system

Abstract

The system according to the embodiment aims to efficiently generate and produce furniture designs that suit a room. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a presentation unit, and a production unit. The reception unit takes a photo of a room and inputs it into AI. The generation unit analyzes the photo of the room input by the reception unit and generates a furniture design. The presentation unit presents the design generated by the generation unit to the user. The production unit produces furniture based on the design selected by the user via the presentation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently create and produce furniture designs that fit a room.

[0005] The system according to the embodiment aims to efficiently generate and produce furniture designs that suit a room. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a presentation unit, and a production unit. The reception unit takes a photo of the room and inputs it into the AI. The generation unit analyzes the photo of the room input by the reception unit and generates a furniture design. The presentation unit presents the design generated by the generation unit to the user. The production unit produces furniture based on the design selected by the user via the presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate and produce furniture designs that fit a room. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A furniture production system according to an embodiment of the present invention uses AI to generate images of furniture suited to a room and then produces the furniture based on the generated images. This furniture production system begins when a user takes a photo of the room and inputs it into the AI. The AI ​​then analyzes the photo and generates furniture designs that match the room's size and interior style. The generated furniture designs are presented to the user, and the furniture is produced based on the design selected by the user. This system allows users to easily obtain furniture that best suits their room. For example, a user may take a photo of the room and input it into the AI. It is recommended that photos be taken from multiple angles to provide an overall view of the room. For example, photos taken from all four corners of the room may be input into the AI. The AI ​​then analyzes the input photo of the room. The AI ​​analyzes the size, shape, and interior style of the room and generates furniture designs based on the analysis. For example, if the room has a modern style, it generates furniture with a modern design. The generated furniture designs are then presented to the user. The user can select the design that best suits their preferences from the presented designs. For example, multiple designs may be presented and the user may choose one. Finally, the furniture is produced based on the design selected by the user. The created furniture is delivered to the user's room and installed. This system allows the user to easily obtain the furniture that best suits their room. In this way, the furniture creation system can provide the user with the furniture that best suits their room.

[0029] A furniture production system according to an embodiment includes a reception unit, a generation unit, a presentation unit, and a production unit. The reception unit allows a user to take a photo of a room and input the photo to an AI. When taking a photo of a room, it is recommended that the user take photos from multiple angles so that the entire room can be seen. For example, photos taken from all four corners of the room can be input to the AI. The generation unit analyzes the photo of the room input by the reception unit and generates a furniture design. The generation unit analyzes, for example, the size, shape, and interior style of the room and generates a furniture design based on the analysis. For example, if the room has a modern style, it generates furniture with a modern design. The presentation unit presents the design generated by the generation unit to the user. For example, the presentation unit presents multiple designs to the user and produces furniture based on a design selected by the user. The production unit produces furniture based on the design selected by the user via the presentation unit. For example, the production unit produces furniture based on the design selected by the user, and delivers and installs the produced furniture in the user's room. This allows the furniture production system according to an embodiment to provide furniture optimal for the user's room.

[0030] The generation unit may include an analysis unit that analyzes the size of a room or the style of an interior. For example, the analysis unit may measure the size of a room and analyze the style of the interior. For example, the analysis unit may measure the size of a room in square meters or cubic meters and classify the style of the interior into categories such as modern, classic, and minimalist. For example, the analysis unit may measure the dimensions of the room using image recognition technology to measure the size of the room. Furthermore, the analysis unit may analyze the design elements of the room using feature extraction technology to analyze the style of the interior. This allows the analysis unit to generate an optimal furniture design based on the size of the room and the style of the interior.

[0031] The presentation unit may include a customization unit that allows a user to specify a specific color or material. The customization unit, for example, allows the user to customize the furniture design by specifying a specific color or material. The customization unit, for example, may use RGB values ​​or a color picker to allow the user to specify a color. The customization unit may also provide material options, such as wood, metal, and plastic, to allow the user to specify a material. The customization unit, for example, provides an interface for the user to specify a color or material, and can customize the furniture design based on the color or material selected by the user. This allows the customization unit to customize the furniture design by allowing the user to specify a specific color or material.

[0032] The production unit may include a delivery unit that delivers and installs the manufactured furniture in the user's room. The delivery unit can, for example, deliver and install the manufactured furniture in the user's room. The delivery unit can, for example, arrange for a delivery company to deliver the manufactured furniture to an address specified by the user. The delivery unit can also provide procedures and tools for installing the furniture. The delivery unit can, for example, confirm the installation location of the furniture, and assemble and install the furniture according to the installation procedures. In this way, the delivery unit can deliver and install the manufactured furniture in the user's room.

[0033] The reception unit can analyze the user's past photography history and select the optimal photography method. The reception unit can, for example, analyze the user's past photography history and select the optimal photography method. The reception unit can, for example, analyze the quality of photos taken by the user in the past and suggest the optimal photography angle and lighting conditions. The reception unit can also suggest the optimal settings by referring to camera settings used by the user in the past. Furthermore, the reception unit can identify the most successful photography method from the user's past photography history and recommend it. This allows the reception unit to suggest the optimal photography method based on the past photography history. The photography history is analyzed using data such as the past photography date and time, photography location, and photography conditions. This allows the reception unit to analyze the user's past photography history and select the optimal photography method.

[0034] The reception unit can perform filtering based on the user's current interior state when taking a photo of a room. For example, when taking a photo of a room, the reception unit can perform filtering based on the user's current interior state. For example, if the room's interior is messy, the reception unit can provide advice on tidying up and create optimal shooting conditions. Furthermore, if the room's interior is biased toward a particular style, the reception unit can apply a filter that matches that style. Furthermore, the reception unit can automatically select an optimal filter based on the lighting conditions of the room. This allows the reception unit to apply an optimal filter based on the interior state. The filtering method is performed using, for example, image processing technology or a type of filter. This allows the reception unit to perform filtering based on the user's current interior state when taking a photo of a room.

[0035] The reception unit, when taking a photo of a room, can prioritize acquiring highly relevant photos based on the user's geographical location information. For example, when taking a photo of a room, the reception unit can prioritize acquiring highly relevant photos taking the user's geographical location information into consideration. For example, when taking a photo of a room, the reception unit can prioritize acquiring photos that match the urban interior style if the user lives in an urban area. Furthermore, when the user lives in a suburban area, the reception unit can prioritize acquiring photos that make use of natural light. Furthermore, when the user lives in a specific region, the reception unit can prioritize acquiring photos that match the culture and style of that region. This allows the reception unit to acquire optimal photos based on the geographical location information. The geographical location information is acquired using, for example, GPS data or address information. This allows the reception unit, when taking a photo of a room, to prioritize acquiring highly relevant photos based on the user's geographical location information.

[0036] The reception unit can analyze the user's social media activity when taking a photo of a room and acquire related photos. For example, when taking a photo of a room, the reception unit can analyze the user's social media activity and acquire related photos. For example, the reception unit can analyze photos shared by the user on social media and suggest optimal shooting angles and styles. The reception unit can also identify the user's preferred interior style from the content of the user's social media posts and acquire photos that match that style. Furthermore, the reception unit can analyze the reactions of the user's social media followers and suggest the most popular style. This allows the reception unit to acquire optimal photos based on the social media activity. The analysis of social media activity is performed using data such as the content of the post, the number of likes, and comments. This allows the reception unit to analyze the user's social media activity when taking a photo of a room and acquire related photos.

[0037] The generation unit can adjust the level of detail of the design based on the importance of the room when generating the design. For example, the generation unit can adjust the level of detail of the design based on the importance of the room when generating the design. For example, the generation unit can generate a detailed design for an important room such as a living room. The generation unit can also generate a simple design for a room that is not used often, such as a closet. Furthermore, the generation unit can generate a practical design for a room where functionality is important, such as a kitchen. This allows the generation unit to generate an optimal design depending on the importance of the room. The importance is evaluated using criteria such as the frequency of use of the room and the area of ​​the room. This allows the generation unit to adjust the level of detail of the design based on the importance of the room when generating the design.

[0038] The generation unit can apply different design algorithms depending on the room category when generating a design. For example, the generation unit can apply different design algorithms depending on the room category when generating a design. For example, the generation unit can apply a design algorithm that emphasizes comfort to a living room. The generation unit can also apply a design algorithm that promotes relaxation to a bedroom. Furthermore, the generation unit can apply a design algorithm that promotes concentration to an office. This allows the generation unit to generate an optimal design depending on the room category. Categorization is performed using criteria such as living room, bedroom, and kitchen. This allows the generation unit to apply different design algorithms depending on the room category when generating a design.

[0039] The generation unit can determine the priority of designs based on the time when the room was photographed when generating the designs. For example, the generation unit can determine the priority of designs based on the time when the room was photographed when generating the designs. For example, the generation unit can generate designs by prioritizing the most recently photographed photo of the room. The generation unit can also generate designs by prioritizing designs according to the season. Furthermore, the generation unit can generate designs by prioritizing designs suited to specific events. This allows the generation unit to generate optimal designs based on the time when the photo was photographed. The time when the photo was photographed is acquired using data such as the date and time of the photo, the season, and the time of day. This allows the generation unit to determine the priority of designs based on the time when the room was photographed when generating the designs.

[0040] The generation unit can adjust the order of designs based on the relevance of rooms when generating designs. For example, the generation unit can adjust the order of designs based on the relevance of rooms when generating designs. For example, the generation unit can first generate a design for a living room, followed by designs for other rooms. The generation unit can also generate designs by prioritizing rooms that are frequently used by the user. Furthermore, the generation unit can adjust the order of designs based on the functionality of the rooms. This allows the generation unit to generate an optimal design based on the relevance of rooms. The evaluation of relevance is performed using criteria such as the purpose of the room or the theme of the design. This allows the generation unit to adjust the order of designs based on the relevance of rooms when generating designs.

[0041] The presentation unit may adjust the level of detail of presentation based on the importance of the design when presenting the design. For example, the presentation unit may adjust the level of detail of presentation based on the importance of the design when presenting the design. For example, the presentation unit may provide a detailed description and a high-resolution image for an important design. The presentation unit may also provide a brief description and a low-resolution image for a less important design. Furthermore, the presentation unit may provide additional information or options for a design in which the user is particularly interested. This allows the presentation unit to provide an optimal presentation method depending on the importance of the design. The importance is evaluated using criteria such as the frequency of use of the room or the area of ​​the room. This allows the presentation unit to adjust the level of detail of presentation based on the importance of the design when presenting the design.

[0042] The presentation unit can apply different presentation algorithms depending on the design category when presenting the designs. For example, the presentation unit can apply different presentation algorithms depending on the design category when presenting the designs. For example, the presentation unit can apply a presentation algorithm that emphasizes comfort to living room designs. The presentation unit can apply a presentation algorithm that promotes relaxation to bedroom designs. The presentation unit can also apply a presentation algorithm that promotes concentration to office designs. This allows the presentation unit to provide an optimal presentation method depending on the design category. Categorization is performed using criteria such as living room, bedroom, and kitchen. This allows the presentation unit to apply different presentation algorithms depending on the design category when presenting the designs.

[0043] The presentation unit can determine the priority of presentation based on the time of creation of the design at the time of presentation. For example, the presentation unit can determine the priority of presentation based on the time of creation of the design at the time of presentation. For example, the presentation unit can prioritize and present the most recently created design. The presentation unit can also prioritize and present designs according to the season. Furthermore, the presentation unit can prioritize and present designs suited to a specific event. This allows the presentation unit to present the optimal design based on the time of creation. The time of creation is acquired using data such as the date and time of creation, the season, and the time zone. This allows the presentation unit to determine the priority of presentation based on the time of creation of the design at the time of presentation.

[0044] The presentation unit can adjust the presentation order based on the relevance of the designs when presenting them. For example, the presentation unit can adjust the presentation order based on the relevance of the designs when presenting them. For example, the presentation unit can first present designs for a living room, followed by designs for other rooms. The presentation unit can also prioritize and present designs for rooms that the user frequently uses. Furthermore, the presentation unit can adjust the presentation order based on the functionality of the rooms. This allows the presentation unit to present designs in an optimal order based on the relevance of the designs. The relevance is evaluated using criteria such as the purpose of the room or the theme of the design. This allows the presentation unit to adjust the presentation order based on the relevance of the designs when presenting them.

[0045] The production department can select the optimal production method by analyzing the user's past consumption behavior during production. For example, the production department can select the optimal production method by analyzing the user's past consumption behavior during production. For example, the production department can analyze the style of furniture the user has previously purchased and select a production method that suits it. The production department can also identify the user's preferred materials and colors from the user's past consumption behavior and select a production method based on that. Furthermore, the production department can set an optimal production schedule by referring to the user's past consumption behavior. This allows the production department to provide the optimal production method based on the user's past consumption behavior. The analysis of consumption behavior is performed using data such as purchase history, consumption patterns, and expenditure amounts. This allows the production department to select the optimal production method by analyzing the user's past consumption behavior during production.

[0046] The production unit can customize the production means based on the user's current living situation during production. For example, if the user has small children, the production unit can select a production method that prioritizes safety. If the user has pets, the production unit can select a production method that uses durable materials. Furthermore, if the user moves frequently, the production unit can select a production method that reflects an easy-to-assemble design. This allows the production unit to provide the optimal production means based on the user's current living situation. The evaluation of the living situation is performed using criteria such as family composition, lifestyle, and income status. This allows the production unit to customize the production means based on the user's current living situation during production.

[0047] The production department can select the optimal production method by taking into account the user's geographical location information during production. For example, the production department can select the optimal production method by taking into account the user's geographical location information during production. For example, if the user lives in an urban area, the production department can select a production method that suits the urban interior style. Furthermore, if the user lives in a suburban area, the production department can select a production method that makes use of natural materials. Furthermore, if the user lives in a specific region, the production department can select a production method that suits the culture and style of that region. This allows the production department to provide the optimal production method based on the geographical location information. The geographical location information can be acquired using, for example, GPS data or address information. This allows the production department to select the optimal production method by taking into account the user's geographical location information during production.

[0048] The production department can analyze a user's social media activity and suggest production methods during production. For example, the production department can analyze a user's social media activity and suggest production methods during production. For example, the production department can analyze the interior style shared by the user on social media and suggest a production method that matches it. The production department can also identify a user's preferred design from the user's social media posts and suggest a production method based on that. Furthermore, the production department can analyze the reactions of the user's social media followers and suggest a production method that reflects the most popular style. This allows the production department to provide the optimal production method based on the social media activity. The analysis of social media activity is performed using data such as the post content, the number of likes, and comments. This allows the production department to analyze a user's social media activity and suggest a production method during production.

[0049] The analysis unit can optimize the analysis algorithm by referring to past data of the room during analysis. For example, the analysis unit can optimize the analysis algorithm by referring to past data of the room during analysis. For example, the analysis unit can refer to past photo data of the room and propose an optimal design. The analysis unit can also analyze the past interior style of the room and optimize the analysis algorithm based on the analysis. Furthermore, the analysis unit can refer to the past usage of the room and propose an optimal design. This allows the analysis unit to provide an optimal analysis algorithm based on the past data. Referencing the past data is performed using data such as past photography dates and times, photography locations, and photography conditions. This allows the analysis unit to optimize the analysis algorithm by referring to the past data of the room during analysis.

[0050] The analysis unit can apply different analysis methods to each room category during analysis. For example, the analysis unit can apply different analysis methods to each room category during analysis. For example, the analysis unit can apply an analysis method that emphasizes comfort to a living room. Furthermore, the analysis unit can apply an analysis method that promotes relaxation to a bedroom. Furthermore, the analysis unit can apply an analysis method that promotes concentration to an office. This allows the analysis unit to provide the optimal analysis method depending on the room category. Categorization is performed using criteria such as living room, bedroom, and kitchen. This allows the analysis unit to apply different analysis methods to each room category during analysis.

[0051] The analysis unit can analyze changes in the analysis based on the time when the room was photographed during analysis. The analysis unit can, for example, analyze changes in the analysis based on the time when the room was photographed during analysis. The analysis unit, for example, prioritizes analysis of photos of the room that were photographed most recently. The analysis unit can also perform analysis according to the season and propose an optimal design. Furthermore, the analysis unit can perform analysis tailored to a specific event and propose an optimal design. This allows the analysis unit to provide an optimal analysis based on the time when the photo was taken. The time when the photo was taken is obtained using data such as the date and time of the photo, the season, and the time of day. This allows the analysis unit to analyze changes in the analysis based on the time when the room was photographed during analysis.

[0052] The analysis unit can perform the analysis by referring to market data related to the room during the analysis. The analysis unit can, for example, perform the analysis by referring to market data related to the room during the analysis. The analysis unit can, for example, refer to current interior market trends and propose an optimal design. The analysis unit can also refer to market data according to the room category and perform the analysis based on the market data. Furthermore, the analysis unit can refer to market data according to the region of the room and propose an optimal design. This allows the analysis unit to provide an optimal analysis based on the market data. The market data is referenced using data such as sales data and consumer survey data. This allows the analysis unit to perform the analysis by referring to market data related to the room during the analysis.

[0053] The customization unit can select the optimal customization method by referring to the user's past selection history during customization. For example, the customization unit can select the optimal customization method by referring to the user's past selection history during customization. For example, the customization unit can refer to colors and materials selected by the user in the past and suggest customization options based on them. The customization unit can also identify a preferred design style from the user's past selection history and suggest customization options based on that. Furthermore, the customization unit can set an optimal customization schedule by referring to the user's past selection history. This allows the customization unit to provide the optimal customization method based on the past selection history. The selection history is referenced using data such as the date and time of past selections, selection contents, and selection conditions. This allows the customization unit to select the optimal customization method by referring to the user's past selection history during customization.

[0054] The customization unit can customize the customization means based on the user's current living situation during customization. For example, the customization unit can customize the customization means based on the user's current living situation during customization. For example, if the user has small children, the customization unit can provide a customization option that emphasizes safety. Furthermore, if the user has pets, the customization unit can provide a customization option that uses durable materials. Furthermore, if the user moves frequently, the customization unit can provide a customization option that reflects an easy-to-assemble design. This allows the customization unit to provide the optimal customization means based on the user's current living situation. The evaluation of the living situation is performed using criteria such as family composition, lifestyle patterns, and income status. This allows the customization unit to customize the customization means based on the user's current living situation during customization.

[0055] The customization unit can select the optimal customization method by taking into account the user's geographical location information during customization. For example, the customization unit can select the optimal customization method by taking into account the user's geographical location information during customization. For example, if the user lives in an urban area, the customization unit can provide customization options that suit the urban interior style. Furthermore, if the user lives in a suburban area, the customization unit can provide customization options that make use of natural materials. Furthermore, if the user lives in a specific region, the customization unit can provide customization options that suit the culture and style of that region. This allows the customization unit to provide the optimal customization method based on the geographical location information. The geographical location information is acquired using, for example, GPS data or address information. This allows the customization unit to select the optimal customization method by taking into account the user's geographical location information during customization.

[0056] The customization unit can analyze the user's social media activity and suggest customization options during customization. For example, the customization unit can analyze the user's social media activity and suggest customization options during customization. For example, the customization unit can analyze the interior style shared by the user on social media and suggest customization options that match it. The customization unit can also identify the user's preferred design from the content of the user's social media posts and suggest customization options based on that. Furthermore, the customization unit can analyze the reactions of the user's social media followers and suggest customization options that reflect the most popular styles. This allows the customization unit to provide optimal customization options based on the social media activity. The analysis of social media activity is performed using data such as the content of posts, the number of likes, and comments. This allows the customization unit to analyze the user's social media activity and suggest customization options during customization.

[0057] The delivery unit can select the optimal delivery method by referring to the user's past delivery history at the time of delivery. The delivery unit can, for example, select the optimal delivery method by referring to the user's past delivery history at the time of delivery. The delivery unit can, for example, refer to delivery methods used by the user in the past and suggest the optimal delivery method based on that. The delivery unit can also identify a preferred delivery time zone from the user's past delivery history and set a delivery schedule based on that. Furthermore, the delivery unit can select the optimal delivery means by referring to the user's past delivery history. This allows the delivery unit to provide the optimal delivery method based on the past delivery history. The delivery history is referenced using data such as past delivery dates and times, delivery locations, and delivery conditions. This allows the delivery unit to select the optimal delivery method by referring to the user's past delivery history at the time of delivery.

[0058] The delivery unit can customize the delivery means based on the user's current living situation at the time of delivery. For example, the delivery unit can customize the delivery means based on the user's current living situation at the time of delivery. For example, if the user has small children, the delivery unit can select a delivery method that prioritizes safety. Furthermore, if the user has pets, the delivery unit can select a delivery method that takes the pets into consideration. Furthermore, if the user moves frequently, the delivery unit can provide a flexible delivery schedule. This allows the delivery unit to provide the optimal delivery means based on the user's current living situation. The evaluation of the living situation is performed using criteria such as family composition, lifestyle patterns, and income status. This allows the delivery unit to customize the delivery means based on the user's current living situation at the time of delivery.

[0059] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, the delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user lives in an urban area, the delivery unit selects the optimal delivery method according to the traffic conditions in the urban area. Furthermore, if the user lives in a suburban area, the delivery unit can select the optimal delivery method according to the road conditions in the suburban area. Furthermore, if the user lives in a specific area, the delivery unit can select the optimal delivery method according to the characteristics of that area. This allows the delivery unit to provide the optimal delivery method based on the geographical location information. The geographical location information is acquired using, for example, GPS data or address information. This allows the delivery unit to select the optimal delivery method at the time of delivery, taking into account the user's geographical location information.

[0060] The delivery unit can analyze the user's social media activity and suggest a delivery method at the time of delivery. For example, the delivery unit can analyze the user's social media activity and suggest a delivery method at the time of delivery. For example, the delivery unit can analyze delivery experiences shared by the user on social media and suggest an optimal delivery method based on the analysis. The delivery unit can also identify a preferred delivery time slot from the user's social media posts and set a delivery schedule based on the identified time slot. Furthermore, the delivery unit can analyze the reactions of the user's social media followers and suggest the most popular delivery method. This allows the delivery unit to provide an optimal delivery method based on the social media activity. The analysis of social media activity is performed using data such as the post content, the number of likes, and comments. This allows the delivery unit to analyze the user's social media activity and suggest a delivery method at the time of delivery.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The reception unit can analyze the user's past interior style history and suggest optimal shooting angles and lighting conditions. For example, the reception unit can suggest the most effective shooting method based on the interior style the user has previously selected. The reception unit can also analyze the quality of photos the user has previously taken and suggest optimal camera settings. Furthermore, the reception unit can identify the most successful shooting method from the user's past shooting history and recommend it. This allows the reception unit to suggest the optimal shooting method based on the user's past interior style history.

[0063] The reception unit can suggest optimal shooting locations and times taking into consideration the user's geographical location information. For example, if the user lives in an urban area, the reception unit can suggest shooting locations and times that suit the characteristics of the urban area. Also, if the user lives in a suburban area, the reception unit can suggest shooting locations and times that make use of natural light. Furthermore, if the user lives in a specific area, the reception unit can suggest shooting locations and times that suit the characteristics of that area. This allows the reception unit to suggest optimal shooting locations and times based on the geographical location information.

[0064] When generating a design, the generation unit can generate an optimal design by referring to the user's past selection history. For example, the most effective design is generated based on the design style the user has previously selected. The generation unit can also identify the user's preferred colors and materials from the user's past selection history and generate a design based on them. Furthermore, the generation unit can set an optimal design schedule by referring to the user's past selection history. This allows the generation unit to provide an optimal design based on the user's past selection history.

[0065] When presenting designs, the presentation unit can analyze the user's social media activity and suggest the most suitable design. For example, the presentation unit can analyze the interior style shared by the user on social media and suggest a design that matches it. The presentation unit can also identify the user's preferred designs from the content of the user's social media posts and suggest designs based on those. Furthermore, the presentation unit can analyze the reactions of the user's social media followers and suggest the most popular designs. This allows the presentation unit to provide the most suitable design based on the user's social media activity.

[0066] The production department can customize the production method based on the user's current living situation during production. For example, if the user has small children, a production method that prioritizes safety can be selected. If the user has pets, a production method that uses durable materials can be selected. Furthermore, if the user moves frequently, a production method that reflects an easy-to-assemble design can be selected. In this way, the production department can provide the optimal production method based on the user's current living situation.

[0067] At the time of delivery, the delivery unit can select the optimal delivery method by referring to the user's past delivery history. For example, the delivery unit can refer to delivery methods used by the user in the past and suggest the optimal delivery method based on that. The delivery unit can also identify the user's preferred delivery time slots from the user's past delivery history and set a delivery schedule based on that. Furthermore, the delivery unit can select the optimal delivery means by referring to the user's past delivery history. This allows the delivery unit to provide the optimal delivery method based on the past delivery history.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The reception unit allows the user to take a photo of the room and input it into the AI. When taking a photo of the room, it is recommended that the user take photos from multiple angles so that the entire room can be seen. For example, photos taken from all four corners of the room can be input into the AI. Step 2: The generator analyzes the photo of the room input by the receiver and generates furniture designs. For example, the generator analyzes the size, shape, and interior style of the room and generates furniture designs based on that. For example, if the room has a modern style, it generates furniture with a modern design. Step 3: The presentation unit presents the designs generated by the generation unit to the user. For example, the presentation unit presents a plurality of designs to the user and produces furniture based on the design selected by the user. Step 4: The production unit produces furniture based on the design selected by the user via the presentation unit. For example, the production unit produces furniture based on the design selected by the user, delivers the produced furniture to the user's room, and installs it.

[0070] (Example 2) A furniture production system according to an embodiment of the present invention uses AI to generate images of furniture suited to a room and then produces the furniture based on the generated images. This furniture production system begins when a user takes a photo of the room and inputs it into the AI. The AI ​​then analyzes the photo and generates furniture designs that match the room's size and interior style. The generated furniture designs are presented to the user, and the furniture is produced based on the design selected by the user. This system allows users to easily obtain furniture that best suits their room. For example, a user may take a photo of the room and input it into the AI. It is recommended that photos be taken from multiple angles to provide an overall view of the room. For example, photos taken from all four corners of the room may be input into the AI. The AI ​​then analyzes the input photo of the room. The AI ​​analyzes the size, shape, and interior style of the room and generates furniture designs based on the analysis. For example, if the room has a modern style, it generates furniture with a modern design. The generated furniture designs are then presented to the user. The user can select the design that best suits their preferences from the presented designs. For example, multiple designs may be presented and the user may choose one. Finally, the furniture is produced based on the design selected by the user. The created furniture is delivered to the user's room and installed. This system allows the user to easily obtain the furniture that best suits their room. In this way, the furniture creation system can provide the user with the furniture that best suits their room.

[0071] A furniture production system according to an embodiment includes a reception unit, a generation unit, a presentation unit, and a production unit. The reception unit allows a user to take a photo of a room and input the photo to an AI. When taking a photo of a room, it is recommended that the user take photos from multiple angles so that the entire room can be seen. For example, photos taken from all four corners of the room can be input to the AI. The generation unit analyzes the photo of the room input by the reception unit and generates a furniture design. The generation unit analyzes, for example, the size, shape, and interior style of the room and generates a furniture design based on the analysis. For example, if the room has a modern style, it generates furniture with a modern design. The presentation unit presents the design generated by the generation unit to the user. For example, the presentation unit presents multiple designs to the user and produces furniture based on a design selected by the user. The production unit produces furniture based on the design selected by the user via the presentation unit. For example, the production unit produces furniture based on the design selected by the user, and delivers and installs the produced furniture in the user's room. This allows the furniture production system according to an embodiment to provide furniture optimal for the user's room.

[0072] The generation unit may include an analysis unit that analyzes the size of a room or the style of an interior. For example, the analysis unit may measure the size of a room and analyze the style of the interior. For example, the analysis unit may measure the size of a room in square meters or cubic meters and classify the style of the interior into categories such as modern, classic, and minimalist. For example, the analysis unit may measure the dimensions of the room using image recognition technology to measure the size of the room. Furthermore, the analysis unit may analyze the design elements of the room using feature extraction technology to analyze the style of the interior. This allows the analysis unit to generate an optimal furniture design based on the size of the room and the style of the interior.

[0073] The presentation unit may include a customization unit that allows a user to specify a specific color or material. The customization unit, for example, allows the user to customize the furniture design by specifying a specific color or material. The customization unit, for example, may use RGB values ​​or a color picker to allow the user to specify a color. The customization unit may also provide material options, such as wood, metal, and plastic, to allow the user to specify a material. The customization unit, for example, provides an interface for the user to specify a color or material, and can customize the furniture design based on the color or material selected by the user. This allows the customization unit to customize the furniture design by allowing the user to specify a specific color or material.

[0074] The production unit may include a delivery unit that delivers and installs the manufactured furniture in the user's room. The delivery unit can, for example, deliver and install the manufactured furniture in the user's room. The delivery unit can, for example, arrange for a delivery company to deliver the manufactured furniture to an address specified by the user. The delivery unit can also provide procedures and tools for installing the furniture. The delivery unit can, for example, confirm the installation location of the furniture, and assemble and install the furniture according to the installation procedures. In this way, the delivery unit can deliver and install the manufactured furniture in the user's room.

[0075] The reception unit can estimate the user's emotions and determine the timing for taking a photo of the room based on the estimated user's emotions. The reception unit can, for example, estimate the user's emotions and adjust the timing for taking a photo of the room based on the estimated user's emotions. For example, if the user is relaxed, the reception unit can suggest taking a photo at a time when natural light is best. Furthermore, if the user is busy, the reception unit can suggest a method for taking photos from multiple angles in a short time. Furthermore, if the user is feeling stressed, the reception unit can provide simple guidance to minimize the photography procedure. This allows the reception unit to suggest the optimal photography timing based on the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and questionnaire results. This allows the reception unit to adjust the timing for taking a photo of the room based on the user's emotions.

[0076] The reception unit can analyze the user's past photography history and select the optimal photography method. The reception unit can, for example, analyze the user's past photography history and select the optimal photography method. The reception unit can, for example, analyze the quality of photos taken by the user in the past and suggest the optimal photography angle and lighting conditions. The reception unit can also suggest the optimal settings by referring to camera settings used by the user in the past. Furthermore, the reception unit can identify the most successful photography method from the user's past photography history and recommend it. This allows the reception unit to suggest the optimal photography method based on the past photography history. The photography history is analyzed using data such as the past photography date and time, photography location, and photography conditions. This allows the reception unit to analyze the user's past photography history and select the optimal photography method.

[0077] The reception unit can perform filtering based on the user's current interior state when taking a photo of a room. For example, when taking a photo of a room, the reception unit can perform filtering based on the user's current interior state. For example, if the room's interior is messy, the reception unit can provide advice on tidying up and create optimal shooting conditions. Furthermore, if the room's interior is biased toward a particular style, the reception unit can apply a filter that matches that style. Furthermore, the reception unit can automatically select an optimal filter based on the lighting conditions of the room. This allows the reception unit to apply an optimal filter based on the interior state. The filtering method is performed using, for example, image processing technology or a type of filter. This allows the reception unit to perform filtering based on the user's current interior state when taking a photo of a room.

[0078] The reception unit can estimate the user's emotions and determine the priority of photos to be taken based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and determine the priority of photos to be taken based on the estimated user emotions. For example, the reception unit can suggest that, when the user is relaxed, the user should prioritize capturing an overall image. Furthermore, when the user is in a hurry, the reception unit can suggest that the user should prioritize capturing only important parts. Furthermore, when the user is excited, the reception unit can suggest that the user should prioritize capturing details. This allows the reception unit to determine the priority of photos to be taken based on the user's emotions. Emotions are estimated using techniques such as facial expression recognition, voice analysis, and questionnaire results. This allows the reception unit to determine the priority of photos to be taken based on the user's emotions.

[0079] The reception unit, when taking a photo of a room, can prioritize acquiring highly relevant photos based on the user's geographical location information. For example, when taking a photo of a room, the reception unit can prioritize acquiring highly relevant photos taking the user's geographical location information into consideration. For example, when taking a photo of a room, the reception unit can prioritize acquiring photos that match the urban interior style if the user lives in an urban area. Furthermore, when the user lives in a suburban area, the reception unit can prioritize acquiring photos that make use of natural light. Furthermore, when the user lives in a specific region, the reception unit can prioritize acquiring photos that match the culture and style of that region. This allows the reception unit to acquire optimal photos based on the geographical location information. The geographical location information is acquired using, for example, GPS data or address information. This allows the reception unit, when taking a photo of a room, to prioritize acquiring highly relevant photos based on the user's geographical location information.

[0080] The reception unit can analyze the user's social media activity when taking a photo of a room and acquire related photos. For example, when taking a photo of a room, the reception unit can analyze the user's social media activity and acquire related photos. For example, the reception unit can analyze photos shared by the user on social media and suggest optimal shooting angles and styles. The reception unit can also identify the user's preferred interior style from the content of the user's social media posts and acquire photos that match that style. Furthermore, the reception unit can analyze the reactions of the user's social media followers and suggest the most popular style. This allows the reception unit to acquire optimal photos based on the social media activity. The analysis of social media activity is performed using data such as the content of the post, the number of likes, and comments. This allows the reception unit to analyze the user's social media activity when taking a photo of a room and acquire related photos.

[0081] The generation unit can estimate the user's emotion and adjust the expression method of the generated design based on the estimated user's emotion. The generation unit can, for example, estimate the user's emotion and adjust the expression method of the generated design based on the estimated user's emotion. For example, the generation unit can generate a design with soft colors and a simple design when the user is relaxed. Furthermore, the generation unit can generate a design with bright colors and a bold design when the user is excited. Furthermore, the generation unit can generate a design with calm colors and a relaxing design when the user is stressed. This allows the generation unit to generate an optimal design according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and survey results. This allows the generation unit to adjust the expression method of the generated design based on the user's emotion.

[0082] The generation unit can adjust the level of detail of the design based on the importance of the room when generating the design. For example, the generation unit can adjust the level of detail of the design based on the importance of the room when generating the design. For example, the generation unit can generate a detailed design for an important room such as a living room. The generation unit can also generate a simple design for a room that is not used often, such as a closet. Furthermore, the generation unit can generate a practical design for a room where functionality is important, such as a kitchen. This allows the generation unit to generate an optimal design depending on the importance of the room. The importance is evaluated using criteria such as the frequency of use of the room and the area of ​​the room. This allows the generation unit to adjust the level of detail of the design based on the importance of the room when generating the design.

[0083] The generation unit can apply different design algorithms depending on the room category when generating a design. For example, the generation unit can apply different design algorithms depending on the room category when generating a design. For example, the generation unit can apply a design algorithm that emphasizes comfort to a living room. The generation unit can also apply a design algorithm that promotes relaxation to a bedroom. Furthermore, the generation unit can apply a design algorithm that promotes concentration to an office. This allows the generation unit to generate an optimal design depending on the room category. Categorization is performed using criteria such as living room, bedroom, and kitchen. This allows the generation unit to apply different design algorithms depending on the room category when generating a design.

[0084] The generation unit can estimate the user's emotion and adjust the length of the design based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the design based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit can provide a design that can be generated in a short time. Furthermore, if the user is relaxed, the generation unit can provide a detailed design. Furthermore, if the user is excited, the generation unit can provide a visually stimulating design. This allows the generation unit to provide an optimal design length depending on the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and survey results. This allows the generation unit to adjust the length of the design based on the user's emotion.

[0085] The generation unit can determine the priority of designs based on the time when the room was photographed when generating the designs. For example, the generation unit can determine the priority of designs based on the time when the room was photographed when generating the designs. For example, the generation unit can generate designs by prioritizing the most recently photographed photo of the room. The generation unit can also generate designs by prioritizing designs according to the season. Furthermore, the generation unit can generate designs by prioritizing designs suited to specific events. This allows the generation unit to generate optimal designs based on the time when the photo was photographed. The time when the photo was photographed is acquired using data such as the date and time of the photo, the season, and the time of day. This allows the generation unit to determine the priority of designs based on the time when the room was photographed when generating the designs.

[0086] The generation unit can adjust the order of designs based on the relevance of rooms when generating designs. For example, the generation unit can adjust the order of designs based on the relevance of rooms when generating designs. For example, the generation unit can first generate a design for a living room, followed by designs for other rooms. The generation unit can also generate designs by prioritizing rooms that are frequently used by the user. Furthermore, the generation unit can adjust the order of designs based on the functionality of the rooms. This allows the generation unit to generate an optimal design based on the relevance of rooms. The evaluation of relevance is performed using criteria such as the purpose of the room or the theme of the design. This allows the generation unit to adjust the order of designs based on the relevance of rooms when generating designs.

[0087] The presentation unit can estimate the user's emotion and adjust the presentation expression method based on the estimated user's emotion. For example, the presentation unit can estimate the user's emotion and adjust the presentation expression method based on the estimated user's emotion. For example, if the user is relaxed, the presentation unit can present a design with soft colors and a simple layout. If the user is excited, the presentation unit can present a design with vivid colors and dynamic effects. Furthermore, if the user is stressed, the presentation unit can present a design with calm colors and a visually relaxing layout. This allows the presentation unit to provide an optimal presentation method depending on the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and survey results. This allows the presentation unit to adjust the presentation expression method based on the user's emotion.

[0088] The presentation unit may adjust the level of detail of presentation based on the importance of the design when presenting the design. For example, the presentation unit may adjust the level of detail of presentation based on the importance of the design when presenting the design. For example, the presentation unit may provide a detailed description and a high-resolution image for an important design. The presentation unit may also provide a brief description and a low-resolution image for a less important design. Furthermore, the presentation unit may provide additional information or options for a design in which the user is particularly interested. This allows the presentation unit to provide an optimal presentation method depending on the importance of the design. The importance is evaluated using criteria such as the frequency of use of the room or the area of ​​the room. This allows the presentation unit to adjust the level of detail of presentation based on the importance of the design when presenting the design.

[0089] The presentation unit can apply different presentation algorithms depending on the design category when presenting the designs. For example, the presentation unit can apply different presentation algorithms depending on the design category when presenting the designs. For example, the presentation unit can apply a presentation algorithm that emphasizes comfort to living room designs. The presentation unit can apply a presentation algorithm that promotes relaxation to bedroom designs. The presentation unit can also apply a presentation algorithm that promotes concentration to office designs. This allows the presentation unit to provide an optimal presentation method depending on the design category. Categorization is performed using criteria such as living room, bedroom, and kitchen. This allows the presentation unit to apply different presentation algorithms depending on the design category when presenting the designs.

[0090] The presentation unit can estimate the user's emotion and adjust the length of the presentation based on the estimated user's emotion. The presentation unit can, for example, estimate the user's emotion and adjust the length of the presentation based on the estimated user's emotion. For example, when the user is in a hurry, the presentation unit can provide a short, concise presentation. Furthermore, when the user is relaxed, the presentation unit can provide a longer presentation including detailed information. Furthermore, when the user is excited, the presentation unit can provide a presentation with a visually stimulating effect. This allows the presentation unit to provide an optimal length of presentation depending on the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and survey results. This allows the presentation unit to adjust the length of the presentation based on the user's emotion.

[0091] The presentation unit can determine the priority of presentation based on the time of creation of the design at the time of presentation. For example, the presentation unit can determine the priority of presentation based on the time of creation of the design at the time of presentation. For example, the presentation unit can prioritize and present the most recently created design. The presentation unit can also prioritize and present designs according to the season. Furthermore, the presentation unit can prioritize and present designs suited to a specific event. This allows the presentation unit to present the optimal design based on the time of creation. The time of creation is acquired using data such as the date and time of creation, the season, and the time zone. This allows the presentation unit to determine the priority of presentation based on the time of creation of the design at the time of presentation.

[0092] The presentation unit can adjust the presentation order based on the relevance of the designs when presenting them. For example, the presentation unit can adjust the presentation order based on the relevance of the designs when presenting them. For example, the presentation unit can first present designs for a living room, followed by designs for other rooms. The presentation unit can also prioritize and present designs for rooms that the user frequently uses. Furthermore, the presentation unit can adjust the presentation order based on the functionality of the rooms. This allows the presentation unit to present designs in an optimal order based on the relevance of the designs. The relevance is evaluated using criteria such as the purpose of the room or the theme of the design. This allows the presentation unit to adjust the presentation order based on the relevance of the designs when presenting them.

[0093] The production unit can estimate the user's emotions and adjust the production method based on the estimated user's emotions. For example, the production unit can estimate the user's emotions and adjust the production method based on the estimated user's emotions. For example, if the user is relaxed, the production unit can select a production method that has a warm, handmade feel. Furthermore, if the user is in a hurry, the production unit can select a method that allows for quick production. Furthermore, if the user is excited, the production unit can select a production method that reflects a visually stimulating design. This allows the production unit to provide an optimal production method according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and survey results. This allows the production unit to adjust the production method based on the user's emotions.

[0094] The production department can select the optimal production method by analyzing the user's past consumption behavior during production. For example, the production department can select the optimal production method by analyzing the user's past consumption behavior during production. For example, the production department can analyze the style of furniture the user has previously purchased and select a production method that suits it. The production department can also identify the user's preferred materials and colors from the user's past consumption behavior and select a production method based on that. Furthermore, the production department can set an optimal production schedule by referring to the user's past consumption behavior. This allows the production department to provide the optimal production method based on the user's past consumption behavior. The analysis of consumption behavior is performed using data such as purchase history, consumption patterns, and expenditure amounts. This allows the production department to select the optimal production method by analyzing the user's past consumption behavior during production.

[0095] The production unit can customize the production means based on the user's current living situation during production. For example, if the user has small children, the production unit can select a production method that prioritizes safety. If the user has pets, the production unit can select a production method that uses durable materials. Furthermore, if the user moves frequently, the production unit can select a production method that reflects an easy-to-assemble design. This allows the production unit to provide the optimal production means based on the user's current living situation. The evaluation of the living situation is performed using criteria such as family composition, lifestyle, and income status. This allows the production unit to customize the production means based on the user's current living situation during production.

[0096] The production unit can estimate the user's emotions and determine production priorities based on the estimated user emotions. For example, the production unit can estimate the user's emotions and determine production priorities based on the estimated user emotions. For example, if the user is in a hurry, the production unit can prioritize furniture that can be produced quickly. Furthermore, if the user is relaxed, the production unit can prioritize furniture that has a handmade feel. Furthermore, if the user is excited, the production unit can prioritize furniture that reflects a visually stimulating design. This allows the production unit to provide optimal production priorities according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and survey results. This allows the production unit to determine production priorities based on the user's emotions.

[0097] The production department can select the optimal production method by taking into account the user's geographical location information during production. For example, the production department can select the optimal production method by taking into account the user's geographical location information during production. For example, if the user lives in an urban area, the production department can select a production method that suits the urban interior style. Furthermore, if the user lives in a suburban area, the production department can select a production method that makes use of natural materials. Furthermore, if the user lives in a specific region, the production department can select a production method that suits the culture and style of that region. This allows the production department to provide the optimal production method based on the geographical location information. The geographical location information can be acquired using, for example, GPS data or address information. This allows the production department to select the optimal production method by taking into account the user's geographical location information during production.

[0098] The production department can analyze a user's social media activity and suggest production methods during production. For example, the production department can analyze a user's social media activity and suggest production methods during production. For example, the production department can analyze the interior style shared by the user on social media and suggest a production method that matches it. The production department can also identify a user's preferred design from the user's social media posts and suggest a production method based on that. Furthermore, the production department can analyze the reactions of the user's social media followers and suggest a production method that reflects the most popular style. This allows the production department to provide the optimal production method based on the social media activity. The analysis of social media activity is performed using data such as the post content, the number of likes, and comments. This allows the production department to analyze a user's social media activity and suggest a production method during production.

[0099] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide multiple design options. Furthermore, if the user is in a hurry, the analysis unit can perform a concise analysis and quickly suggest an optimal design. Furthermore, if the user is excited, the analysis unit can perform an analysis that reflects a visually stimulating design. This allows the analysis unit to provide an optimal analysis method depending on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and survey results. This allows the analysis unit to adjust the analysis method based on the user's emotions.

[0100] The analysis unit can optimize the analysis algorithm by referring to past data of the room during analysis. For example, the analysis unit can optimize the analysis algorithm by referring to past data of the room during analysis. For example, the analysis unit can refer to past photo data of the room and propose an optimal design. The analysis unit can also analyze the past interior style of the room and optimize the analysis algorithm based on the analysis. Furthermore, the analysis unit can refer to the past usage of the room and propose an optimal design. This allows the analysis unit to provide an optimal analysis algorithm based on the past data. Referencing the past data is performed using data such as past photography dates and times, photography locations, and photography conditions. This allows the analysis unit to optimize the analysis algorithm by referring to the past data of the room during analysis.

[0101] The analysis unit can apply different analysis methods to each room category during analysis. For example, the analysis unit can apply different analysis methods to each room category during analysis. For example, the analysis unit can apply an analysis method that emphasizes comfort to a living room. Furthermore, the analysis unit can apply an analysis method that promotes relaxation to a bedroom. Furthermore, the analysis unit can apply an analysis method that promotes concentration to an office. This allows the analysis unit to provide the optimal analysis method depending on the room category. Categorization is performed using criteria such as living room, bedroom, and kitchen. This allows the analysis unit to apply different analysis methods to each room category during analysis.

[0102] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can prioritize designs that can be analyzed quickly. Furthermore, if the user is relaxed, the analysis unit can prioritize detailed analysis. Furthermore, if the user is excited, the analysis unit can prioritize visually stimulating designs. This allows the analysis unit to provide optimal analysis priorities according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and survey results. This allows the analysis unit to determine the analysis priority based on the user's emotions.

[0103] The analysis unit can analyze changes in the analysis based on the time when the room was photographed during analysis. The analysis unit can, for example, analyze changes in the analysis based on the time when the room was photographed during analysis. The analysis unit, for example, prioritizes analysis of photos of the room that were photographed most recently. The analysis unit can also perform analysis according to the season and propose an optimal design. Furthermore, the analysis unit can perform analysis tailored to a specific event and propose an optimal design. This allows the analysis unit to provide an optimal analysis based on the time when the photo was taken. The time when the photo was taken is obtained using data such as the date and time of the photo, the season, and the time of day. This allows the analysis unit to analyze changes in the analysis based on the time when the room was photographed during analysis.

[0104] The analysis unit can perform the analysis by referring to market data related to the room during the analysis. The analysis unit can, for example, perform the analysis by referring to market data related to the room during the analysis. The analysis unit can, for example, refer to current interior market trends and propose an optimal design. The analysis unit can also refer to market data according to the room category and perform the analysis based on the market data. Furthermore, the analysis unit can refer to market data according to the region of the room and propose an optimal design. This allows the analysis unit to provide an optimal analysis based on the market data. The market data is referenced using data such as sales data and consumer survey data. This allows the analysis unit to perform the analysis by referring to market data related to the room during the analysis.

[0105] The customization unit can estimate the user's emotion and adjust the customization method based on the estimated user's emotion. For example, the customization unit can estimate the user's emotion and adjust the customization method based on the estimated user's emotion. For example, if the user is relaxed, the customization unit can provide soft colors and simple customization options. Furthermore, if the user is excited, the customization unit can provide vivid colors and diverse customization options. Furthermore, if the user is stressed, the customization unit can provide calm colors and visually relaxing customization options. This allows the customization unit to provide an optimal customization method depending on the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and survey results. This allows the customization unit to adjust the customization method based on the user's emotion.

[0106] The customization unit can select the optimal customization method by referring to the user's past selection history during customization. For example, the customization unit can select the optimal customization method by referring to the user's past selection history during customization. For example, the customization unit can refer to colors and materials selected by the user in the past and suggest customization options based on them. The customization unit can also identify a preferred design style from the user's past selection history and suggest customization options based on that. Furthermore, the customization unit can set an optimal customization schedule by referring to the user's past selection history. This allows the customization unit to provide the optimal customization method based on the past selection history. The selection history is referenced using data such as the date and time of past selections, selection contents, and selection conditions. This allows the customization unit to select the optimal customization method by referring to the user's past selection history during customization.

[0107] The customization unit can customize the customization means based on the user's current living situation during customization. For example, the customization unit can customize the customization means based on the user's current living situation during customization. For example, if the user has small children, the customization unit can provide a customization option that emphasizes safety. Furthermore, if the user has pets, the customization unit can provide a customization option that uses durable materials. Furthermore, if the user moves frequently, the customization unit can provide a customization option that reflects an easy-to-assemble design. This allows the customization unit to provide the optimal customization means based on the user's current living situation. The evaluation of the living situation is performed using criteria such as family composition, lifestyle patterns, and income status. This allows the customization unit to customize the customization means based on the user's current living situation during customization.

[0108] The customization unit can estimate a user's emotion and determine a customization priority based on the estimated user's emotion. For example, the customization unit can estimate a user's emotion and determine a customization priority based on the estimated user's emotion. For example, when the user is in a hurry, the customization unit can prioritize providing options that allow quick customization. Furthermore, when the user is relaxed, the customization unit can prioritize providing detailed customization options. Furthermore, when the user is excited, the customization unit can prioritize providing visually stimulating customization options. This allows the customization unit to provide optimal customization priorities according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and survey results. This allows the customization unit to determine a customization priority based on the user's emotion.

[0109] The customization unit can select the optimal customization method by taking into account the user's geographical location information during customization. For example, the customization unit can select the optimal customization method by taking into account the user's geographical location information during customization. For example, if the user lives in an urban area, the customization unit can provide customization options that suit the urban interior style. Furthermore, if the user lives in a suburban area, the customization unit can provide customization options that make use of natural materials. Furthermore, if the user lives in a specific region, the customization unit can provide customization options that suit the culture and style of that region. This allows the customization unit to provide the optimal customization method based on the geographical location information. The geographical location information is acquired using, for example, GPS data or address information. This allows the customization unit to select the optimal customization method by taking into account the user's geographical location information during customization.

[0110] The customization unit can analyze the user's social media activity and suggest customization options during customization. For example, the customization unit can analyze the user's social media activity and suggest customization options during customization. For example, the customization unit can analyze the interior style shared by the user on social media and suggest customization options that match it. The customization unit can also identify the user's preferred design from the content of the user's social media posts and suggest customization options based on that. Furthermore, the customization unit can analyze the reactions of the user's social media followers and suggest customization options that reflect the most popular styles. This allows the customization unit to provide optimal customization options based on the social media activity. The analysis of social media activity is performed using data such as the content of posts, the number of likes, and comments. This allows the customization unit to analyze the user's social media activity and suggest customization options during customization.

[0111] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user's emotions. For example, the delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user's emotions. For example, the delivery unit can provide a flexible delivery schedule when the user is relaxed. Furthermore, the delivery unit can provide a quick delivery option when the user is in a hurry. Furthermore, the delivery unit can provide a method that allows the user to complete delivery with a simple procedure when the user is feeling stressed. This allows the delivery unit to provide the optimal delivery method depending on the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and survey results. This allows the delivery unit to adjust the delivery method based on the user's emotions.

[0112] The delivery unit can select the optimal delivery method by referring to the user's past delivery history at the time of delivery. The delivery unit can, for example, select the optimal delivery method by referring to the user's past delivery history at the time of delivery. The delivery unit can, for example, refer to delivery methods used by the user in the past and suggest the optimal delivery method based on that. The delivery unit can also identify a preferred delivery time zone from the user's past delivery history and set a delivery schedule based on that. Furthermore, the delivery unit can select the optimal delivery means by referring to the user's past delivery history. This allows the delivery unit to provide the optimal delivery method based on the past delivery history. The delivery history is referenced using data such as past delivery dates and times, delivery locations, and delivery conditions. This allows the delivery unit to select the optimal delivery method by referring to the user's past delivery history at the time of delivery.

[0113] The delivery unit can customize the delivery means based on the user's current living situation at the time of delivery. For example, the delivery unit can customize the delivery means based on the user's current living situation at the time of delivery. For example, if the user has small children, the delivery unit can select a delivery method that prioritizes safety. Furthermore, if the user has pets, the delivery unit can select a delivery method that takes the pets into consideration. Furthermore, if the user moves frequently, the delivery unit can provide a flexible delivery schedule. This allows the delivery unit to provide the optimal delivery means based on the user's current living situation. The evaluation of the living situation is performed using criteria such as family composition, lifestyle patterns, and income status. This allows the delivery unit to customize the delivery means based on the user's current living situation at the time of delivery.

[0114] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. For example, the delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. For example, the delivery unit can prioritize quick delivery when the user is in a hurry. Furthermore, the delivery unit can provide a flexible delivery schedule when the user is relaxed. Furthermore, the delivery unit can provide visually stimulating packaging when the user is excited. This allows the delivery unit to provide optimal delivery priorities according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and survey results. This allows the delivery unit to determine delivery priorities based on the user's emotions.

[0115] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, the delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user lives in an urban area, the delivery unit selects the optimal delivery method according to the traffic conditions in the urban area. Furthermore, if the user lives in a suburban area, the delivery unit can select the optimal delivery method according to the road conditions in the suburban area. Furthermore, if the user lives in a specific area, the delivery unit can select the optimal delivery method according to the characteristics of that area. This allows the delivery unit to provide the optimal delivery method based on the geographical location information. The geographical location information is acquired using, for example, GPS data or address information. This allows the delivery unit to select the optimal delivery method at the time of delivery, taking into account the user's geographical location information.

[0116] The delivery unit can analyze the user's social media activity and suggest a delivery method at the time of delivery. For example, the delivery unit can analyze the user's social media activity and suggest a delivery method at the time of delivery. For example, the delivery unit can analyze delivery experiences shared by the user on social media and suggest an optimal delivery method based on the analysis. The delivery unit can also identify a preferred delivery time slot from the user's social media posts and set a delivery schedule based on the identified time slot. Furthermore, the delivery unit can analyze the reactions of the user's social media followers and suggest the most popular delivery method. This allows the delivery unit to provide an optimal delivery method based on the social media activity. The analysis of social media activity is performed using data such as the post content, the number of likes, and comments. This allows the delivery unit to analyze the user's social media activity and suggest a delivery method at the time of delivery. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, and production unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit allows the user to take a photo of the room using the camera 42 of the smart device 14 and input the photo to the AI ​​via the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photo of the room and generates a furniture design. The presentation unit presents the generated design to the user using, for example, the display 40A of the smart device 14. The production unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and produces furniture based on the design selected by the user, and delivers and installs it. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, and production unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit allows the user to take a photo of the room using the camera 42 of the smart glasses 214 and input the photo to the AI ​​via the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photo of the room and generates a furniture design. The presentation unit presents the generated design to the user, for example, using the display of the smart glasses 214. The production unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and produces, delivers, and installs furniture based on the design selected by the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, and production unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit allows the user to take a photo of the room using the camera 42 of the headset-type terminal 314 and input the photo to the AI ​​via the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photo of the room and generates a furniture design. The presentation unit presents the generated design to the user using, for example, the display 343 of the headset-type terminal 314. The production unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and produces furniture based on the design selected by the user, and delivers and installs it. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, and production unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit allows the user to take a photo of the room using the camera 42 of the robot 414 and input the photo to the AI ​​via the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photo of the room and generates a furniture design. The presentation unit presents the generated design to the user using, for example, the display of the robot 414. The production unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and produces furniture based on the design selected by the user, and delivers and installs it.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The reception unit can analyze the user's past interior style history and suggest optimal shooting angles and lighting conditions. For example, the reception unit can suggest the most effective shooting method based on the interior style the user has previously selected. The reception unit can also analyze the quality of photos the user has previously taken and suggest optimal camera settings. Furthermore, the reception unit can identify the most successful shooting method from the user's past shooting history and recommend it. This allows the reception unit to suggest the optimal shooting method based on the user's past interior style history.

[0119] The generation unit can estimate the user's emotions and adjust the colors and materials of the generated design based on the estimated user's emotions. For example, if the user is relaxed, a design using soft colors and natural materials can be generated. If the user is excited, a design using vivid colors and modern materials can be generated. Furthermore, if the user is stressed, a design using calm colors and relaxing materials can be generated. In this way, the generation unit can generate an optimal design according to the user's emotions.

[0120] The presentation unit can estimate the user's emotions and adjust the order of designs to be presented based on the estimated user's emotions. For example, if the user is relaxed, a simple and calming design can be presented first. If the user is excited, a visually stimulating design can be presented first. Furthermore, if the user is stressed, a relaxing design can be presented first. In this way, the presentation unit can provide an optimal order of designs according to the user's emotions.

[0121] The production department can estimate the user's emotions and adjust the production schedule based on the estimated user's emotions. For example, if the user is in a hurry, a schedule that allows for quick production can be set. If the user is relaxed, a production method with a handmade feel can be selected and a slow schedule can be set. Furthermore, if the user is excited, a production method that reflects a visually stimulating design can be selected and the schedule can be adjusted. In this way, the production department can provide an optimal production schedule according to the user's emotions.

[0122] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user's emotions. For example, if the user is relaxed, a flexible delivery schedule can be provided. If the user is in a hurry, a quick delivery option can be provided. Furthermore, if the user is stressed, a method that allows delivery to be completed with a simple procedure can be provided. This allows the delivery unit to provide the optimal delivery method according to the user's emotions.

[0123] The reception unit can suggest optimal shooting locations and times taking into consideration the user's geographical location information. For example, if the user lives in an urban area, the reception unit can suggest shooting locations and times that suit the characteristics of the urban area. Also, if the user lives in a suburban area, the reception unit can suggest shooting locations and times that make use of natural light. Furthermore, if the user lives in a specific area, the reception unit can suggest shooting locations and times that suit the characteristics of that area. This allows the reception unit to suggest optimal shooting locations and times based on the geographical location information.

[0124] When generating a design, the generation unit can generate an optimal design by referring to the user's past selection history. For example, the most effective design is generated based on the design style the user has previously selected. The generation unit can also identify the user's preferred colors and materials from the user's past selection history and generate a design based on them. Furthermore, the generation unit can set an optimal design schedule by referring to the user's past selection history. This allows the generation unit to provide an optimal design based on the user's past selection history.

[0125] When presenting designs, the presentation unit can analyze the user's social media activity and suggest the most suitable design. For example, the presentation unit can analyze the interior style shared by the user on social media and suggest a design that matches it. The presentation unit can also identify the user's preferred designs from the content of the user's social media posts and suggest designs based on that. Furthermore, the presentation unit can analyze the reactions of the user's social media followers and suggest the most popular designs. This allows the presentation unit to provide the most suitable design based on the user's social media activity.

[0126] The production department can customize the production method based on the user's current living situation during production. For example, if the user has small children, a production method that prioritizes safety can be selected. If the user has pets, a production method that uses durable materials can be selected. Furthermore, if the user moves frequently, a production method that reflects an easy-to-assemble design can be selected. In this way, the production department can provide the optimal production method based on the user's current living situation.

[0127] At the time of delivery, the delivery unit can select the optimal delivery method by referring to the user's past delivery history. For example, the delivery unit can refer to delivery methods used by the user in the past and suggest the optimal delivery method based on that. The delivery unit can also identify the user's preferred delivery time slots from the user's past delivery history and set a delivery schedule based on that. Furthermore, the delivery unit can select the optimal delivery means by referring to the user's past delivery history. This allows the delivery unit to provide the optimal delivery method based on the past delivery history.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The reception unit allows the user to take a photo of the room and input it into the AI. When taking a photo of the room, it is recommended that the user take photos from multiple angles so that the entire room can be seen. For example, photos taken from all four corners of the room can be input into the AI. Step 2: The generator analyzes the photo of the room input by the receiver and generates furniture designs. For example, the generator analyzes the size, shape, and interior style of the room and generates furniture designs based on that. For example, if the room has a modern style, it generates furniture with a modern design. Step 3: The presentation unit presents the designs generated by the generation unit to the user. For example, the presentation unit presents a plurality of designs to the user and produces furniture based on the design selected by the user. Step 4: The production unit produces furniture based on the design selected by the user via the presentation unit. For example, the production unit produces furniture based on the design selected by the user, delivers the produced furniture to the user's room, and installs it.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0132] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0170] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0172] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0173] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0174] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0185] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0186] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0187] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0191] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0192] 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.

[0193] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0194] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0195] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0196] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0198] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0201] [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The reception desk takes photos of the room and inputs them into the AI, A generation unit analyzes the room photos entered by the reception unit and generates furniture designs, A presentation unit that presents the design generated by the generation unit to the user, A manufacturing unit that produces furniture based on the design selected by the user via the aforementioned display unit, Equipped with A system characterized by:

2. The generation unit It includes an analysis unit that analyzes the size of the room or the style of the interior. The system of claim 1 .

3. The presentation unit It features a customization section that allows users to specify particular colors and materials. The system of claim 1 .

4. The manufacturing department The company has a delivery department that delivers and installs the manufactured furniture in the user's room. The system of claim 1 .

5. The reception unit The system estimates the user's emotions and determines the timing for taking photos of the room based on those estimated emotions. The system of claim 1 .

6. The reception unit The system analyzes the user's past photo-taking history and selects the optimal shooting method. The system of claim 1 .

7. The reception unit When taking photos of a room, the system filters the images based on the user's current interior design. The system of claim 1 .

8. The reception unit It estimates the user's emotions and determines the priority of photos to take based on the estimated user emotions. The system of claim 1 .

9. The reception unit When taking photos of a room, the system prioritizes retrieving photos that are more relevant based on the user's geographical location. The system of claim 1 .

Images