coordination based on the user's past purchase history and preferences.

[0106] The coordination suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the proposed interior coordination and make adjustments to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions the user feels toward the proposed interior coordination in real time. For example, the emotion estimation function is used to analyze the emotions the user feels toward the proposed interior coordination. For example, the emotion estimation function is used to analyze the emotions the user feels toward the proposed interior coordination and make specific adjustments to elicit positive emotions. For example, the emotion estimation function is used to suggest changes to the colors or placement if the user is dissatisfied. The coordination suggestion unit can also use the emotion estimation function to analyze the emotions the user feels toward the proposed interior coordination and provide advice to elicit positive emotions. For example, the emotion estimation function is used to suggest elements that bring joy to the user. This makes it possible to adjust the interior coordination based on the user's emotions and elicit positive emotions.

[0107] The coordination suggestion unit can propose interior coordination that combines the user's existing furniture and decorations. For example, we will develop a system that combines the user's existing furniture and decorations with the interior coordination proposed by the generation AI. For example, it will propose the optimal coordination based on the sofas and tables the user owns. In addition, the coordination suggestion unit analyzes the user's list of possessions to propose interior coordination that combines the user's existing furniture and decorations. For example, it will propose the optimal layout based on the color and style of the owned furniture. In addition, when the generation AI proposes interior coordination that combines the user's existing furniture and decorations, the coordination suggestion unit also takes into account the user's preferences and past selection history. For example, it will make suggestions that make use of the user's existing furniture based on the user's preferred style. This makes it possible to propose interior coordination that makes use of the furniture and decorations the user already owns.

[0108] The coordination suggestion unit can propose hybrid coordination that combines different interior styles. For example, we will develop a system in which the generation AI proposes hybrid coordination that combines different interior styles. For example, we will propose a coordination that combines modern and classic elements. In addition, in order to propose hybrid coordination that combines different interior styles, the generation AI analyzes the characteristics of the styles. For example, it will propose the optimal combination based on the characteristics of the colors and materials of each style. In addition, the coordination suggestion unit will propose hybrid coordination that combines different interior styles by taking into account the user's preferences and past selection history. For example, it will propose the optimal combination based on the style that the user prefers. This makes it possible to propose hybrid coordination that combines different interior styles.

[0109] The product search unit can use the emotion estimation function to analyze the emotions a user has toward the lowest priced product and make adjustments to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions a user has toward the lowest priced product in real time. For example, the emotion estimation function is used to analyze the emotions a user has toward the lowest priced product and make adjustments to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions a user has toward the lowest priced product and make specific adjustments to elicit positive emotions. For example, if the user is dissatisfied, the product search unit can suggest changing the product description or image. The product search unit can also use the emotion estimation function to analyze the emotions a user has toward the lowest priced product and provide advice to elicit positive emotions. For example, the product search unit can suggest products that emphasize elements that the user finds enjoyable. This makes it possible to adjust the lowest priced product based on the user's emotions and elicit positive emotions.

[0110] The product search unit can search inventory information not only from multiple online shopping sites but also from local stores. For example, when the generation AI searches for the lowest price product, we will develop a system that searches inventory information from local stores as well as from multiple online shopping sites. For example, it will compare online and offline prices and suggest the best value product. In addition, the product search unit will add a function to search inventory information from local stores, so that when the generation AI searches for the lowest price product, it will comprehensively compare online and offline prices. For example, it will take into account discount information at local stores to suggest the most suitable product. In addition, when the generation AI searches for the lowest price product, the product search unit will also take into account inventory information at local stores, improving user convenience. For example, it will prioritize suggesting products that can be picked up on the same day at a nearby store. This will enable it to comprehensively compare online and offline prices and suggest the best value product.

[0111] The product search unit can prioritize searches for products from specific brands or manufacturers specified by the user. For example, we will develop a system in which, when the generation AI searches for the lowest-priced products, it prioritizes searches for products from specific brands or manufacturers specified by the user. For example, if the user specifies "a specific brand of sofa," it will prioritize searches for products from that brand. We will also add a function to the product search unit to prioritize searches for products from specific brands or manufacturers specified by the user, so that the generation AI will reflect the user's preferences when searching for the lowest-priced products. For example, it will suggest optimal products based on products from brands that the user has previously purchased. We will also add a function to the product search unit to prioritize searches for products from specific brands or manufacturers specified by the user when the generation AI searches for the lowest-priced products, improving user satisfaction. For example, it will prioritize suggestions for products from brands that the user trusts. This will allow it to prioritize searches for products from the brands or manufacturers specified by the user.

[0112] The product search unit can use the emotion estimation function to analyze the emotions of a user when searching for the lowest priced product in real time and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used ... and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze the emotions of a user when searching for the lowest priced product and provide an interface for eliciting positive emotions. For example, if the user is feeling stressed, the emotion estimation function can be used to display relaxing music or images. The product search unit can also use the emotion estimation function to analyze the emotions of a user when searching for the lowest priced product and provide advice for eliciting positive emotions. For example, if the user is feeling anxious, the emotion estimation function can be used to display a simple search method or a support message. In this way, the lowest priced product can be searched for based on the user's emotions and positive emotions can be elicited.

[0113] The integrated presentation unit can integrate suggestions taking into account the user's lifestyle and family composition. For example, we will develop a system in which the generation AI takes into account the user's lifestyle and family composition when integrating suggestions. For example, it will suggest furniture layouts that prioritize safety for households with children. The integrated presentation unit also takes into account the user's lifestyle and family composition, allowing the generation AI to make optimal suggestions. For example, it will suggest a compact and functional furniture layout for a user living alone, and a spacious living room layout for a large family. The integrated presentation unit also takes into account the user's lifestyle and family composition when integrating suggestions, making suggestions tailored to the user's needs. For example, it will suggest pet-friendly furniture layouts for households with pets. This makes it possible to make optimal suggestions based on the user's lifestyle and family composition.

[0114] The integrated presentation unit visualizes the proposals as a 3D model, making it easier for users to visualize the actual room. For example, we will develop a system that visualizes the proposals as a 3D model when the generation AI integrates the proposals. For example, we will allow users to check the proposed interior coordination in a 3D model. The integrated presentation unit also visualizes the proposals as a 3D model, making it easier for users to visualize the actual room. For example, the furniture arrangement and color combinations can be checked in a 3D model, making a proposal that satisfies the user. The integrated presentation unit also visualizes the proposals as a 3D model when the generation AI integrates the proposals, providing an interface that makes it easier for users to visualize the actual room. For example, the user can operate the 3D model to check the furniture arrangement. In this way, visualizing the proposals as a 3D model makes it easier for users to visualize the actual room.

[0115] The integrated presentation unit can use the emotion estimation function to analyze the emotion of the user when reviewing the proposal content and make adjustments to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotion of the user when reviewing the proposal content in real time. For example, the emotion estimation function is used to analyze the emotion of the user when reviewing the proposal content. For example, the emotion estimation function is used to analyze the emotion of the user when reviewing the proposal content and make adjustments to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotion of the user when reviewing the proposal content and make specific adjustments to elicit positive emotions. For example, if the user is dissatisfied, the integrated presentation unit can make a suggestion to change the proposal content. For example, the integrated presentation unit can use the emotion estimation function to analyze the emotion of the user when reviewing the proposal content and provide advice to elicit positive emotions. For example, the integrated presentation unit can make a suggestion to emphasize elements that make the user feel happy. In this way, the proposal content can be adjusted based on the user's emotion and elicit positive emotions.

[0116] The integrated presentation unit can provide an interface that allows users to customize the proposals. For example, we will develop a system that provides an interface that allows users to customize the proposals when the generation AI integrates the proposals. For example, we will allow users to freely change the furniture arrangement and color combinations. The integrated presentation unit also provides an interface that allows users to customize the proposals, allowing them to adjust the proposals to suit their own preferences. For example, we will add a function that allows users to edit the proposed interior coordination themselves. The integrated presentation unit also provides an interface that allows users to customize the proposals when the generation AI integrates the proposals, improving user satisfaction. For example, we will allow users to change the proposals in real time and check the results. In this way, by providing an interface that allows users to customize the proposals, user satisfaction can be improved.

[0117] The integrated presentation unit can present the proposals as multiple variations, allowing the user to select from them. For example, we will develop a system in which, when the generation AI integrates the proposals, it presents the proposals as multiple variations. For example, it may present variations of different interior styles or furniture arrangements to the user. The integrated presentation unit may also present the proposals as multiple variations, allowing the user to select according to their preferences. For example, it may allow the user to select their favorite interior coordination from the proposed interior coordinations. The integrated presentation unit may also present the proposals as multiple variations, expanding the user's options when the generation AI integrates the proposals. For example, it may allow the user to compare different color or material combinations. This will expand the user's options by presenting the proposals as multiple variations.

[0118] When analyzing user feedback, the feedback analysis unit can take into account not only the content of the feedback but also the user's past behavioral history. For example, we will develop a system in which the generation AI analyzes user feedback and takes into account not only the content of the feedback but also the user's past behavioral history. For example, it makes optimal re-suggestions based on past purchase history and interior coordination selection history. Furthermore, when analyzing user feedback, the feedback analysis unit takes into account past behavioral history so that the generation AI can make more appropriate re-suggestions. For example, it makes re-suggestions based on styles and colors that the user has selected in the past. Furthermore, when the generation AI analyzes user feedback, the feedback analysis unit takes into account past behavioral history so that it can make re-suggestions that are tailored to the user's preferences and needs. For example, it makes re-suggestions based on suggestions that the user has given high ratings to in the past. This allows it to make more appropriate re-suggestions by taking into account the user's past behavioral history.

[0119] The feedback analysis unit analyzes the emotional tone of the feedback and can make improvements, especially in response to negative feedback. For example, a system is developed that analyzes the emotional tone of the feedback in real time when the generation AI analyzes user feedback. For example, it analyzes user comments and ratings and makes improvements, especially in response to negative feedback. The feedback analysis unit also analyzes the emotional tone of the feedback, and the generation AI makes specific improvement suggestions to make improvements, especially in response to negative feedback. For example, it identifies areas where the user is dissatisfied and proposes improvements. The feedback analysis unit also takes the emotional tone into consideration when the generation AI analyzes user feedback and makes improvements, especially in response to negative feedback. For example, if the user is dissatisfied, it makes alternative or additional suggestions. This makes it possible to make improvements, especially in response to negative feedback, thereby improving user satisfaction.

[0120] The feedback analysis unit can compare the feedback with that of other users and identify common areas for improvement. For example, a system is developed in which, when the generation AI analyzes a user's feedback, it compares it with the feedback of other users. For example, feedback from multiple users is analyzed and common areas for improvement are identified. The feedback analysis unit also compares it with the feedback of other users, allowing the generation AI to identify common areas for improvement and make specific improvement suggestions. For example, it identifies areas where multiple users are dissatisfied and proposes improvement measures. The feedback analysis unit also compares the user's feedback with that of other users and identifies common areas for improvement. For example, if multiple users point out the same problem, it makes suggestions to solve the problem. This allows the generation AI to identify common areas for improvement by comparing it with the feedback of other users and make more effective improvement suggestions.

[0121] The feedback analysis unit can automatically classify the content of the feedback and make re-suggestions for each different category. For example, a system is developed that automatically classifies the content of the feedback when the generation AI analyzes user feedback. For example, it may classify the feedback into positive feedback, negative feedback, improvement suggestions, etc. Furthermore, the feedback analysis unit automatically classifies the content of the feedback and makes specific suggestions so that the generation AI can make re-suggestions for each different category. For example, it may propose improvement measures for negative feedback and make additional suggestions for positive feedback. Furthermore, the feedback analysis unit automatically classifies the content of the feedback when the generation AI analyzes user feedback and makes re-suggestions for each different category. For example, it may identify areas that need improvement and make specific improvement suggestions. In this way, by automatically classifying the content of the feedback and making re-suggestions for each different category, more appropriate improvement suggestions can be made.

[0122] The feedback analysis unit can use the emotion estimation function to analyze the emotion a user has when providing feedback in real time and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze the emotion a user has when providing feedback in real time and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze the emotion a user has when providing feedback and provide an interface for eliciting positive emotions. For example, the user's facial expression and voice can be analyzed and positive feedback can be displayed. The feedback analysis unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when the user provides feedback. For example, if the user is feeling stressed, relaxing music or images can be displayed. The feedback analysis unit also uses the emotion estimation function to analyze the emotion a user has when providing feedback and provide advice for eliciting positive emotions. For example, if the user is feeling anxious, simple operating instructions or a support message can be displayed. In this way, an interface for providing feedback based on the user's emotions can be provided and positive emotions can be elicited.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The floor plan information acquisition unit acquires the floor plan information of the room. For example, it can receive blueprints, photos, text data, etc. provided by the user. It can also receive photos of the room taken by the user with a smartphone and extract floor plan information using image analysis technology. It can also acquire the room dimensions and layout information entered by the user as text data. Step 2: The analysis unit analyzes the floor plan information acquired by the floor plan information acquisition unit. For example, the generation AI analyzes the size and shape of the room, as well as the position of windows and doors, to identify the space where furniture can be placed. It can also automatically estimate the purpose of the room and perform analysis based on that. It can also perform analysis taking into account the way light enters and ventilation. Step 3: The coordination suggestion unit proposes interior coordination based on the floor plan information analyzed by the analysis unit. For example, the generation AI proposes the optimal arrangement of furniture and decorations based on the user's desired interior style and budget. It can also automatically suggest changes to interior coordination according to the season and weather. It can also propose interior coordination that reflects the user's past purchase history and preferences. Step 4: The product search unit searches online shopping sites for the lowest priced products based on the interior design suggested by the coordination suggestion unit. For example, the generation AI searches multiple online shopping sites for the suggested furniture and decorative items and identifies the products offered at the lowest prices. It can also take into account product reviews and ratings to prioritize the suggestions of high-quality products. Furthermore, it can also consider not only the product price but also delivery time and shipping costs to suggest the best overall value products. Step 5: The integrated presentation unit integrates the proposals obtained by the coordination proposal unit and the product search unit and presents them to the user. For example, the generation AI can combine the interior coordination proposals and the results of the lowest price product search into a single proposal and present it to the user. The proposals can also be visualized as 3D models, making it easier for users to visualize the actual room. Furthermore, an interface can be provided that allows users to customize the proposals. Step 6: The feedback analysis unit analyzes the user's feedback and makes new suggestions. For example, the generation AI analyzes the user's feedback and creates new interior coordination and searches for the cheapest products based on the user's requests. It can also analyze the user's emotions and provide an interface to elicit positive emotions. It can also compare the feedback with that of other users to identify common areas for improvement.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0127] 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.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 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.

[0142] 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.

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

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

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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 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.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the robot 414, 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 robot 414 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.

[0170] 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.

[0171] 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.

[0172] 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 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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. [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. a floor plan information acquisition unit that acquires room layout information; an analysis unit that analyzes the floor plan information acquired by the floor plan information acquisition unit; a coordination suggestion unit that suggests interior coordination based on the floor plan information analyzed by the analysis unit; a product search unit that searches online shopping sites for the lowest priced products based on the interior design suggested by the coordinate suggestion unit; an integrated presentation unit that integrates the proposal contents obtained by the coordination proposal unit and the product search unit and presents the integrated proposal contents to a user; A feedback analysis unit that analyzes user feedback and makes a new proposal. A system characterized by:

2. The analysis unit When analyzing the floor plan information, not only the space where furniture can be placed but also the way light enters and ventilation are taken into consideration.

2. The system of claim 1.

3. The analysis unit When analyzing the floor plan information, the purpose of use of the room is automatically estimated and analysis is performed based on that.

2. The system of claim 1.

4. The analysis unit The user's emotions are analyzed, and if the user is feeling stressed, the input method is simplified.

2. The system of claim 1.

5. The floor plan information acquisition unit Automatically generating the floor plan based on the user's voice input.

2. The system of claim 1.

6. The floor plan information acquisition unit Input the layout information of the different rooms at the same time and integrate them to propose the overall interior coordination 2. The system of claim 1.

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