system

Abstract

The system according to this embodiment aims to efficiently propose interior plans based on the user's preferences and to facilitate the seamless process from planning to purchasing furniture. [Solution] The system according to the embodiment comprises a preference recognition unit, a model generation unit, a plan proposal unit, and a coordination unit. The preference recognition unit recognizes the user's preferences. The model generation unit generates a 3D model of the room based on the preferences recognized by the preference recognition unit. The plan proposal unit proposes an interior plan based on the 3D model generated by the model generation unit. The coordination unit coordinates the purchase of furniture based on the interior plan proposed by the plan proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently propose an interior plan based on the user's preferences and seamlessly proceed to the purchase of furniture.

[0005] The system according to the embodiment aims to efficiently propose an interior plan based on the user's preferences and seamlessly proceed to the purchase of furniture.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a preference recognition unit, a model generation unit, a plan proposal unit, and a coordination unit. The preference recognition unit recognizes the user's preferences. The model generation unit generates a 3D model of the room based on the preferences recognized by the preference recognition unit. The plan proposal unit proposes an interior plan based on the 3D model generated by the model generation unit. The coordination unit coordinates the purchase of furniture based on the interior plan proposed by the plan proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment efficiently proposes interior plans based on the user's preferences and allows for seamless furniture purchases. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The interior plan proposal system according to an embodiment of the present invention is a system in which a user generates a 3D model by taking a picture of a room with their smartphone camera, and a generating AI proposes an interior plan that suits the user's preferences and budget. This interior plan proposal system generates a 3D model by taking a picture of a room with the user's smartphone camera, and the generating AI proposes an interior plan that suits the user's preferences and budget. Furthermore, it enables seamless furniture purchase through integration with e-commerce sites. As a result, the user can experience efficient and comfortable interior planning, and the effort involved in selecting and arranging furniture can be greatly reduced. For example, the system can understand the user's preferences in detail by showing the user multiple types of interior photos and having them select "like" or "dislike". Next, the user generates a 3D model by taking a picture of the room with their smartphone camera. At this time, it is recommended to take pictures from multiple angles so that the overall layout of the room can be seen. The captured images are input to the generating AI, and a 3D model is generated. The generating AI analyzes the captured images and accurately reproduces the dimensions and shape of the room. Based on the generated 3D model, the generating AI proposes an interior plan that suits the user's preferences and budget. For example, if a user prefers modern design and has a limited budget, the generative AI will suggest cost-effective furniture within that modern design category. It can also present multiple plans based on the user's budget. Furthermore, integration with e-commerce sites allows users to seamlessly purchase the suggested furniture. Based on the suggested interior plan, the generative AI lists furniture available for purchase on e-commerce sites. Users can then select from the list and proceed with the purchase. This system allows users to experience efficient and comfortable interior planning. For example, it significantly reduces the time spent selecting and arranging furniture. Personalized suggestions from the generative AI also improve user satisfaction. For instance, users can easily find interiors that suit their preferences, reducing the indecision they face when selecting furniture. Additionally, the generative AI utilizes user preference data and trend data to provide even more accurate suggestions.For example, by offering suggestions that reflect the latest interior design trends, the system provides users with attractive plans. This interior design plan suggestion system generates a 3D model when the user takes a picture of their room with their smartphone camera, and the generating AI then suggests an interior design plan tailored to the user's preferences and budget. Integration with e-commerce sites enables seamless furniture purchases, allowing users to experience efficient and comfortable interior planning. As a result, the interior design plan suggestion system can understand the user's preferences in detail, generate a 3D model, propose an interior design plan, and facilitate seamless furniture purchases.

[0029] The interior plan proposal system according to this embodiment comprises a preference recognition unit, a model generation unit, a plan proposal unit, and a linkage unit. The preference recognition unit recognizes the user's preferences. For example, the preference recognition unit recognizes the user's preferences by presenting the user with multiple interior photos and having them select "like" or "dislike." For example, the preference recognition unit can more accurately recognize the preferences of a user who likes modern designs by showing them many modern interior photos. The preference recognition unit can also accumulate user preference data and utilize it for future proposals. For example, the preference recognition unit can analyze the user's preference trends based on data of interior photos the user has selected in the past. The model generation unit generates a 3D model of the room based on the preferences recognized by the preference recognition unit. For example, the model generation unit analyzes images of the room taken by the user with a smartphone camera and reproduces the dimensions and shape of the room. For example, the model generation unit can accurately reproduce the height and width of the walls, the arrangement of furniture, etc., of the room based on the captured images. The model generation unit can also integrate images taken from multiple angles to generate a more detailed 3D model. The plan proposal unit proposes interior plans based on the 3D models generated by the model generation unit. The plan proposal unit proposes multiple interior plans based on the user's preferences and budget, for example. For example, it can propose a modern interior plan to a user who prefers modern designs, and suggest cost-effective furniture if the budget is limited. The plan proposal unit can also present multiple plans according to the user's budget. The integration unit integrates with the purchase of furniture based on the interior plans proposed by the plan proposal unit. For example, the integration unit lists furniture available for purchase on e-commerce sites, allowing the user to select from the list and proceed with the purchase. For example, the integration unit can automatically list furniture available for purchase on e-commerce sites based on the proposed interior plan, allowing the user to select from the list and proceed with the purchase.As a result, the interior plan proposal system according to this embodiment can understand the user's preferences in detail, generate a 3D model, propose an interior plan, and seamlessly facilitate the purchase of furniture.

[0030] The preference recognition unit understands the user's preferences. For example, it can understand the user's preferences by presenting the user with multiple interior design photos and having them select "like" or "dislike." Specifically, the preference recognition unit collects data on the interior design photos selected by the user and analyzes the user's preference trends based on this data. For example, if a user selects many photos of modern designs, the preference recognition unit will determine that the user prefers modern designs. The preference recognition unit can also accumulate user preference data and use it for future suggestions. For example, by tracking changes in the user's preferences based on data of interior design photos selected by the user in the past, it can make more accurate suggestions. Furthermore, the preference recognition unit can compare user preference data with data from other users to find common trends. As a result, the preference recognition unit can understand the user's preferences in detail and provide a foundation for proposing interior design plans that meet individual needs.

[0031] The model generation unit generates a 3D model of the room based on the preferences identified by the preference recognition unit. Specifically, the model generation unit analyzes images of the room taken by the user with their smartphone camera and reproduces the room's dimensions and shape. For example, based on the captured images, the model generation unit can accurately reproduce the height and width of the room's walls, the placement of furniture, and other details. Furthermore, the model generation unit can integrate images taken from multiple angles to generate a more detailed 3D model. This allows users to consider interior design plans while viewing an accurate 3D model of their own room. The model generation unit can also suggest optimal furniture placement and design based on the room's dimensions and shape provided by the user. For example, it can suggest optimizing furniture placement according to the room's shape and dimensions to make effective use of space. In this way, the model generation unit can provide a foundation for offering optimal interior design plans tailored to the user's preferences and the characteristics of the room.

[0032] The plan proposal unit proposes interior plans based on the 3D models generated by the model generation unit. Specifically, the plan proposal unit proposes multiple interior plans based on the user's preferences and budget. For example, it can propose a modern interior plan to a user who prefers modern designs, and suggest cost-effective furniture if the budget is limited. Furthermore, the plan proposal unit can present multiple plans according to the user's budget. For example, it can present plans in different price ranges, such as a plan using high-end furniture and a plan using affordable furniture, allowing the user to choose. The plan proposal unit can also make suggestions to optimize furniture placement and design according to the user's preferences and budget. In this way, the plan proposal unit can provide the optimal interior plan that meets the user's needs and support them in realizing an interior that satisfies them.

[0033] The Integration Department coordinates the purchase of furniture based on the interior design plans proposed by the Plan Proposal Department. Specifically, the Integration Department lists furniture available for purchase on e-commerce sites, allowing users to select items from the list and proceed with the purchase. For example, the Integration Department can automatically list furniture available for purchase on e-commerce sites based on the proposed interior design plan, allowing users to select items from the list and proceed with the purchase. Furthermore, the Integration Department can check the inventory status and delivery information of the furniture selected by the user in real time. This allows users to smoothly purchase the necessary furniture based on the proposed interior design plan. The Integration Department can also track the progress of the purchase process and notify the user. For example, it notifies the user when the order is confirmed and updates the estimated delivery date and delivery status in real time. This allows the Integration Department to help users smoothly purchase furniture to realize their interior design plan and keep track of the delivery status.

[0034] The preference recognition unit can understand the user's preferences by presenting them with multiple interior design photos and having them select "like" or "dislike." For example, by showing the user many photos of modern interior designs, the preference recognition unit can gain a detailed understanding of the user's preferences. The preference recognition unit can also accumulate user preference data and use it for future suggestions. For example, the preference recognition unit can analyze the user's preference trends based on data of interior design photos the user has selected in the past. This allows for a detailed understanding of the user's preferences. Some or all of the above processing in the preference recognition unit may be performed using AI, or not. For example, the preference recognition unit can input user preference data into AI and have the AI ​​analyze the user's preference trends.

[0035] The model generation unit can analyze images of a room taken by a user with their smartphone camera and reproduce the dimensions and shape of the room. For example, the model generation unit can accurately reproduce the height and width of the room walls, the arrangement of furniture, etc., based on images of the room taken by a user with their smartphone camera. The model generation unit can also integrate images taken from multiple angles to generate a more detailed 3D model. For example, the model generation unit can accurately reproduce the dimensions and shape of a room based on the captured images. This allows for accurate reproduction of the room's dimensions and shape. Some or all of the above-described processes in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input the captured images into a generation AI and have the generation AI perform the reproduction of the room's dimensions and shape.

[0036] The plan proposal unit can propose multiple interior design plans based on the user's preferences and budget. For example, the plan proposal unit can propose a modern design interior plan based on the user's preferences and budget. Furthermore, the plan proposal unit can present multiple plans according to the user's budget. For example, the plan proposal unit can propose cost-effective furniture based on the user's preferences and budget. This allows for the proposal of interior design plans that meet the user's preferences and budget. Some or all of the above-described processes in the plan proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the plan proposal unit can input user preference and budget data into a generative AI and have the generative AI generate interior design plan proposals.

[0037] The integration unit can list furniture available for purchase on e-commerce sites based on the proposed interior plan. For example, the integration unit can automatically list furniture available for purchase on e-commerce sites based on the proposed interior plan. This allows users to seamlessly purchase furniture based on the proposed interior plan. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input data on the proposed interior plan into the AI ​​and have the AI ​​perform the task of listing furniture available for purchase.

[0038] The integration unit enables users to select from a list and proceed with the purchase. For example, the integration unit allows users to select furniture listed based on a proposed interior plan and proceed with the purchase. This makes it easy for users to purchase the proposed furniture. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input the data of the listed furniture into the AI ​​and have the AI ​​execute the purchase procedure.

[0039] The preference recognition unit can analyze the user's past preference data and select the most suitable interior photos. For example, the preference recognition unit can analyze the styles of interior photos that the user has previously selected as "liked" and prioritize presenting photos of similar styles. It can also select photos to present that avoid the characteristics of interior photos that the user has previously selected as "disliked." Furthermore, the preference recognition unit can select interior photos related to specific seasons or events based on the user's past preference data. This allows the system to present the most suitable interior photos based on the user's past preference data. Some or all of the above processing in the preference recognition unit may be performed using AI, or not. For example, the preference recognition unit can input the user's past preference data into an AI and have the AI ​​select the most suitable interior photos.

[0040] The preference recognition unit can filter interior photos based on the user's current living situation and areas of interest. For example, if the user owns a pet, the preference recognition unit will prioritize displaying pet-friendly interior photos. If the user works remotely, the preference recognition unit can display interior photos suitable for a home office. Furthermore, if the user has started a new hobby, the preference recognition unit can display interior photos related to that hobby. This allows the display of interior photos tailored to the user's living situation and areas of interest. Some or all of the above processing in the preference recognition unit may be performed using AI, or not. For example, the preference recognition unit can input data on the user's living situation and areas of interest into an AI, and have the AI ​​perform the filtering of interior photos.

[0041] The preference recognition unit can prioritize displaying highly relevant interior photos by considering the user's geographical location when presenting interior photos. For example, if the user lives in a cold climate, the preference recognition unit can prioritize displaying warm-themed interior photos. If the user lives in an urban area, the preference recognition unit can display compact and functional interior photos. Furthermore, if the user lives by the sea, the preference recognition unit can display ocean-themed interior photos. This allows the display of highly relevant interior photos based on the user's geographical location. Some or all of the above processing in the preference recognition unit may be performed using AI, for example, or without AI. For example, the preference recognition unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the task of displaying highly relevant interior photos.

[0042] The preference recognition unit can analyze the user's social media activity when presenting interior photos and display relevant photos. For example, the preference recognition unit can analyze the style of interior photos that the user has "liked" on social media and display photos of a similar style. It can also prioritize displaying works by interior designers that the user follows. Furthermore, the preference recognition unit can analyze the content of the user's social media posts and display interior photos related to themes of interest. This allows the display of relevant interior photos based on the user's social media activity. Some or all of the above processing in the preference recognition unit may be performed using AI, for example, or without AI. For example, the preference recognition unit can input data on the user's social media activity into an AI and have the AI ​​display relevant interior photos.

[0043] The model generation unit can improve accuracy when generating 3D models by considering room lighting conditions and furniture placement. For example, the model generation unit can analyze room lighting conditions and generate a 3D model considering the effects of natural and artificial light. It can also analyze furniture placement and generate a 3D model based on the actual placement. Furthermore, the model generation unit can generate a more realistic 3D model by considering the color of the room walls and the type of flooring. As a result, by considering room lighting conditions and furniture placement, a more accurate 3D model can be generated. Some or all of the above processing in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input data on room lighting conditions and furniture placement into a generation AI and have the generation AI perform 3D model generation.

[0044] The model generation unit can select the optimal generation method by referring to the user's past room image data when generating a 3D model. For example, the model generation unit can analyze image data of a room previously taken by the user and generate a 3D model in a similar style. Furthermore, the model generation unit can analyze furniture arrangement and design trends from the user's past room image data and select the optimal generation method. In addition, the model generation unit can accurately reproduce the dimensions and shape of a room based on the user's past room image data. This allows for the generation of an optimal 3D model based on the user's past room image data. Some or all of the above-described processes in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input the user's past room image data into a generation AI and have the generation AI select the optimal generation method.

[0045] The model generation unit can select the optimal generation method when generating a 3D model, taking into account the user's geographical location information. For example, if the user lives in a cold region, the model generation unit can generate a 3D model using warm colors and materials. If the user lives in an urban area, the model generation unit can generate a compact and functional 3D model. Furthermore, if the user lives by the sea, the model generation unit can generate a 3D model with a sea-themed design. This allows for the generation of the optimal 3D model based on the user's geographical location information. Some or all of the above-described processes in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal generation method.

[0046] The model generation unit can analyze the user's social media activity and reflect relevant data when generating 3D models. For example, the model generation unit can generate a 3D model that reflects the interior design that the user has "liked" on social media. It can also generate a 3D model that reflects the style of the interior designers that the user follows. Furthermore, the model generation unit can analyze the content of the user's social media posts and generate a 3D model that reflects themes of interest. This allows for the generation of 3D models that reflect relevant data based on the user's social media activity. Some or all of the above processing in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the reflection of relevant data.

[0047] The plan proposal unit can select the optimal plan when proposing an interior design plan by referring to the user's past preference data. For example, the plan proposal unit can analyze the style of interior design plans that the user has previously selected as "liked" and propose plans of a similar style. The plan proposal unit can also select plans that avoid the characteristics of interior design plans that the user has previously selected as "disliked". Furthermore, the plan proposal unit can also propose interior design plans related to specific seasons or events based on the user's past preference data. This allows the system to propose the optimal interior design plan based on the user's past preference data. Some or all of the above processing in the plan proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the plan proposal unit can input the user's past preference data into a generative AI and have the generative AI select the optimal plan.

[0048] The plan proposal unit can customize interior design plans based on the user's current living situation and budget. For example, if the user has a pet, the plan proposal unit can propose a pet-friendly interior design plan. If the user works remotely, the plan proposal unit can propose an interior design plan suitable for a home office. Furthermore, the plan proposal unit can propose a cost-effective interior design plan according to the user's budget. This allows for the proposal of interior design plans tailored to the user's living situation and budget. Some or all of the above-described processes in the plan proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the plan proposal unit can input data on the user's living situation and budget into a generative AI and have the generative AI perform the plan customization.

[0049] The plan proposal unit can analyze a user's social media activity and propose relevant plans when suggesting interior design plans. For example, the plan proposal unit can analyze the styles of interior design plans that the user has "liked" on social media and propose plans of a similar style. It can also prioritize suggesting works by interior designers that the user follows. Furthermore, the plan proposal unit can analyze the content of the user's social media posts and propose interior design plans related to themes of interest. This allows the system to propose relevant interior design plans based on the user's social media activity. Some or all of the above processing in the plan proposal unit may be performed using, for example, a generative AI, or without one. For example, the plan proposal unit can input data on the user's social media activity into a generative AI and have the AI ​​generate relevant plan suggestions.

[0050] The integration unit can select the most suitable furniture by referring to the user's past purchase history when listing furniture. For example, the integration unit can analyze the style of furniture the user has purchased in the past and list furniture of a similar style. The integration unit can also prioritize listing furniture from brands the user has previously purchased. Furthermore, the integration unit can list furniture related to specific seasons or events based on the user's past purchase history. This allows the integration unit to list the most suitable furniture based on the user's past purchase history. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the integration unit can input data from the user's past purchase history into a generative AI and have the generative AI select the most suitable furniture.

[0051] The integration unit can customize the furniture list based on the user's current living situation and budget. For example, if the user has a pet, the integration unit can list pet-friendly furniture. If the user works remotely, the integration unit can list furniture suitable for a home office. Furthermore, the integration unit can list cost-effective furniture according to the user's budget. This allows the system to list furniture that suits the user's living situation and budget. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not. For example, the integration unit can input data on the user's living situation and budget into the generative AI and have the generative AI perform the list customization.

[0052] The collaborative unit can select the most suitable furniture when listing furniture, taking into account the user's geographical location. For example, if the user lives in a cold region, the collaborative unit will prioritize listing furniture made of warm materials and designs. If the user lives in an urban area, the collaborative unit can list compact and functional furniture. Furthermore, if the user lives by the sea, the collaborative unit can list furniture with a sea-themed design. This allows the system to list the most suitable furniture based on the user's geographical location. Some or all of the above processing in the collaborative unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collaborative unit can input the user's geographical location information into a generative AI and have the generative AI select the most suitable furniture.

[0053] The integration unit can analyze a user's social media activity when listing furniture and list relevant furniture. For example, the integration unit can analyze the styles of furniture that a user has "liked" on social media and list furniture of similar styles. The integration unit can also prioritize listing works by interior designers that the user follows. Furthermore, the integration unit can analyze the content of a user's social media posts and list furniture related to themes of interest. This allows the integration unit to list relevant furniture based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the integration unit can input data on the user's social media activity into a generative AI and have the generative AI perform the task of listing relevant furniture.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The interior design proposal system can also acquire user health data and propose interior designs based on their health status. For example, if a user has allergies, it can suggest furniture made from allergen-free materials. If a user needs relaxation, it can suggest interiors with relaxing colors and designs. Furthermore, if a user is not getting enough exercise, it can suggest interior layouts that encourage exercise. This allows the system to provide interior designs tailored to the user's health condition.

[0056] The interior design proposal system can also acquire the user's family structure data and propose interior designs that ensure comfort for the entire family. For example, it can suggest furniture that prioritizes safety for families with young children. It can also suggest barrier-free interior designs for families with elderly members. Furthermore, it can suggest pet-friendly furniture and layouts for families with pets. This allows the system to provide interior designs that ensure comfort for the entire family.

[0057] The interior design proposal system can also propose interior designs that take into account the user's hobbies and interests. For example, it can suggest a comfortable reading space for a user who enjoys reading. It can also suggest an interior design that takes acoustics into consideration for a user who enjoys music. Furthermore, it can suggest a user-friendly kitchen layout for a user who enjoys cooking. This allows the system to provide interior designs that are tailored to the user's hobbies and interests.

[0058] The interior design planning system can also acquire user lifestyle data and propose interior designs tailored to that lifestyle. For example, it can suggest an efficient home office layout for users who work remotely. It can also suggest storage space for outdoor equipment for users who enjoy the outdoors. Furthermore, it can suggest a comfortable guest space for users who frequently invite guests. This allows the system to provide interior designs that match the user's lifestyle.

[0059] The interior design proposal system can also acquire user energy consumption data and propose interior designs that take energy efficiency into consideration. For example, it can suggest energy-efficient lighting and appliances to users with high energy consumption. It can also suggest layouts that maximize the use of natural light. Furthermore, it can suggest curtains and windows with high insulation properties. In this way, it can provide interior designs that take energy efficiency into consideration.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The preference assessment unit understands the user's preferences. For example, it can assess the user's preferences by showing them multiple interior design photos and having them select "like" or "dislike." Furthermore, by showing users who like modern designs more modern interior design photos, the system can more accurately understand their preferences. In addition, user preference data can be accumulated and used for future suggestions. Step 2: The model generation unit generates a 3D model of the room based on the preferences identified by the preference recognition unit. For example, it analyzes images of the room taken by the user with their smartphone camera and reproduces the room's dimensions and shape. Furthermore, it can accurately reproduce the height and width of the room's walls, the placement of furniture, and other details based on the captured images. It can also integrate images taken from multiple angles to generate a more detailed 3D model. Step 3: The plan proposal unit proposes interior plans based on the 3D model generated by the model generation unit. For example, it can propose multiple interior plans based on the user's preferences and budget. For users who prefer modern designs, it can propose a modern interior plan, and for users with limited budgets, it can propose cost-effective furniture. It can also present multiple plans according to the user's budget. Step 4: The Integration Department integrates with the Plan Proposal Department to purchase furniture based on the proposed interior design plan. For example, it lists furniture available for purchase on e-commerce sites, allowing users to select from the list and proceed with the purchase. Based on the proposed interior design plan, it automatically lists furniture available for purchase on e-commerce sites, allowing users to select from the list and proceed with the purchase.

[0062] (Example of form 2) The interior plan proposal system according to an embodiment of the present invention is a system in which a user generates a 3D model by taking a picture of a room with their smartphone camera, and a generating AI proposes an interior plan that suits the user's preferences and budget. This interior plan proposal system generates a 3D model by taking a picture of a room with the user's smartphone camera, and the generating AI proposes an interior plan that suits the user's preferences and budget. Furthermore, it enables seamless furniture purchase through integration with e-commerce sites. As a result, the user can experience efficient and comfortable interior planning, and the effort involved in selecting and arranging furniture can be greatly reduced. For example, the system can understand the user's preferences in detail by showing the user multiple types of interior photos and having them select "like" or "dislike". Next, the user generates a 3D model by taking a picture of the room with their smartphone camera. At this time, it is recommended to take pictures from multiple angles so that the overall layout of the room can be seen. The captured images are input to the generating AI, and a 3D model is generated. The generating AI analyzes the captured images and accurately reproduces the dimensions and shape of the room. Based on the generated 3D model, the generating AI proposes an interior plan that suits the user's preferences and budget. For example, if a user prefers modern design and has a limited budget, the generative AI will suggest cost-effective furniture within that modern design category. It can also present multiple plans based on the user's budget. Furthermore, integration with e-commerce sites allows users to seamlessly purchase the suggested furniture. Based on the suggested interior plan, the generative AI lists furniture available for purchase on e-commerce sites. Users can then select from the list and proceed with the purchase. This system allows users to experience efficient and comfortable interior planning. For example, it significantly reduces the time spent selecting and arranging furniture. Personalized suggestions from the generative AI also improve user satisfaction. For instance, users can easily find interiors that suit their preferences, reducing the indecision they face when selecting furniture. Additionally, the generative AI utilizes user preference data and trend data to provide even more accurate suggestions.For example, by offering suggestions that reflect the latest interior design trends, the system provides users with attractive plans. This interior design plan suggestion system generates a 3D model when the user takes a picture of their room with their smartphone camera, and the generating AI then suggests an interior design plan tailored to the user's preferences and budget. Integration with e-commerce sites enables seamless furniture purchases, allowing users to experience efficient and comfortable interior planning. As a result, the interior design plan suggestion system can understand the user's preferences in detail, generate a 3D model, propose an interior design plan, and facilitate seamless furniture purchases.

[0063] The interior plan proposal system according to this embodiment comprises a preference recognition unit, a model generation unit, a plan proposal unit, and a linkage unit. The preference recognition unit recognizes the user's preferences. For example, the preference recognition unit recognizes the user's preferences by presenting the user with multiple interior photos and having them select "like" or "dislike." For example, the preference recognition unit can more accurately recognize the preferences of a user who likes modern designs by showing them many modern interior photos. The preference recognition unit can also accumulate user preference data and utilize it for future proposals. For example, the preference recognition unit can analyze the user's preference trends based on data of interior photos the user has selected in the past. The model generation unit generates a 3D model of the room based on the preferences recognized by the preference recognition unit. For example, the model generation unit analyzes images of the room taken by the user with a smartphone camera and reproduces the dimensions and shape of the room. For example, the model generation unit can accurately reproduce the height and width of the walls, the arrangement of furniture, etc., of the room based on the captured images. The model generation unit can also integrate images taken from multiple angles to generate a more detailed 3D model. The plan proposal unit proposes interior plans based on the 3D models generated by the model generation unit. The plan proposal unit proposes multiple interior plans based on the user's preferences and budget, for example. For example, it can propose a modern interior plan to a user who prefers modern designs, and suggest cost-effective furniture if the budget is limited. The plan proposal unit can also present multiple plans according to the user's budget. The integration unit integrates with the purchase of furniture based on the interior plans proposed by the plan proposal unit. For example, the integration unit lists furniture available for purchase on e-commerce sites, allowing the user to select from the list and proceed with the purchase. For example, the integration unit can automatically list furniture available for purchase on e-commerce sites based on the proposed interior plan, allowing the user to select from the list and proceed with the purchase.As a result, the interior plan proposal system according to this embodiment can understand the user's preferences in detail, generate a 3D model, propose an interior plan, and seamlessly facilitate the purchase of furniture.

[0064] The preference recognition unit understands the user's preferences. For example, it can understand the user's preferences by presenting the user with multiple interior design photos and having them select "like" or "dislike." Specifically, the preference recognition unit collects data on the interior design photos selected by the user and analyzes the user's preference trends based on this data. For example, if a user selects many photos of modern designs, the preference recognition unit will determine that the user prefers modern designs. The preference recognition unit can also accumulate user preference data and use it for future suggestions. For example, by tracking changes in the user's preferences based on data of interior design photos selected by the user in the past, it can make more accurate suggestions. Furthermore, the preference recognition unit can compare user preference data with data from other users to find common trends. As a result, the preference recognition unit can understand the user's preferences in detail and provide a foundation for proposing interior design plans that meet individual needs.

[0065] The model generation unit generates a 3D model of the room based on the preferences identified by the preference recognition unit. Specifically, the model generation unit analyzes images of the room taken by the user with their smartphone camera and reproduces the room's dimensions and shape. For example, based on the captured images, the model generation unit can accurately reproduce the height and width of the room's walls, the placement of furniture, and other details. Furthermore, the model generation unit can integrate images taken from multiple angles to generate a more detailed 3D model. This allows users to consider interior design plans while viewing an accurate 3D model of their own room. The model generation unit can also suggest optimal furniture placement and design based on the room's dimensions and shape provided by the user. For example, it can suggest optimizing furniture placement according to the room's shape and dimensions to make effective use of space. In this way, the model generation unit can provide a foundation for offering optimal interior design plans tailored to the user's preferences and the characteristics of the room.

[0066] The plan proposal unit proposes interior plans based on the 3D models generated by the model generation unit. Specifically, the plan proposal unit proposes multiple interior plans based on the user's preferences and budget. For example, it can propose a modern interior plan to a user who prefers modern designs, and suggest cost-effective furniture if the budget is limited. Furthermore, the plan proposal unit can present multiple plans according to the user's budget. For example, it can present plans in different price ranges, such as a plan using high-end furniture and a plan using affordable furniture, allowing the user to choose. The plan proposal unit can also make suggestions to optimize furniture placement and design according to the user's preferences and budget. In this way, the plan proposal unit can provide the optimal interior plan that meets the user's needs and support them in realizing an interior that satisfies them.

[0067] The Integration Department coordinates the purchase of furniture based on the interior design plans proposed by the Plan Proposal Department. Specifically, the Integration Department lists furniture available for purchase on e-commerce sites, allowing users to select items from the list and proceed with the purchase. For example, the Integration Department can automatically list furniture available for purchase on e-commerce sites based on the proposed interior design plan, allowing users to select items from the list and proceed with the purchase. Furthermore, the Integration Department can check the inventory status and delivery information of the furniture selected by the user in real time. This allows users to smoothly purchase the necessary furniture based on the proposed interior design plan. The Integration Department can also track the progress of the purchase process and notify the user. For example, it notifies the user when the order is confirmed and updates the estimated delivery date and delivery status in real time. This allows the Integration Department to help users smoothly purchase furniture to realize their interior design plan and keep track of the delivery status.

[0068] The preference recognition unit can understand the user's preferences by presenting them with multiple interior design photos and having them select "like" or "dislike." For example, by showing the user many photos of modern interior designs, the preference recognition unit can gain a detailed understanding of the user's preferences. The preference recognition unit can also accumulate user preference data and use it for future suggestions. For example, the preference recognition unit can analyze the user's preference trends based on data of interior design photos the user has selected in the past. This allows for a detailed understanding of the user's preferences. Some or all of the above processing in the preference recognition unit may be performed using AI, or not. For example, the preference recognition unit can input user preference data into AI and have the AI ​​analyze the user's preference trends.

[0069] The model generation unit can analyze images of a room taken by a user with their smartphone camera and reproduce the dimensions and shape of the room. For example, the model generation unit can accurately reproduce the height and width of the room walls, the arrangement of furniture, etc., based on images of the room taken by a user with their smartphone camera. The model generation unit can also integrate images taken from multiple angles to generate a more detailed 3D model. For example, the model generation unit can accurately reproduce the dimensions and shape of a room based on the captured images. This allows for accurate reproduction of the room's dimensions and shape. Some or all of the above-described processes in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input the captured images into a generation AI and have the generation AI perform the reproduction of the room's dimensions and shape.

[0070] The plan proposal unit can propose multiple interior design plans based on the user's preferences and budget. For example, the plan proposal unit can propose a modern design interior plan based on the user's preferences and budget. Furthermore, the plan proposal unit can present multiple plans according to the user's budget. For example, the plan proposal unit can propose cost-effective furniture based on the user's preferences and budget. This allows for the proposal of interior design plans that meet the user's preferences and budget. Some or all of the above-described processes in the plan proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the plan proposal unit can input user preference and budget data into a generative AI and have the generative AI generate interior design plan proposals.

[0071] The integration unit can list furniture available for purchase on e-commerce sites based on the proposed interior plan. For example, the integration unit can automatically list furniture available for purchase on e-commerce sites based on the proposed interior plan. This allows users to seamlessly purchase furniture based on the proposed interior plan. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input data on the proposed interior plan into the AI ​​and have the AI ​​perform the task of listing furniture available for purchase.

[0072] The integration unit enables users to select from a list and proceed with the purchase. For example, the integration unit allows users to select furniture listed based on a proposed interior plan and proceed with the purchase. This makes it easy for users to purchase the proposed furniture. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input the data of the listed furniture into the AI ​​and have the AI ​​execute the purchase procedure.

[0073] The preference recognition unit can estimate the user's emotions and adjust the order in which interior photos are presented based on the estimated emotions. For example, if the user is relaxed, the preference recognition unit can present casual interior photos first to encourage selection. If the user is stressed, the preference recognition unit can prioritize presenting simple and calming interior photos. Furthermore, if the user is excited, the preference recognition unit can present vibrant and visually stimulating interior photos. By adjusting the order in which interior photos are presented according to the user's emotions, more appropriate photos can be presented. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the preference recognition unit may be performed using AI, or not. For example, the preference recognition unit can input user emotion data into an AI and have the AI ​​adjust the order in which interior photos are presented.

[0074] The preference recognition unit can analyze the user's past preference data and select the most suitable interior photos. For example, the preference recognition unit can analyze the styles of interior photos that the user has previously selected as "liked" and prioritize presenting photos of similar styles. It can also select photos to present that avoid the characteristics of interior photos that the user has previously selected as "disliked." Furthermore, the preference recognition unit can select interior photos related to specific seasons or events based on the user's past preference data. This allows the system to present the most suitable interior photos based on the user's past preference data. Some or all of the above processing in the preference recognition unit may be performed using AI, or not. For example, the preference recognition unit can input the user's past preference data into an AI and have the AI ​​select the most suitable interior photos.

[0075] The preference recognition unit can filter interior photos based on the user's current living situation and areas of interest. For example, if the user owns a pet, the preference recognition unit will prioritize displaying pet-friendly interior photos. If the user works remotely, the preference recognition unit can display interior photos suitable for a home office. Furthermore, if the user has started a new hobby, the preference recognition unit can display interior photos related to that hobby. This allows the display of interior photos tailored to the user's living situation and areas of interest. Some or all of the above processing in the preference recognition unit may be performed using AI, or not. For example, the preference recognition unit can input data on the user's living situation and areas of interest into an AI, and have the AI ​​perform the filtering of interior photos.

[0076] The preference recognition unit can estimate the user's emotions and adjust the display time of interior photos based on the estimated emotions. For example, if the user is relaxed, the preference recognition unit can set a longer display time for the interior photos to allow for careful selection. If the user is in a hurry, the preference recognition unit can set a shorter display time for the interior photos to allow for quick selection. Furthermore, if the user is excited, the preference recognition unit can appropriately adjust the display time of the interior photos to maintain interest. This allows for more appropriate selection by adjusting the display time of interior photos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the preference recognition unit may be performed using AI or not. For example, the preference recognition unit can input user emotion data into an AI and have the AI ​​adjust the display time of the interior photos.

[0077] The preference recognition unit can prioritize displaying highly relevant interior photos by considering the user's geographical location when presenting interior photos. For example, if the user lives in a cold climate, the preference recognition unit can prioritize displaying warm-themed interior photos. If the user lives in an urban area, the preference recognition unit can display compact and functional interior photos. Furthermore, if the user lives by the sea, the preference recognition unit can display ocean-themed interior photos. This allows the display of highly relevant interior photos based on the user's geographical location. Some or all of the above processing in the preference recognition unit may be performed using AI, for example, or without AI. For example, the preference recognition unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the task of displaying highly relevant interior photos.

[0078] The preference recognition unit can analyze the user's social media activity when presenting interior photos and display relevant photos. For example, the preference recognition unit can analyze the style of interior photos that the user has "liked" on social media and display photos of a similar style. It can also prioritize displaying works by interior designers that the user follows. Furthermore, the preference recognition unit can analyze the content of the user's social media posts and display interior photos related to themes of interest. This allows the display of relevant interior photos based on the user's social media activity. Some or all of the above processing in the preference recognition unit may be performed using AI, for example, or without AI. For example, the preference recognition unit can input data on the user's social media activity into an AI and have the AI ​​display relevant interior photos.

[0079] The model generation unit can estimate the user's emotions and adjust the 3D model generation method based on the estimated user emotions. For example, if the user is relaxed, the model generation unit can generate a detailed 3D model that reproduces even the smallest details. If the user is in a hurry, the model generation unit can generate a simplified 3D model that can quickly propose a plan. Furthermore, if the user is excited, the model generation unit can also generate a 3D model with visually appealing effects. In this way, by adjusting the 3D model generation method according to the user's emotions, a more appropriate 3D model can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the model generation unit may be performed using a generative AI, or not. For example, the model generation unit can input user emotion data into a generative AI and have the generative AI adjust the 3D model generation method.

[0080] The model generation unit can improve accuracy when generating 3D models by considering room lighting conditions and furniture placement. For example, the model generation unit can analyze room lighting conditions and generate a 3D model considering the effects of natural and artificial light. It can also analyze furniture placement and generate a 3D model based on the actual placement. Furthermore, the model generation unit can generate a more realistic 3D model by considering the color of the room walls and the type of flooring. As a result, by considering room lighting conditions and furniture placement, a more accurate 3D model can be generated. Some or all of the above processing in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input data on room lighting conditions and furniture placement into a generation AI and have the generation AI perform 3D model generation.

[0081] The model generation unit can select the optimal generation method by referring to the user's past room image data when generating a 3D model. For example, the model generation unit can analyze image data of a room previously taken by the user and generate a 3D model in a similar style. Furthermore, the model generation unit can analyze furniture arrangement and design trends from the user's past room image data and select the optimal generation method. In addition, the model generation unit can accurately reproduce the dimensions and shape of a room based on the user's past room image data. This allows for the generation of an optimal 3D model based on the user's past room image data. Some or all of the above-described processes in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input the user's past room image data into a generation AI and have the generation AI select the optimal generation method.

[0082] The model generation unit can estimate the user's emotions and adjust the display method of the 3D model based on the estimated user emotions. For example, if the user is relaxed, the model generation unit can display the 3D model at a relaxed pace. If the user is in a hurry, the model generation unit can display the 3D model quickly and provide the necessary information immediately. Furthermore, if the user is excited, the model generation unit can display the 3D model with visually stimulating effects. This allows for a more appropriate display by adjusting the display method of the 3D model according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the model generation unit may be performed using a generative AI, or not. For example, the model generation unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the 3D model.

[0083] The model generation unit can select the optimal generation method when generating a 3D model, taking into account the user's geographical location information. For example, if the user lives in a cold region, the model generation unit can generate a 3D model using warm colors and materials. If the user lives in an urban area, the model generation unit can generate a compact and functional 3D model. Furthermore, if the user lives by the sea, the model generation unit can generate a 3D model with a sea-themed design. This allows for the generation of the optimal 3D model based on the user's geographical location information. Some or all of the above-described processes in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal generation method.

[0084] The model generation unit can analyze the user's social media activity and reflect relevant data when generating 3D models. For example, the model generation unit can generate a 3D model that reflects the interior design that the user has "liked" on social media. It can also generate a 3D model that reflects the style of the interior designers that the user follows. Furthermore, the model generation unit can analyze the content of the user's social media posts and generate a 3D model that reflects themes of interest. This allows for the generation of 3D models that reflect relevant data based on the user's social media activity. Some or all of the above processing in the model generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the model generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the reflection of relevant data.

[0085] The plan proposal unit can estimate the user's emotions and adjust the interior plan proposal method based on the estimated emotions. For example, if the user is relaxed, the plan proposal unit can propose a detailed interior plan and increase the options. If the user is in a hurry, the plan proposal unit can propose a concise and to-the-point interior plan. Furthermore, if the user is excited, the plan proposal unit can propose an interior plan with visually appealing effects. In this way, by adjusting the interior plan proposal method according to the user's emotions, a more appropriate plan can be proposed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the plan proposal unit may be performed using, for example, generative AI, or not using generative AI. For example, the plan proposal unit can input user emotion data into a generative AI and have the generative AI adjust the interior plan proposal method.

[0086] The plan proposal unit can select the optimal plan when proposing an interior design plan by referring to the user's past preference data. For example, the plan proposal unit can analyze the style of interior design plans that the user has previously selected as "liked" and propose plans of a similar style. The plan proposal unit can also select plans that avoid the characteristics of interior design plans that the user has previously selected as "disliked". Furthermore, the plan proposal unit can also propose interior design plans related to specific seasons or events based on the user's past preference data. This allows the system to propose the optimal interior design plan based on the user's past preference data. Some or all of the above processing in the plan proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the plan proposal unit can input the user's past preference data into a generative AI and have the generative AI select the optimal plan.

[0087] The plan proposal unit can customize interior design plans based on the user's current living situation and budget. For example, if the user has a pet, the plan proposal unit can propose a pet-friendly interior design plan. If the user works remotely, the plan proposal unit can propose an interior design plan suitable for a home office. Furthermore, the plan proposal unit can propose a cost-effective interior design plan according to the user's budget. This allows for the proposal of interior design plans tailored to the user's living situation and budget. Some or all of the above-described processes in the plan proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the plan proposal unit can input data on the user's living situation and budget into a generative AI and have the generative AI perform the plan customization.

[0088] The plan suggestion unit can estimate the user's emotions and adjust the display order of interior plans based on the estimated emotions. For example, if the user is relaxed, the plan suggestion unit may present casual interior plans first to encourage selection. If the user is stressed, the plan suggestion unit may prioritize presenting simple and calming interior plans. Furthermore, if the user is excited, the plan suggestion unit may present vibrant and visually stimulating interior plans. By adjusting the display order of interior plans according to the user's emotions, a more appropriate plan can be presented. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the plan suggestion unit may be performed using, for example, generative AI, or not using generative AI. For example, the plan suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the display order of interior plans.

[0089] The plan proposal unit can analyze a user's social media activity and propose relevant plans when suggesting interior design plans. For example, the plan proposal unit can analyze the styles of interior design plans that the user has "liked" on social media and propose plans of a similar style. It can also prioritize suggesting works by interior designers that the user follows. Furthermore, the plan proposal unit can analyze the content of the user's social media posts and propose interior design plans related to themes of interest. This allows the system to propose relevant interior design plans based on the user's social media activity. Some or all of the above processing in the plan proposal unit may be performed using, for example, a generative AI, or without one. For example, the plan proposal unit can input data on the user's social media activity into a generative AI and have the AI ​​generate relevant plan suggestions.

[0090] The integration unit can estimate the user's emotions and adjust the furniture listing method based on the estimated emotions. For example, if the user is relaxed, the integration unit can provide a detailed furniture list with more options. If the user is in a hurry, the integration unit can provide a concise and to-the-point furniture list. Furthermore, if the user is excited, the integration unit can provide a furniture list with visually appealing effects. This allows for a more appropriate furniture list to be provided by adjusting the furniture listing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using or without a generative AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI adjust the furniture listing method.

[0091] The integration unit can select the most suitable furniture by referring to the user's past purchase history when listing furniture. For example, the integration unit can analyze the style of furniture the user has purchased in the past and list furniture of a similar style. The integration unit can also prioritize listing furniture from brands the user has previously purchased. Furthermore, the integration unit can list furniture related to specific seasons or events based on the user's past purchase history. This allows the integration unit to list the most suitable furniture based on the user's past purchase history. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the integration unit can input data from the user's past purchase history into a generative AI and have the generative AI select the most suitable furniture.

[0092] The integration unit can customize the furniture list based on the user's current living situation and budget. For example, if the user has a pet, the integration unit can list pet-friendly furniture. If the user works remotely, the integration unit can list furniture suitable for a home office. Furthermore, the integration unit can list cost-effective furniture according to the user's budget. This allows the system to list furniture that suits the user's living situation and budget. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not. For example, the integration unit can input data on the user's living situation and budget into the generative AI and have the generative AI perform the list customization.

[0093] The interaction unit can estimate the user's emotions and adjust the display order of furniture based on the estimated emotions. For example, if the user is relaxed, the interaction unit may present casual furniture first to encourage selection. If the user is stressed, the interaction unit may prioritize presenting simple and calming furniture. Furthermore, if the user is excited, the interaction unit may present bright and visually stimulating furniture. By adjusting the display order of furniture according to the user's emotions, more appropriate furniture can be presented. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the interaction unit can input user emotion data into a generative AI and have the generative AI adjust the display order of furniture.

[0094] The collaborative unit can select the most suitable furniture when listing furniture, taking into account the user's geographical location. For example, if the user lives in a cold region, the collaborative unit will prioritize listing furniture made of warm materials and designs. If the user lives in an urban area, the collaborative unit can list compact and functional furniture. Furthermore, if the user lives by the sea, the collaborative unit can list furniture with a sea-themed design. This allows the system to list the most suitable furniture based on the user's geographical location. Some or all of the above processing in the collaborative unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collaborative unit can input the user's geographical location information into a generative AI and have the generative AI select the most suitable furniture.

[0095] The integration unit can analyze a user's social media activity when listing furniture and list relevant furniture. For example, the integration unit can analyze the styles of furniture that a user has "liked" on social media and list furniture of similar styles. The integration unit can also prioritize listing works by interior designers that the user follows. Furthermore, the integration unit can analyze the content of a user's social media posts and list furniture related to themes of interest. This allows the integration unit to list relevant furniture based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the integration unit can input data on the user's social media activity into a generative AI and have the generative AI perform the task of listing relevant furniture.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The interior design proposal system can also acquire user health data and propose interior designs based on their health status. For example, if a user has allergies, it can suggest furniture made from allergen-free materials. If a user needs relaxation, it can suggest interiors with relaxing colors and designs. Furthermore, if a user is not getting enough exercise, it can suggest interior layouts that encourage exercise. This allows the system to provide interior designs tailored to the user's health condition.

[0098] The interior design proposal system can also acquire the user's family structure data and propose interior designs that ensure comfort for the entire family. For example, it can suggest furniture that prioritizes safety for families with young children. It can also suggest barrier-free interior designs for families with elderly members. Furthermore, it can suggest pet-friendly furniture and layouts for families with pets. This allows the system to provide interior designs that ensure comfort for the entire family.

[0099] The interior design proposal system can also propose interior designs that take into account the user's hobbies and interests. For example, it can suggest a comfortable reading space for a user who enjoys reading. It can also suggest an interior design that takes acoustics into consideration for a user who enjoys music. Furthermore, it can suggest a user-friendly kitchen layout for a user who enjoys cooking. This allows the system to provide interior designs that are tailored to the user's hobbies and interests.

[0100] The interior design planning system can also acquire user lifestyle data and propose interior designs tailored to that lifestyle. For example, it can suggest an efficient home office layout for users who work remotely. It can also suggest storage space for outdoor equipment for users who enjoy the outdoors. Furthermore, it can suggest a comfortable guest space for users who frequently invite guests. This allows the system to provide interior designs that match the user's lifestyle.

[0101] The interior design proposal system can also acquire user energy consumption data and propose interior designs that take energy efficiency into consideration. For example, it can suggest energy-efficient lighting and appliances to users with high energy consumption. It can also suggest layouts that maximize the use of natural light. Furthermore, it can suggest curtains and windows with high insulation properties. In this way, it can provide interior designs that take energy efficiency into consideration.

[0102] The interior design plan suggestion system can estimate the user's emotions and adjust the colors of the interior plan based on those emotions. For example, if the user is relaxed, it can suggest calming colors. If the user is stressed, it can suggest relaxing colors. Furthermore, if the user is excited, it can suggest bright and vibrant colors. This allows the system to provide an interior plan with colors that match the user's emotions.

[0103] The interior design proposal system can estimate the user's emotions and adjust the layout of the interior design based on those emotions. For example, if the user is relaxed, it can suggest a spacious layout. If the user is stressed, it can suggest a simple and organized layout. Furthermore, if the user is excited, it can suggest a visually stimulating layout. This allows the system to provide interior design plans tailored to the user's emotions.

[0104] The interior design proposal system can estimate the user's emotions and adjust the materials used in the interior design based on those emotions. For example, if the user is relaxed, it can suggest furniture made of soft materials. If the user is stressed, it can suggest materials with a relaxing effect. Furthermore, if the user is excited, it can suggest visually stimulating materials. This allows the system to provide an interior design plan with materials that match the user's emotions.

[0105] The interior design plan suggestion system can estimate the user's emotions and adjust the lighting in the interior plan based on those emotions. For example, if the user is relaxed, it can suggest soft lighting. If the user is stressed, it can suggest relaxing lighting. Furthermore, if the user is excited, it can suggest visually stimulating lighting. This allows the system to provide interior design plans with lighting tailored to the user's emotions.

[0106] The interior design proposal system can estimate the user's emotions and adjust the acoustic environment of the interior design based on those emotions. For example, if the user is relaxed, it can suggest a quiet environment. If the user is stressed, it can suggest relaxing music. Furthermore, if the user is excited, it can suggest lively music. This allows the system to provide an interior design plan with an acoustic environment tailored to the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The preference assessment unit understands the user's preferences. For example, it can assess the user's preferences by showing them multiple interior design photos and having them select "like" or "dislike." Furthermore, by showing users who like modern designs more modern interior design photos, the system can more accurately understand their preferences. In addition, user preference data can be accumulated and used for future suggestions. Step 2: The model generation unit generates a 3D model of the room based on the preferences identified by the preference recognition unit. For example, it analyzes images of the room taken by the user with their smartphone camera and reproduces the room's dimensions and shape. Furthermore, it can accurately reproduce the height and width of the room's walls, the placement of furniture, and other details based on the captured images. It can also integrate images taken from multiple angles to generate a more detailed 3D model. Step 3: The plan proposal unit proposes interior plans based on the 3D model generated by the model generation unit. For example, it can propose multiple interior plans based on the user's preferences and budget. For users who prefer modern designs, it can propose a modern interior plan, and for users with limited budgets, it can propose cost-effective furniture. It can also present multiple plans according to the user's budget. Step 4: The Integration Department integrates with the Plan Proposal Department to purchase furniture based on the proposed interior design plan. For example, it lists furniture available for purchase on e-commerce sites, allowing users to select from the list and proceed with the purchase. Based on the proposed interior design plan, it automatically lists furniture available for purchase on e-commerce sites, allowing users to select from the list and proceed with the purchase.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the preference recognition unit, model generation unit, plan proposal unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the preference recognition unit uses the camera 42 and touch panel 38A of the smart device 14 to understand the user's preferences. The model generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates a 3D model by analyzing images captured by the smart device 14. The plan proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes an interior plan based on the user's preferences and budget. The collaboration unit is implemented in the control unit 46A of the smart device 14, for example, and collaborates with e-commerce sites. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the preference recognition unit, model generation unit, plan proposal unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the preference recognition unit uses the camera 42 and microphone 238 of the smart glasses 214 to understand the user's preferences. The model generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates a 3D model by analyzing images captured by the smart glasses 214. The plan proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes an interior plan based on the user's preferences and budget. The collaboration unit is implemented in the control unit 46A of the smart glasses 214, for example, and collaborates with e-commerce sites. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the preference recognition unit, model generation unit, plan proposal unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the preference recognition unit uses the camera 42 and microphone 238 of the headset terminal 314 to understand the user's preferences. The model generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates a 3D model by analyzing images captured by the headset terminal 314. The plan proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes an interior plan based on the user's preferences and budget. The collaboration unit is implemented in the control unit 46A of the headset terminal 314, for example, and collaborates with e-commerce sites. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the preference recognition unit, model generation unit, plan proposal unit, and collaboration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the preference recognition unit uses the camera 42 and microphone 238 of the robot 414 to understand the user's preferences. The model generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates a 3D model by analyzing images taken by the robot 414. The plan proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes an interior plan based on the user's preferences and budget. The collaboration unit is implemented in the control unit 46A of the robot 414, for example, and collaborates with e-commerce sites. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A preference recognition unit that understands the user's preferences, A model generation unit generates a 3D model of a room based on the preferences identified by the preference recognition unit, A plan proposal unit proposes an interior plan based on the 3D model generated by the aforementioned model generation unit, The system includes a coordination unit for purchasing furniture based on the interior plan proposed by the aforementioned plan proposal unit. A system characterized by the following features. (Note 2) The aforementioned preference recognition unit, By presenting users with multiple interior design photos and asking them to select "like" or "dislike," we can understand their preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned model generation unit, The system analyzes images of a room taken by the user with their smartphone camera and recreates the room's dimensions and shape. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned plan proposal department, We propose multiple interior design plans based on the user's preferences and budget. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, Based on the proposed interior design plan, list the furniture items available for purchase on e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, Allows users to select from a list and proceed with the purchase. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned preference recognition unit, The system estimates the user's emotions and adjusts the order in which interior photos are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned preference recognition unit, Analyze the user's past preference data to select the most suitable interior photos. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned preference recognition unit, When presenting interior photos, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned preference recognition unit, The system estimates the user's emotions and adjusts the display time of interior photos based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned preference recognition unit, When displaying interior photos, the system prioritizes showing photos that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned preference recognition unit, When presenting interior photos, the system analyzes the user's social media activity and displays relevant photos. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned model generation unit, It estimates the user's emotions and adjusts the 3D model generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned model generation unit, When generating 3D models, accuracy is improved by considering room lighting conditions and furniture placement. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned model generation unit, When generating a 3D model, the system references the user's past room image data to select the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned model generation unit, It estimates the user's emotions and adjusts how the 3D model is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned model generation unit, When generating 3D models, the optimal generation method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned model generation unit, When generating 3D models, the system analyzes the user's social media activity and incorporates relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned plan proposal department, The system estimates the user's emotions and adjusts the interior design plan proposal method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned plan proposal department, When proposing interior design plans, the optimal plan is selected by referring to the user's past preference data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned plan proposal department, When proposing interior design plans, customize the plan based on the user's current living situation and budget. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned plan proposal department, It estimates the user's emotions and adjusts the display order of interior plans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned plan proposal department, When proposing interior design plans, we analyze the user's social media activity and propose plans that are relevant to their needs. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, The system estimates the user's emotions and adjusts the furniture listing method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, When listing furniture items, the system selects the most suitable furniture by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, When listing furniture items, customize the list based on the user's current living situation and budget. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the display order of furniture based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, When listing furniture items, the system selects the most suitable furniture by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, When listing furniture items, the system analyzes the user's social media activity and lists relevant furniture items. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A preference recognition unit that understands the user's preferences, A model generation unit generates a 3D model of a room based on the preferences identified by the preference recognition unit, A plan proposal unit proposes an interior plan based on the 3D model generated by the aforementioned model generation unit, The system includes a coordination unit for purchasing furniture based on the interior plan proposed by the aforementioned plan proposal unit. A system characterized by the following features.

2. The aforementioned preference recognition unit, By presenting users with multiple interior design photos and allowing them to select their preference, we can understand their preferences. The system according to feature 1.

3. The aforementioned model generation unit, The system analyzes images of a room taken by the user with their smartphone camera and recreates the room's dimensions and shape. The system according to feature 1.

4. The aforementioned plan proposal department, We propose multiple interior design plans based on the user's preferences and budget. The system according to feature 1.

5. The aforementioned linkage unit is, Based on the proposed interior design plan, list the furniture items available for purchase on e-commerce sites. The system according to feature 1.

6. The aforementioned linkage unit is, Allows users to select from a list and proceed with the purchase. The system according to feature 1.

7. The aforementioned preference recognition unit, The system estimates the user's emotions and adjusts the order in which interior photos are presented based on those emotions. The system according to feature 1.

8. The aforementioned preference recognition unit, Analyze the user's past preference data to select the most suitable interior photos. The system according to feature 1.

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