System

Abstract

An object of a system according to an embodiment is to propose an optimal layout on the basis of a photograph of a room of a user and propose purchase of furniture or an interior.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The registrar uploads the picture of the room of the user. The analysis unit analyzes the photograph uploaded by the reception unit and grasps the size and shape of the room and the arrangement of the existing furniture. The generation unit generates a layout based on the information grasped by the analysis unit. The proposal unit makes a proposal to purchase furniture or an interior based on the layout generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose optimal layouts based on photos of a user's room and make purchasing suggestions for furniture and interior items, so there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal layout based on a photo of a user's room and make purchasing suggestions for furniture and interior items. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit uploads photos of the user's room. The analysis unit analyzes the photos uploaded by the reception unit to determine the size and shape of the room and the layout of existing furniture. The generation unit generates a layout based on the information determined by the analysis unit. The proposal unit makes purchasing suggestions for furniture and interior items based on the layout generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal layout based on a photo of the user's room and make purchasing suggestions for furniture and interior items. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI app according to an embodiment of the present invention is a system that proposes optimal layouts based on photos of a user's room and even makes purchasing suggestions for furniture and interior items to match. The AI ​​app allows users to upload photos of their rooms, and the AI ​​analyzes the photos to determine the room's size, shape, and the layout of existing furniture. Based on this, the AI ​​generates an optimal layout and proposes it to the user. Furthermore, users can input requests for the generated layout, such as "more cute" or "black as the main color." The AI ​​analyzes these requests and adjusts the layout accordingly. For example, in response to a request for "more cute," the AI ​​suggests furniture and interior items in pink or pastel colors. In response to a request for "black as the main color," the AI ​​suggests furniture and interior items with a chic black design. The AI ​​also makes purchasing suggestions for furniture and interior items based on the proposed layout. Users can purchase the suggested furniture and interior items within the app. This allows users to easily change the room layout and purchase the necessary furniture and interior items. This allows the AI ​​app to easily realize the optimal room layout tailored to the user's preferences. For example, when moving or redecorating, users can simply upload photos of their room, and the AI ​​will propose the optimal layout and purchase the necessary furniture and interior items. In addition, by adjusting the layout according to the user's requests, it is possible to create a room that is more satisfying.

[0029] An AI application according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit uploads a photo of a user's room. The photo of the user's room may be in, for example, JPEG format, PNG format, or the like, but is not limited to these examples. The reception unit, for example, prompts the user to take a photo that captures the entire room and upload it to the application. The analysis unit analyzes the photo uploaded by the reception unit to determine the size and shape of the room and the layout of existing furniture. The analysis unit, for example, uses image recognition technology to analyze the size and shape of the room and the layout of existing furniture. For example, the analysis unit may use deep learning to detect objects in the image and identify their positions and sizes. The analysis unit may also use a computer vision algorithm to analyze the shape of the room and the layout of furniture. The generation unit generates an optimal layout based on the information obtained by the analysis unit. For example, the generation unit uses AI to generate an optimal layout based on the size and shape of the room and the layout of existing furniture. The generation unit, for example, uses a genetic algorithm to generate an optimal furniture layout pattern. The generation unit can also use simulated annealing to generate a layout that takes into consideration traffic flow lines and design consistency. The suggestion unit makes purchasing suggestions for furniture and interior items based on the layout generated by the generation unit. The suggestion unit, for example, analyzes the user's past purchase history and preference data to select furniture and interior items to suggest. The suggestion unit, for example, uses a recommendation system to suggest furniture and interior items that match the user's preferences. The suggestion unit can also make suggestions using collaborative filtering, taking into account the purchase histories of other users. As a result, the AI ​​app according to the embodiment can propose an optimal layout based on photos of the user's room and even make purchasing suggestions for furniture and interior items that match it.

[0030] The generation unit can adjust the layout based on the user's request. The generation unit, for example, analyzes the user's request and adjusts the layout. For example, if the user inputs the request "more cute," the generation unit can suggest furniture and interior decor in pink or pastel colors. Furthermore, if the user inputs the request "based on black," the generation unit can suggest furniture and interior decor with a chic design based on black. The generation unit can also analyze color and design trends and adjust the layout according to the user's request. For example, the generation unit can analyze the latest interior design trends and generate a layout that meets the user's request. This allows the layout to be adjusted according to the user's request. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's request to the generation AI and cause the generation AI to generate a layout based on the request.

[0031] The suggestion unit can select furniture and interior items to suggest based on the user's past purchase history and preference data. The suggestion unit, for example, analyzes the user's past purchase history and preference data to select the furniture and interior items to suggest. For example, the suggestion unit references a database of the user's purchase history to analyze trends in furniture and interior items purchased in the past. The suggestion unit can also collect and analyze preference data based on the user's ratings and reviews. For example, the suggestion unit analyzes features of furniture and interior items that the user has given high ratings and reflects the features in the suggestions. The suggestion unit can also collect and analyze preference data based on the user's browsing history. For example, the suggestion unit analyzes features of furniture and interior items frequently viewed by the user and reflects the features in the suggestions. This makes it possible to suggest more appropriate furniture and interior items based on the user's past purchase history and preference data. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's purchase history data into AI and have the AI ​​select the furniture and interior items to suggest.

[0032] The analysis unit can analyze the size and shape of a room and the layout of existing furniture using image recognition technology. The analysis unit can analyze the size and shape of a room and the layout of existing furniture using, for example, image recognition technology. For example, the analysis unit can detect objects in an image using deep learning and determine their respective positions and sizes. The analysis unit can also analyze the shape of a room and the layout of furniture using a computer vision algorithm. For example, the analysis unit can measure the area and ceiling height of a room to determine the shape of the room. The analysis unit can also analyze the location information, type, and size of existing furniture to determine the layout of the furniture. In this way, the size and shape of a room and the layout of existing furniture can be accurately determined using image recognition technology. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input photo data of a room into AI and have the AI ​​analyze the size, shape, and layout of the room.

[0033] The generation unit can generate a layout that meets the user's request based on color and design trends. The generation unit, for example, analyzes color and design trends and generates a layout that meets the user's request. For example, the generation unit refers to trend reports to understand the latest color and design trends. The generation unit can also use social media analysis to collect color and design trends that meet the user's request. For example, the generation unit analyzes popular interior designs on social media and generates a layout that meets the user's request. The generation unit can also generate a layout that reflects color and design trends based on the user's request. For example, if the user inputs a request such as "more cute," the generation unit can suggest furniture and interior decor in pink or pastel colors. If the user inputs a request such as "based on black," the generation unit can suggest furniture and interior decor with a chic design based on black. In this way, by analyzing color and design trends, a layout that meets the user's request can be generated. Some or all of the above-described processing by the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input color and design trend data into the generation AI and have the generation AI generate a layout based on the customer's requests.

[0034] The suggestion unit can execute a purchasing procedure for furniture and interior items suggested within the app. For example, the suggestion unit executes a purchasing procedure for furniture and interior items suggested within the app. For example, the suggestion unit provides an interface through which a user can select suggested furniture and interior items and complete the purchasing procedure. The suggestion unit can also provide information about the purchasing procedure, such as a payment method, delivery procedure, and return policy. For example, the suggestion unit can provide a payment method, such as a credit card or electronic money, and complete the purchasing procedure for the furniture and interior items selected by the user. The suggestion unit can also provide information about the delivery procedure and return policy after the purchasing procedure is completed. For example, the suggestion unit can track the delivery status of the purchased furniture and interior items and notify the user. The suggestion unit can also provide an interface for completing the return procedure based on the return policy. This allows the user to complete the purchasing procedure for the furniture and interior items suggested within the app. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information about the purchasing procedure into AI and leave the execution of the purchasing procedure to AI.

[0035] The reception unit can analyze the user's past photo upload history and select an appropriate upload method. The reception unit, for example, analyzes the user's past photo upload history and selects the optimal upload method. For example, the reception unit may preferentially suggest an upload method (e.g., voice input) that the user has frequently used in the past. If the user tends to upload during a specific time period, the reception unit can also send a notification during that time period. The reception unit can also suggest the optimal upload method based on the types of photos the user has previously uploaded. For example, the reception unit may analyze the content of photos the user has previously uploaded and suggest related upload methods. This allows the optimal upload method to be selected by analyzing the user's past photo upload history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's upload history data into AI and have the AI ​​select the optimal upload method.

[0036] The reception unit may filter photos based on the user's current project or areas of interest when uploading photos. For example, the reception unit may suggest that the user upload only photos related to the user's current project or areas of interest. The reception unit may also prioritize uploading related photos based on the user's areas of interest. If the user is interested in a particular theme, the reception unit may filter and upload photos related to that theme. For example, the reception unit may analyze tagging data for the user's project management tool or areas of interest and filter related photos. By filtering photos based on the user's current project or areas of interest, highly relevant photos can be uploaded. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's project data into AI and have the AI ​​perform the filtering.

[0037] The reception unit can select an appropriate upload means according to the user's input method when uploading a photo. For example, when uploading a photo, the reception unit selects the optimal upload means according to the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the reception unit can provide a means for uploading photos by voice. If the user prefers text input, the reception unit can also provide a means for inputting a description of the photo in text. If the user prefers image input, the reception unit can also provide a means for uploading images by drag and drop. This improves user convenience by selecting the optimal upload means according to the user's input method. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data into AI and have the AI ​​select the optimal upload means.

[0038] When uploading photos, the reception unit can prioritize uploading highly relevant photos based on the user's geographical location information. For example, when uploading photos, the reception unit prioritizes uploading highly relevant photos by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes uploading photos related to that area. Furthermore, when the user is traveling, the reception unit can prioritize uploading photos of the travel destination. Furthermore, when the user is at home, the reception unit can prioritize uploading photos related to the home. In this way, highly relevant photos can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into AI and cause the AI ​​to select highly relevant photos.

[0039] The reception unit can upload related photos based on the user's social media activity when uploading a photo. For example, when uploading a photo, the reception unit analyzes the user's social media activity and uploads related photos. For example, the reception unit prioritizes uploading photos shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and upload related photos. The reception unit can also upload related photos by referring to the activity of the user's friends on social media. In this way, related photos can be uploaded by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI and have the AI ​​select related photos.

[0040] The reception unit can customize the upload method based on the user's past feedback when uploading photos. For example, the reception unit customizes the upload method by reflecting the user's past feedback when uploading photos. For example, the reception unit preferentially suggests upload methods that the user has previously preferred. The reception unit can also simplify the upload procedure based on the user's past feedback. The reception unit can also customize the interface when uploading by referring to the user's past feedback. In this way, the upload method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into AI and have the AI ​​customize the upload method.

[0041] The analysis unit can set the level of detail of the analysis based on the importance of the room during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the room during analysis. For example, the analysis unit analyzes photos of the living room in detail. The analysis unit can also analyze photos of the bedroom with a medium level of detail. The analysis unit can also simplify and analyze photos of the closet. In this way, by adjusting the level of detail of the analysis based on the importance of the room, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input room importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0042] The analysis unit can apply an appropriate analysis algorithm depending on the room category during analysis. For example, the analysis unit applies different analysis algorithms depending on the room category during analysis. For example, the analysis unit applies an analysis algorithm that emphasizes cooking utensils and storage space to photos of kitchens. The analysis unit can also apply an analysis algorithm that emphasizes furniture arrangement and traffic flow to photos of living rooms. The analysis unit can also apply an analysis algorithm that emphasizes bed arrangement and lighting to photos of bedrooms. In this way, by applying different analysis algorithms depending on the room category, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input room category data into AI and cause the AI ​​to apply an appropriate analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit performs analysis by referring to layouts that the user has previously preferred. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. The analysis unit can also store the user's past analysis results in a database and use them for the next analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0044] The analysis unit can set analysis priorities based on when the room was photographed during analysis. For example, the analysis unit determines analysis priorities based on when the room was photographed during analysis. For example, the analysis unit prioritizes analysis of recently photographed photos. The analysis unit can also analyze photos taken by season and propose a layout appropriate for the season. The analysis unit can also prioritize analysis of photos taken during a specific event (such as moving or redecorating). This allows for more appropriate analysis by determining analysis priorities based on when the room was photographed. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input data on when the room was photographed into AI and have the AI ​​determine the analysis priorities.

[0045] The analysis unit can set the order of analysis based on the relevance of rooms during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of rooms during analysis. For example, the analysis unit performs analysis taking into account the relevance between the living room and the dining room. The analysis unit can also perform analysis taking into account the relevance between the bedroom and the closet. The analysis unit can also perform analysis taking into account the relevance between the kitchen and the pantry. In this way, adjusting the order of analysis based on the relevance of rooms allows for more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input room relevance data into AI and have the AI ​​adjust the order of analysis.

[0046] The analysis unit can set the use of technical terms during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. For example, if the user has expertise in interior design, the analysis unit can use a lot of technical terms. Furthermore, if the user is a beginner, the analysis unit can avoid technical terms and use simpler explanations. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This allows for more appropriate analysis by adjusting the use of technical terms during analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terms.

[0047] The generation unit can set the level of detail of the generation based on the importance of the rooms when generating a layout. The generation unit, for example, adjusts the level of detail of the generation based on the importance of the rooms when generating a layout. For example, the generation unit generates a detailed layout for the living room. The generation unit can also generate a medium level of detail for the bedroom layout. The generation unit can also generate a simplified layout for the closet. In this way, by adjusting the level of detail of the generation based on the importance of the rooms, a more appropriate layout can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input room importance data to AI and cause the AI ​​to adjust the level of detail of the generation.

[0048] The generation unit can apply an appropriate generation algorithm depending on the room category when generating a layout. For example, the generation unit applies different generation algorithms depending on the room category when generating a layout. For example, the generation unit applies a generation algorithm that emphasizes cooking utensils and storage space to a kitchen layout. The generation unit can also apply a generation algorithm that emphasizes furniture placement and traffic flow to a living room layout. The generation unit can also apply a generation algorithm that emphasizes bed placement and lighting to a bedroom layout. In this way, by applying different generation algorithms depending on the room category, a more appropriate layout can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input room category data into AI and cause the AI ​​to apply an appropriate generation algorithm.

[0049] The generation unit can improve the accuracy of generation based on the user's past generation results when generating a layout. For example, the generation unit improves the accuracy of generation by referring to the user's past generation results when generating a layout. For example, the generation unit generates a layout by referring to layouts that the user has previously preferred. The generation unit can also adjust the generation algorithm based on the user's past feedback. The generation unit can also store the user's past generation results in a database and use them for the next generation. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into AI and have the AI ​​improve the accuracy of generation.

[0050] The generation unit can set generation priorities based on when the rooms were photographed when generating a layout. For example, the generation unit determines generation priorities based on when the rooms were photographed when generating a layout. For example, the generation unit prioritizes the use of recently photographed photos in layout generation. The generation unit can also analyze photos taken by season and propose a layout according to the season. The generation unit can also prioritize the use of photos taken during a specific event (such as moving or redecorating) in layout generation. In this way, by determining generation priorities based on when the rooms were photographed, a more appropriate layout can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on when the rooms were photographed into AI and have the AI ​​determine the generation priorities.

[0051] The generation unit can set the generation order based on the relevance of rooms when generating a layout. The generation unit, for example, adjusts the generation order based on the relevance of rooms when generating a layout. For example, the generation unit generates a layout taking into account the relevance between the living room and the dining room. The generation unit can also generate a layout taking into account the relevance between the bedroom and the closet. The generation unit can also generate a layout taking into account the relevance between the kitchen and the pantry. In this way, by adjusting the generation order based on the relevance of rooms, a more appropriate layout can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input room relevance data into AI and cause the AI ​​to adjust the generation order.

[0052] The generation unit can set the use of technical terminology in the generation according to the user's level of expertise when generating a layout. For example, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise when generating a layout. For example, if the user has expertise in interior design, the generation unit can use a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can avoid technical terminology and use simpler language to explain things. The generation unit can also adjust the level of detail of the generated results according to the user's level of expertise. This allows for the generation of a more appropriate layout by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0053] The suggestion unit can set the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes detailed suggestions for expensive furniture and interior items. The suggestion unit can also make suggestions with a medium level of detail for furniture and interior items used daily. The suggestion unit can also make simplified suggestions for decorative items and accessories. In this way, adjusting the level of detail of the suggestion based on the importance of the product allows for more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input product importance data into AI and cause the AI ​​to adjust the level of detail of the suggestion.

[0054] The suggestion unit can apply an appropriate suggestion algorithm depending on the product category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the product category when making a suggestion. For example, the suggestion unit applies a suggestion algorithm that emphasizes placement and usability to large furniture such as sofas and beds. The suggestion unit can also apply a suggestion algorithm that emphasizes color and design to interior items such as lighting and curtains. The suggestion unit can also apply a suggestion algorithm that emphasizes trends and seasonal feel to decorative items and accessories. In this way, by applying different suggestion algorithms depending on the product category, more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product category data into AI and cause the AI ​​to apply an appropriate suggestion algorithm.

[0055] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit makes a suggestion by referring to suggestions that the user liked in the past. The suggestion unit can also adjust the suggestion algorithm based on the user's past feedback. The suggestion unit can also store the user's past suggestion results in a database and use them for the next suggestion. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0056] The suggestion unit can set a priority of suggestions based on the time of product submission when making suggestions. The suggestion unit, for example, determines the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit prioritizes new products and products on sale. The suggestion unit can also prioritize seasonal products and limited-edition products. The suggestion unit can also prioritize products in which the user has shown interest in the past. This allows more appropriate suggestions to be made by determining the priority of suggestions based on the time of product submission. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product submission time data into AI and have the AI ​​determine the priority of suggestions.

[0057] The suggestion unit can set the order of suggestions based on the relevance of products when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit suggests highly related products together, such as sofas and cushions. The suggestion unit can also suggest highly related products together, such as beds and bedding. The suggestion unit can also suggest highly related products together, such as dining tables and chairs. In this way, adjusting the order of suggestions based on the relevance of products allows for more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input product relevance data into AI and cause the AI ​​to adjust the order of suggestions.

[0058] The suggestion unit may set the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has expertise in interior design, the suggestion unit may use a lot of technical terminology. Furthermore, if the user is a beginner, the suggestion unit may avoid technical terminology and use simpler language. Furthermore, the suggestion unit may adjust the level of detail of the proposal content according to the user's level of expertise. This allows for more appropriate suggestions by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

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

[0060] The suggestion unit can also adjust the price range of the suggested products based on the user's past purchase history. For example, if the user has previously purchased expensive furniture, the suggestion unit can suggest products in a higher price range. Also, if the user has previously purchased products in a lower price range, the suggestion unit can also suggest products in a lower price range. Furthermore, if the user has a preference for a particular brand, products from that brand can be preferentially suggested. This makes it possible to make suggestions based on the user's past purchase history.

[0061] The analysis unit can also adjust the analysis algorithm based on the user's past feedback. For example, if the user has previously preferred detailed analysis, the analysis unit can perform a detailed analysis. Also, if the user has previously preferred simplified analysis, the analysis unit can perform a simplified analysis. Furthermore, if the user has previously preferred a specific analysis method, that method can be used preferentially. This makes it possible to perform analysis based on the user's past feedback.

[0062] The generation unit can also adjust the generation algorithm based on the user's past layout generation results. For example, the generation unit can refer to layouts that the user has previously preferred. The generation unit can also take into account layouts that the user has previously avoided. Furthermore, if the user has a preference for a particular design style, the generation unit can prioritize the use of that style. This makes it possible to generate layouts based on the user's past generation results.

[0063] The suggestion unit can also adjust the categories of products it suggests based on the user's current projects and areas of interest. For example, if the user is planning to redecorate their living room, the suggestion unit can prioritize and suggest products related to living rooms. Also, if the user is planning to remodel their kitchen, the suggestion unit can suggest products related to kitchens. Furthermore, if the user is interested in a particular design style, the suggestion unit can suggest products related to that style. This allows suggestions to be made based on the user's current projects and areas of interest.

[0064] The analysis unit can also set priorities for analysis based on when the room was photographed. For example, it can prioritize analysis of photos taken recently. It can also analyze photos taken by season and suggest layouts that suit the season. It can also prioritize analysis of photos taken during specific events (such as moving or redecorating). This makes it possible to set priorities for analysis based on when the room was photographed.

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

[0066] Step 1: The reception unit uploads a photo of the user's room. The photo of the user's room may be in, but is not limited to, a JPEG format or a PNG format. The reception unit, for example, prompts the user to take a photo that captures the entire room and upload it to the app. Step 2: The analysis unit analyzes the photo uploaded by the reception unit to determine the size and shape of the room and the layout of existing furniture. For example, the analysis unit uses image recognition technology to analyze the size and shape of the room and the layout of existing furniture. For example, the analysis unit uses deep learning to detect objects in the image and identify their respective positions and sizes. The analysis unit can also use computer vision algorithms to analyze the shape of the room and the layout of furniture. Step 3: The generation unit generates an optimal layout based on the information obtained by the analysis unit. For example, the generation unit uses AI to generate an optimal layout based on the size and shape of the room and the layout of existing furniture. For example, the generation unit uses a genetic algorithm to generate an optimal furniture layout pattern. The generation unit can also use simulated annealing to generate a layout that takes into account the securing of traffic flow lines and design consistency. Step 4: The suggestion unit makes purchasing suggestions for furniture and interior items based on the layout generated by the generation unit. The suggestion unit, for example, analyzes the user's past purchase history and preference data to select furniture and interior items to suggest. The suggestion unit, for example, uses a recommendation system to suggest furniture and interior items that match the user's preferences. The suggestion unit can also use collaborative filtering to make suggestions based on the purchase history of other users.

[0067] (Example 2) An AI app according to an embodiment of the present invention is a system that proposes optimal layouts based on photos of a user's room and even makes purchasing suggestions for furniture and interior items to match. The AI ​​app allows users to upload photos of their rooms, and the AI ​​analyzes the photos to determine the room's size, shape, and the layout of existing furniture. Based on this, the AI ​​generates an optimal layout and proposes it to the user. Furthermore, users can input requests for the generated layout, such as "more cute" or "black as the main color." The AI ​​analyzes these requests and adjusts the layout accordingly. For example, in response to a request for "more cute," the AI ​​suggests furniture and interior items in pink or pastel colors. In response to a request for "black as the main color," the AI ​​suggests furniture and interior items with a chic black design. The AI ​​also makes purchasing suggestions for furniture and interior items based on the proposed layout. Users can purchase the suggested furniture and interior items within the app. This allows users to easily change the room layout and purchase the necessary furniture and interior items. This allows the AI ​​app to easily realize the optimal room layout tailored to the user's preferences. For example, when moving or redecorating, users can simply upload photos of their room, and the AI ​​will propose the optimal layout and purchase the necessary furniture and interior items. In addition, by adjusting the layout according to the user's requests, it is possible to create a room that is more satisfying.

[0068] An AI application according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit uploads a photo of a user's room. The photo of the user's room may be in, for example, JPEG format, PNG format, or the like, but is not limited to these examples. The reception unit, for example, prompts the user to take a photo that captures the entire room and upload it to the application. The analysis unit analyzes the photo uploaded by the reception unit to determine the size and shape of the room and the layout of existing furniture. The analysis unit, for example, uses image recognition technology to analyze the size and shape of the room and the layout of existing furniture. For example, the analysis unit may use deep learning to detect objects in the image and identify their positions and sizes. The analysis unit may also use a computer vision algorithm to analyze the shape of the room and the layout of furniture. The generation unit generates an optimal layout based on the information obtained by the analysis unit. For example, the generation unit uses AI to generate an optimal layout based on the size and shape of the room and the layout of existing furniture. The generation unit, for example, uses a genetic algorithm to generate an optimal furniture layout pattern. The generation unit can also use simulated annealing to generate a layout that takes into consideration traffic flow lines and design consistency. The suggestion unit makes purchasing suggestions for furniture and interior items based on the layout generated by the generation unit. The suggestion unit, for example, analyzes the user's past purchase history and preference data to select furniture and interior items to suggest. The suggestion unit, for example, uses a recommendation system to suggest furniture and interior items that match the user's preferences. The suggestion unit can also make suggestions using collaborative filtering, taking into account the purchase histories of other users. As a result, the AI ​​app according to the embodiment can propose an optimal layout based on photos of the user's room and even make purchasing suggestions for furniture and interior items that match it.

[0069] The generation unit can adjust the layout based on the user's request. The generation unit, for example, analyzes the user's request and adjusts the layout. For example, if the user inputs the request "more cute," the generation unit can suggest furniture and interior decor in pink or pastel colors. Furthermore, if the user inputs the request "based on black," the generation unit can suggest furniture and interior decor with a chic design based on black. The generation unit can also analyze color and design trends and adjust the layout according to the user's request. For example, the generation unit can analyze the latest interior design trends and generate a layout that meets the user's request. This allows the layout to be adjusted according to the user's request. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's request to the generation AI and cause the generation AI to generate a layout based on the request.

[0070] The suggestion unit can select furniture and interior items to suggest based on the user's past purchase history and preference data. The suggestion unit, for example, analyzes the user's past purchase history and preference data to select the furniture and interior items to suggest. For example, the suggestion unit references a database of the user's purchase history to analyze trends in furniture and interior items purchased in the past. The suggestion unit can also collect and analyze preference data based on the user's ratings and reviews. For example, the suggestion unit analyzes features of furniture and interior items that the user has given high ratings and reflects the features in the suggestions. The suggestion unit can also collect and analyze preference data based on the user's browsing history. For example, the suggestion unit analyzes features of furniture and interior items frequently viewed by the user and reflects the features in the suggestions. This makes it possible to suggest more appropriate furniture and interior items based on the user's past purchase history and preference data. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's purchase history data into AI and have the AI ​​select the furniture and interior items to suggest.

[0071] The analysis unit can analyze the size and shape of a room and the layout of existing furniture using image recognition technology. The analysis unit can analyze the size and shape of a room and the layout of existing furniture using, for example, image recognition technology. For example, the analysis unit can detect objects in an image using deep learning and determine their respective positions and sizes. The analysis unit can also analyze the shape of a room and the layout of furniture using a computer vision algorithm. For example, the analysis unit can measure the area and ceiling height of a room to determine the shape of the room. The analysis unit can also analyze the location information, type, and size of existing furniture to determine the layout of the furniture. In this way, the size and shape of a room and the layout of existing furniture can be accurately determined using image recognition technology. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input photo data of a room into AI and have the AI ​​analyze the size, shape, and layout of the room.

[0072] The generation unit can generate a layout that meets the user's request based on color and design trends. The generation unit, for example, analyzes color and design trends and generates a layout that meets the user's request. For example, the generation unit refers to trend reports to understand the latest color and design trends. The generation unit can also use social media analysis to collect color and design trends that meet the user's request. For example, the generation unit analyzes popular interior designs on social media and generates a layout that meets the user's request. The generation unit can also generate a layout that reflects color and design trends based on the user's request. For example, if the user inputs a request such as "more cute," the generation unit can suggest furniture and interior decor in pink or pastel colors. If the user inputs a request such as "based on black," the generation unit can suggest furniture and interior decor with a chic design based on black. In this way, by analyzing color and design trends, a layout that meets the user's request can be generated. Some or all of the above-described processing by the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input color and design trend data into the generation AI and have the generation AI generate a layout based on the customer's requests.

[0073] The suggestion unit can execute a purchasing procedure for furniture and interior items suggested within the app. For example, the suggestion unit executes a purchasing procedure for furniture and interior items suggested within the app. For example, the suggestion unit provides an interface through which a user can select suggested furniture and interior items and complete the purchasing procedure. The suggestion unit can also provide information about the purchasing procedure, such as a payment method, delivery procedure, and return policy. For example, the suggestion unit can provide a payment method, such as a credit card or electronic money, and complete the purchasing procedure for the furniture and interior items selected by the user. The suggestion unit can also provide information about the delivery procedure and return policy after the purchasing procedure is completed. For example, the suggestion unit can track the delivery status of the purchased furniture and interior items and notify the user. The suggestion unit can also provide an interface for completing the return procedure based on the return policy. This allows the user to complete the purchasing procedure for the furniture and interior items suggested within the app. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information about the purchasing procedure into AI and leave the execution of the purchasing procedure to AI.

[0074] The reception unit can estimate a user's emotions and set the timing of photo uploads based on the estimated user emotions. The reception unit, for example, estimates a user's emotions and adjusts the timing of photo uploads based on the estimated user emotions. For example, the reception unit sends a notification prompting the user to upload photos if the user is relaxed. The reception unit can also suggest postponing uploading if the user is feeling stressed. The reception unit can also prompt the user to upload photos immediately if the user is excited. This allows the photo upload timing to be adjusted according to the user's emotions, thereby uploading photos at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the upload timing based on the emotion.

[0075] The reception unit can analyze the user's past photo upload history and select an appropriate upload method. The reception unit, for example, analyzes the user's past photo upload history and selects the optimal upload method. For example, the reception unit may preferentially suggest an upload method (e.g., voice input) that the user has frequently used in the past. If the user tends to upload during a specific time period, the reception unit can also send a notification during that time period. The reception unit can also suggest the optimal upload method based on the types of photos the user has previously uploaded. For example, the reception unit may analyze the content of photos the user has previously uploaded and suggest related upload methods. This allows the optimal upload method to be selected by analyzing the user's past photo upload history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's upload history data into AI and have the AI ​​select the optimal upload method.

[0076] The reception unit may filter photos based on the user's current project or areas of interest when uploading photos. For example, the reception unit may suggest that the user upload only photos related to the user's current project or areas of interest. The reception unit may also prioritize uploading related photos based on the user's areas of interest. If the user is interested in a particular theme, the reception unit may filter and upload photos related to that theme. For example, the reception unit may analyze tagging data for the user's project management tool or areas of interest and filter related photos. By filtering photos based on the user's current project or areas of interest, highly relevant photos can be uploaded. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's project data into AI and have the AI ​​perform the filtering.

[0077] The reception unit can select an appropriate upload means according to the user's input method when uploading a photo. For example, when uploading a photo, the reception unit selects the optimal upload means according to the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the reception unit can provide a means for uploading photos by voice. If the user prefers text input, the reception unit can also provide a means for inputting a description of the photo in text. If the user prefers image input, the reception unit can also provide a means for uploading images by drag and drop. This improves user convenience by selecting the optimal upload means according to the user's input method. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data into AI and have the AI ​​select the optimal upload means.

[0078] The reception unit can estimate the user's emotions and prioritize the photos to be uploaded based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the photos to be uploaded based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prioritize uploading photos with a lower importance. Also, if the user is stressed, the reception unit can prioritize uploading photos with a higher importance. Also, if the user is excited, the reception unit can prioritize uploading the most recent photos. This allows more appropriate photos to be uploaded by determining the priority of the photos to be uploaded based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to prioritize the photos based on the emotions.

[0079] When uploading photos, the reception unit can prioritize uploading highly relevant photos based on the user's geographical location information. For example, when uploading photos, the reception unit prioritizes uploading highly relevant photos by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes uploading photos related to that area. Furthermore, when the user is traveling, the reception unit can prioritize uploading photos of the travel destination. Furthermore, when the user is at home, the reception unit can prioritize uploading photos related to the home. In this way, highly relevant photos can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into AI and cause the AI ​​to select highly relevant photos.

[0080] The reception unit can upload related photos based on the user's social media activity when uploading a photo. For example, when uploading a photo, the reception unit analyzes the user's social media activity and uploads related photos. For example, the reception unit prioritizes uploading photos shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and upload related photos. The reception unit can also upload related photos by referring to the activity of the user's friends on social media. In this way, related photos can be uploaded by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI and have the AI ​​select related photos.

[0081] The reception unit can customize the upload method based on the user's past feedback when uploading photos. For example, the reception unit customizes the upload method by reflecting the user's past feedback when uploading photos. For example, the reception unit preferentially suggests upload methods that the user has previously preferred. The reception unit can also simplify the upload procedure based on the user's past feedback. The reception unit can also customize the interface when uploading by referring to the user's past feedback. In this way, the upload method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into AI and have the AI ​​customize the upload method.

[0082] The analysis unit can estimate the user's emotions and set a photo analysis method based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the photo analysis method based on the estimated user emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is stressed. The analysis unit can also perform a quick analysis when the user is excited. This allows for more appropriate analysis by adjusting the photo analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the analysis method based on the emotion.

[0083] The analysis unit can set the level of detail of the analysis based on the importance of the room during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the room during analysis. For example, the analysis unit analyzes photos of the living room in detail. The analysis unit can also analyze photos of the bedroom with a medium level of detail. The analysis unit can also simplify and analyze photos of the closet. In this way, by adjusting the level of detail of the analysis based on the importance of the room, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input room importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0084] The analysis unit can apply an appropriate analysis algorithm depending on the room category during analysis. For example, the analysis unit applies different analysis algorithms depending on the room category during analysis. For example, the analysis unit applies an analysis algorithm that emphasizes cooking utensils and storage space to photos of kitchens. The analysis unit can also apply an analysis algorithm that emphasizes furniture arrangement and traffic flow to photos of living rooms. The analysis unit can also apply an analysis algorithm that emphasizes bed arrangement and lighting to photos of bedrooms. In this way, by applying different analysis algorithms depending on the room category, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input room category data into AI and cause the AI ​​to apply an appropriate analysis algorithm.

[0085] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit performs analysis by referring to layouts that the user has previously preferred. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. The analysis unit can also store the user's past analysis results in a database and use them for the next analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotion and set the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is stressed. The analysis unit can also perform a quick analysis when the user is excited. This allows for more appropriate analysis by adjusting the length of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the analysis based on the emotion.

[0087] The analysis unit can set analysis priorities based on when the room was photographed during analysis. For example, the analysis unit determines analysis priorities based on when the room was photographed during analysis. For example, the analysis unit prioritizes analysis of recently photographed photos. The analysis unit can also analyze photos taken by season and propose a layout appropriate for the season. The analysis unit can also prioritize analysis of photos taken during a specific event (such as moving or redecorating). This allows for more appropriate analysis by determining analysis priorities based on when the room was photographed. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input data on when the room was photographed into AI and have the AI ​​determine the analysis priorities.

[0088] The analysis unit can set the order of analysis based on the relevance of rooms during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of rooms during analysis. For example, the analysis unit performs analysis taking into account the relevance between the living room and the dining room. The analysis unit can also perform analysis taking into account the relevance between the bedroom and the closet. The analysis unit can also perform analysis taking into account the relevance between the kitchen and the pantry. In this way, adjusting the order of analysis based on the relevance of rooms allows for more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input room relevance data into AI and have the AI ​​adjust the order of analysis.

[0089] The analysis unit can set the use of technical terms during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. For example, if the user has expertise in interior design, the analysis unit can use a lot of technical terms. Furthermore, if the user is a beginner, the analysis unit can avoid technical terms and use simpler explanations. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This allows for more appropriate analysis by adjusting the use of technical terms during analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terms.

[0090] The generation unit can estimate the user's emotion and set a layout generation method based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the layout generation method based on the estimated user's emotion. For example, the generation unit generates a spacious layout when the user is relaxed. The generation unit can also generate a simple and functional layout when the user is stressed. The generation unit can also generate a visually stimulating layout when the user is excited. This allows a more appropriate layout to be generated by adjusting the layout generation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the layout generation method based on the emotion.

[0091] The generation unit can set the level of detail of the generation based on the importance of the rooms when generating a layout. The generation unit, for example, adjusts the level of detail of the generation based on the importance of the rooms when generating a layout. For example, the generation unit generates a detailed layout for the living room. The generation unit can also generate a medium level of detail for the bedroom layout. The generation unit can also generate a simplified layout for the closet. In this way, by adjusting the level of detail of the generation based on the importance of the rooms, a more appropriate layout can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input room importance data to AI and cause the AI ​​to adjust the level of detail of the generation.

[0092] The generation unit can apply an appropriate generation algorithm depending on the room category when generating a layout. For example, the generation unit applies different generation algorithms depending on the room category when generating a layout. For example, the generation unit applies a generation algorithm that emphasizes cooking utensils and storage space to a kitchen layout. The generation unit can also apply a generation algorithm that emphasizes furniture placement and traffic flow to a living room layout. The generation unit can also apply a generation algorithm that emphasizes bed placement and lighting to a bedroom layout. In this way, by applying different generation algorithms depending on the room category, a more appropriate layout can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input room category data into AI and cause the AI ​​to apply an appropriate generation algorithm.

[0093] The generation unit can improve the accuracy of generation based on the user's past generation results when generating a layout. For example, the generation unit improves the accuracy of generation by referring to the user's past generation results when generating a layout. For example, the generation unit generates a layout by referring to layouts that the user has previously preferred. The generation unit can also adjust the generation algorithm based on the user's past feedback. The generation unit can also store the user's past generation results in a database and use them for the next generation. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into AI and have the AI ​​improve the accuracy of generation.

[0094] The generation unit can estimate the user's emotion and set the length of the layout based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the layout based on the estimated user's emotion. For example, the generation unit generates a detailed layout when the user is relaxed. The generation unit can also generate a simplified layout when the user is stressed. The generation unit can also generate a visually stimulating layout when the user is excited. This allows for the generation of a more appropriate layout by adjusting the length of the layout according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the length of the layout based on the emotion.

[0095] The generation unit can set generation priorities based on when the rooms were photographed when generating a layout. For example, the generation unit determines generation priorities based on when the rooms were photographed when generating a layout. For example, the generation unit prioritizes the use of recently photographed photos in layout generation. The generation unit can also analyze photos taken by season and propose a layout according to the season. The generation unit can also prioritize the use of photos taken during a specific event (such as moving or redecorating) in layout generation. In this way, by determining generation priorities based on when the rooms were photographed, a more appropriate layout can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on when the rooms were photographed into AI and have the AI ​​determine the generation priorities.

[0096] The generation unit can set the generation order based on the relevance of rooms when generating a layout. The generation unit, for example, adjusts the generation order based on the relevance of rooms when generating a layout. For example, the generation unit generates a layout taking into account the relevance between the living room and the dining room. The generation unit can also generate a layout taking into account the relevance between the bedroom and the closet. The generation unit can also generate a layout taking into account the relevance between the kitchen and the pantry. In this way, by adjusting the generation order based on the relevance of rooms, a more appropriate layout can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input room relevance data into AI and cause the AI ​​to adjust the generation order.

[0097] The generation unit can set the use of technical terminology in the generation according to the user's level of expertise when generating a layout. For example, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise when generating a layout. For example, if the user has expertise in interior design, the generation unit can use a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can avoid technical terminology and use simpler language to explain things. The generation unit can also adjust the level of detail of the generated results according to the user's level of expertise. This allows for the generation of a more appropriate layout by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0098] The suggestion unit can estimate the user's emotion and set a method for expressing suggestions based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the method for expressing suggestions based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is stressed. The suggestion unit can also provide visually stimulating suggestions when the user is excited. This allows for more appropriate suggestions to be made by adjusting the method for expressing suggestions according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input user's emotion data into the generation AI and cause the generation AI to adjust the method for expressing suggestions based on the emotion.

[0099] The suggestion unit can set the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes detailed suggestions for expensive furniture and interior items. The suggestion unit can also make suggestions with a medium level of detail for furniture and interior items used daily. The suggestion unit can also make simplified suggestions for decorative items and accessories. In this way, adjusting the level of detail of the suggestion based on the importance of the product allows for more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input product importance data into AI and cause the AI ​​to adjust the level of detail of the suggestion.

[0100] The suggestion unit can apply an appropriate suggestion algorithm depending on the product category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the product category when making a suggestion. For example, the suggestion unit applies a suggestion algorithm that emphasizes placement and usability to large furniture such as sofas and beds. The suggestion unit can also apply a suggestion algorithm that emphasizes color and design to interior items such as lighting and curtains. The suggestion unit can also apply a suggestion algorithm that emphasizes trends and seasonal feel to decorative items and accessories. In this way, by applying different suggestion algorithms depending on the product category, more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product category data into AI and cause the AI ​​to apply an appropriate suggestion algorithm.

[0101] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit makes a suggestion by referring to suggestions that the user liked in the past. The suggestion unit can also adjust the suggestion algorithm based on the user's past feedback. The suggestion unit can also store the user's past suggestion results in a database and use them for the next suggestion. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0102] The suggestion unit can estimate the user's emotion and set the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is stressed. The suggestion unit can also provide visually stimulating suggestions when the user is excited. This allows for more appropriate suggestions to be made by adjusting the length of the suggestion based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion based on the emotion.

[0103] The suggestion unit can set a priority of suggestions based on the time of product submission when making suggestions. The suggestion unit, for example, determines the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit prioritizes new products and products on sale. The suggestion unit can also prioritize seasonal products and limited-edition products. The suggestion unit can also prioritize products in which the user has shown interest in the past. This allows more appropriate suggestions to be made by determining the priority of suggestions based on the time of product submission. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product submission time data into AI and have the AI ​​determine the priority of suggestions.

[0104] The suggestion unit can set the order of suggestions based on the relevance of products when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit suggests highly related products together, such as sofas and cushions. The suggestion unit can also suggest highly related products together, such as beds and bedding. The suggestion unit can also suggest highly related products together, such as dining tables and chairs. In this way, adjusting the order of suggestions based on the relevance of products allows for more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input product relevance data into AI and cause the AI ​​to adjust the order of suggestions.

[0105] The suggestion unit may set the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has expertise in interior design, the suggestion unit may use a lot of technical terminology. Furthermore, if the user is a beginner, the suggestion unit may avoid technical terminology and use simpler language. Furthermore, the suggestion unit may adjust the level of detail of the proposal content according to the user's level of expertise. This allows for more appropriate suggestions by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and suggestion 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 is realized by the reception device 38 of the smart device 14 and allows a user to upload photos of their room. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos to determine the size and shape of the room and the layout of existing furniture. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal layout based on the analyzed information. The suggestion unit is realized, for example, by the output device 40 of the smart device 14 and makes purchasing suggestions for furniture and interior items based on the generated layout. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and suggestion 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 is realized by the microphone 238 of the smart glasses 214 and allows the user to upload photos of their room. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos to determine the size and shape of the room and the layout of existing furniture. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal layout based on the analyzed information. The suggestion unit is realized, for example, by the speaker 240 of the smart glasses 214 and makes purchasing suggestions for furniture and interior items based on the generated layout. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and suggestion 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 is realized by the microphone 238 of the headset-type terminal 314 and allows the user to upload photos of the room. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos to determine the size and shape of the room and the arrangement of existing furniture. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal layout based on the analyzed information. The suggestion unit is realized, for example, by the display 343 of the headset-type terminal 314 and makes purchasing suggestions for furniture and interior items based on the generated layout. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and allows the user to upload photos of the room. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos to determine the size and shape of the room and the layout of existing furniture. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal layout based on the analyzed information. The suggestion unit is realized, for example, by the speaker 240 of the robot 414 and makes purchasing suggestions for furniture and interior items based on the generated layout.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can make suggestions immediately. Also, if the user is feeling stressed, the suggestion unit can postpone making suggestions. Furthermore, if the user is excited, the suggestion unit can make suggestions quickly. This allows for more effective suggestions by adjusting the timing of suggestions according to the user's emotions.

[0108] The generation unit can also estimate the user's emotions and adjust the colors of the layout based on the estimated emotions. For example, if the user is relaxed, the generation unit can suggest a layout with calm colors. If the user is stressed, the generation unit can suggest simple and clean colors. Furthermore, if the user is excited, the generation unit can suggest vivid and lively colors. This makes it possible to adjust the colors according to the user's emotions.

[0109] The analysis unit can also estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is feeling stressed, the analysis unit can also perform a simplified analysis. Furthermore, if the user is excited, the analysis unit can also perform a quick analysis. This makes it possible to adjust the level of detail of the analysis according to the user's emotions.

[0110] The suggestion unit can also estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide concise suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. This makes it possible to adjust the way suggestions are expressed according to the user's emotions.

[0111] The generation unit can also estimate the user's emotion and adjust the length of the layout based on the estimated emotion. For example, if the user is relaxed, the generation unit can generate a detailed layout. If the user is stressed, the generation unit can generate a simplified layout. Furthermore, if the user is excited, the generation unit can generate a visually stimulating layout. This makes it possible to adjust the length of the layout according to the user's emotion.

[0112] The suggestion unit can also adjust the price range of the suggested products based on the user's past purchase history. For example, if the user has previously purchased expensive furniture, the suggestion unit can suggest products in a higher price range. Also, if the user has previously purchased products in a lower price range, the suggestion unit can also suggest products in a lower price range. Furthermore, if the user has a preference for a particular brand, products from that brand can be preferentially suggested. This makes it possible to make suggestions based on the user's past purchase history.

[0113] The analysis unit can also adjust the analysis algorithm based on the user's past feedback. For example, if the user has previously preferred detailed analysis, the analysis unit can perform a detailed analysis. Also, if the user has previously preferred simplified analysis, the analysis unit can perform a simplified analysis. Furthermore, if the user has previously preferred a specific analysis method, that method can be used preferentially. This makes it possible to perform analysis based on the user's past feedback.

[0114] The generation unit can also adjust the generation algorithm based on the user's past layout generation results. For example, the generation unit can refer to layouts that the user has previously preferred. The generation unit can also take into account layouts that the user has previously avoided. Furthermore, if the user has a preference for a particular design style, the generation unit can prioritize the use of that style. This makes it possible to generate layouts based on the user's past generation results.

[0115] The suggestion unit can also adjust the categories of products it suggests based on the user's current projects and areas of interest. For example, if the user is planning to redecorate their living room, the suggestion unit can prioritize and suggest products related to living rooms. Also, if the user is planning to remodel their kitchen, the suggestion unit can suggest products related to kitchens. Furthermore, if the user is interested in a particular design style, the suggestion unit can suggest products related to that style. This allows suggestions to be made based on the user's current projects and areas of interest.

[0116] The analysis unit can also set priorities for analysis based on when the room was photographed. For example, it can prioritize analysis of photos taken recently. It can also analyze photos taken by season and suggest layouts that suit the season. It can also prioritize analysis of photos taken during specific events (such as moving or redecorating). This makes it possible to set priorities for analysis based on when the room was photographed.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit uploads a photo of the user's room. The photo of the user's room may be in, but is not limited to, a JPEG format or a PNG format. The reception unit, for example, prompts the user to take a photo that captures the entire room and upload it to the app. Step 2: The analysis unit analyzes the photo uploaded by the reception unit to determine the size and shape of the room and the layout of existing furniture. For example, the analysis unit uses image recognition technology to analyze the size and shape of the room and the layout of existing furniture. For example, the analysis unit uses deep learning to detect objects in the image and identify their respective positions and sizes. The analysis unit can also use computer vision algorithms to analyze the shape of the room and the layout of furniture. Step 3: The generation unit generates an optimal layout based on the information obtained by the analysis unit. For example, the generation unit uses AI to generate an optimal layout based on the size and shape of the room and the layout of existing furniture. For example, the generation unit uses a genetic algorithm to generate an optimal furniture layout pattern. The generation unit can also use simulated annealing to generate a layout that takes into account the securing of traffic flow lines and design consistency. Step 4: The suggestion unit makes purchasing suggestions for furniture and interior items based on the layout generated by the generation unit. The suggestion unit, for example, analyzes the user's past purchase history and preference data to select furniture and interior items to suggest. The suggestion unit, for example, uses a recommendation system to suggest furniture and interior items that match the user's preferences. The suggestion unit can also use collaborative filtering to make suggestions based on the purchase history of other users.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for uploading photos of users' rooms; an analysis unit that analyzes the photos uploaded by the reception unit and determines the size and shape of the room and the layout of existing furniture; a generation unit that generates a layout based on the information grasped by the analysis unit; a proposal unit that makes a purchase proposal for furniture and interior items based on the layout generated by the generation unit. A system characterized by:

2. The generation unit Adjust the layout based on user requests 2. The system of claim 1.

3. The proposal unit Select furniture and interior design suggestions based on the user's past purchase history and preferences 2. The system of claim 1.

4. The analysis unit Image recognition technology is used to analyze the size and shape of the room and the layout of existing furniture.

2. The system of claim 1.

5. The generation unit Generate layouts based on user requests based on color and design trends 2. The system of claim 1.

6. The proposal unit Complete furniture and decor purchase suggestions within the app 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and set the timing of photo uploads based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past photo upload history and select the appropriate upload method 2. The system of claim 1.

Images