system

Abstract

The system according to the embodiment aims to appropriately propose a room layout based on the user's image. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit receives a user's image. The analysis unit analyzes real-world data of a target room based on the image received by the reception unit. The generation unit generates a layout based on the data analyzed by the analysis unit. The proposal unit proposes products based on the layout generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to appropriately propose a room layout based on the user's image, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately propose a room layout based on the user's image. [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 receives a user's image. The analysis unit analyzes real-world data of a target room based on the image received by the reception unit. The generation unit generates a layout based on the data analyzed by the analysis unit. The proposal unit proposes products based on the layout generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately propose a room layout based on the user's image. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A layout generation system according to an embodiment of the present invention generates and proposes layouts suited to the actual environment by allowing users to input their own image via chat and input real-world environmental data (e.g., data captured by LiDAR, images, and videos) of the target room. This layout generation system incorporates furniture and home appliances sold by manufacturers into the generated layout. It also includes a function for proposing product and shelf layouts based on behavioral economics as an extension for B2B services. For example, a user may input specific requests via chat, such as "I want to place a sofa in the living room" or "I want more storage space in the kitchen." This information is input into the generation AI. Next, the user inputs real-world environmental data for the target room. For example, the user may provide data captured by LiDAR, room images, and videos to the generation AI. The generation AI analyzes this data to determine the room's dimensions, shape, and the layout of existing furniture. The generation AI generates an optimal layout based on the user's image and real-world data. For example, when placing a sofa in the living room, the system proposes the optimal position based on the room's dimensions and shape. When adding storage space to the kitchen, the system proposes an efficient storage space layout, taking into account the layout of existing furniture and home appliances. The generated layout includes furniture and home appliances sold by the manufacturer. For example, sofas and storage furniture from a specific manufacturer are suggested. This allows users to purchase specific products. Furthermore, as an extension for B2B, the system also has a function to suggest product and shelf layout methods based on behavioral economics. For example, to optimize store layouts, the system analyzes customer behavior patterns and suggests shelf layouts. This is expected to increase store sales. This system allows users to easily generate and suggest room layouts that match their image. Furthermore, the extension for B2B can optimize store layouts and increase sales. This allows the layout generation system to generate optimal layouts based on the user's image and real-world data, and suggest specific products.

[0029] A layout generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit receives a user's image. The user's image may include, but is not limited to, text, images, and audio. The reception unit may receive, for example, a user's specific request via chat, such as "I want to place a sofa in my living room" or "I want more storage space in my kitchen." The analysis unit analyzes real-world data of the target room based on the image received by the reception unit. The real-world data may include, but is not limited to, LiDAR data, image data, and temperature data. The analysis unit analyzes, for example, data captured by LiDAR, room images, and videos to determine the dimensions and shape of the room, the layout of existing furniture, and the like. The generation unit generates a layout based on the data analyzed by the analysis unit. The generation unit generates an optimal layout based on, for example, the user's image and the real-world data. The generation unit generates an optimal layout based on the user's image and the real-world data using a generation AI. The generation AI generates a layout using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests products based on the layout generated by the generation unit. The suggestion unit suggests, for example, furniture and home appliances sold by manufacturers. The suggestion unit can also suggest products and shelf layout methods based on behavioral economics. For example, to optimize the store layout, the suggestion unit analyzes customer behavior patterns and suggests shelf layouts. As a result, the layout generation system according to the embodiment can generate an optimal layout based on the user's image and real-world data, and suggest specific products.

[0030] The suggestion unit can suggest furniture or home appliances. The suggestion unit suggests, for example, furniture or home appliances sold by a manufacturer. Suggested furniture and home appliances include, but are not limited to, sofas, televisions, refrigerators, etc. The suggestion unit suggests optimal furniture or home appliances based on the user's image and real-world data. For example, when placing a sofa in a living room, the suggestion unit can suggest a sofa from a specific manufacturer. Also, when adding storage space in a kitchen, the suggestion unit can suggest storage furniture from a specific manufacturer. This makes it easier for the user to purchase specific products by suggesting them. 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 suggest products using an AI model that suggests furniture or home appliances from a specific manufacturer based on the generated layout.

[0031] The analysis unit can analyze data read by LiDAR or images and videos of the room. The analysis unit analyzes, for example, data read by LiDAR. LiDAR data includes, for example, but is not limited to, the type of LiDAR sensor and the data resolution. The analysis unit can use the LiDAR data to determine the dimensions and shape of the room. The analysis unit can also analyze images and videos of the room. The images and videos of the room include, for example, but are not limited to, the type of camera and the shooting angle. The analysis unit can use the images and videos of the room to determine the existing furniture arrangement and room layout. This allows for detailed analysis of the real environment data to generate a more accurate layout. 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 analyze the data using an AI model that inputs LiDAR data, room images, and videos, and outputs the dimensions and shape of the room and the existing furniture arrangement.

[0032] The generation unit can generate a layout based on a user's image and real-world data. The generation unit generates an optimal layout based on, for example, the user's image and real-world data. The user's image includes, for example, but is not limited to, text, images, and audio. The real-world data includes, for example, but is not limited to, LiDAR data, image data, and temperature data. The generation unit uses a generation AI to generate an optimal layout based on the user's image and real-world data. The generation AI generates a layout using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes the user's image and real-world data as input and outputs an optimal layout. For example, when placing a sofa in a living room, the generation AI suggests the optimal position based on the dimensions and shape of the room. Also, when adding storage space in a kitchen, the generation AI suggests an efficient storage space arrangement taking into account the arrangement of existing furniture and appliances. This makes it possible to generate a layout that takes the user's image and real-world data into consideration. 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 generator can generate a layout using an AI model that takes a user's image and real-world data as input and outputs the optimal layout.

[0033] The suggestion unit can propose a product or shelf layout method based on behavioral economics. The suggestion unit proposes, for example, a product or shelf layout method based on behavioral economics. Examples of behavioral economics include, but are not limited to, the anchoring effect and social proof. The suggestion unit makes proposals to optimize store layouts using theories of behavioral economics. For example, the suggestion unit can analyze customer behavior patterns and propose shelf layouts. Examples of customer behavior patterns include, but are not limited to, purchase history and in-store movement paths. The suggestion unit analyzes customer behavior patterns and proposes optimal shelf layouts. This is expected to increase store sales through proposals based on behavioral economics. 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 can propose shelf layouts using an AI model that inputs customer behavior patterns and outputs optimal shelf layouts.

[0034] The analysis unit can analyze customer behavior patterns. The analysis unit analyzes, for example, customer behavior patterns. Customer behavior patterns include, but are not limited to, purchase history, paths taken within a store, and the like. The analysis unit can analyze customer behavior patterns and grasp customer behavior characteristics. For example, it analyzes the paths customers take within a store and the products they purchase. By analyzing customer behavior patterns, more effective product placement becomes possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze behavior patterns using an AI model that inputs customer behavior patterns and outputs customer behavior characteristics.

[0035] The reception unit can analyze the user's past image input history and select the optimal reception method. The reception unit, for example, analyzes the user's past image input history and selects the optimal reception method. The user's past image input history includes, for example, past input data, frequency, etc., but is not limited to these examples. The reception unit provides the optimal reception method based on the past input history. For example, the reception unit can automatically display images that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest images to be used in a specific time period based on the user's past input history. This improves user convenience by providing the optimal reception method based on the past history. Some or all of the above-described processing in 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 past input data to a generation AI and have the generation AI execute the optimal reception method.

[0036] The reception unit may filter images based on the user's current projects or areas of interest when receiving the images. For example, the reception unit may filter images based on the user's current projects or areas of interest when receiving the images. Examples of the user's current projects and areas of interest include, but are not limited to, survey results and past activity history. The reception unit identifies the user's projects and areas of interest and prioritizes receiving highly relevant images. For example, the reception unit may prioritize receiving images related to projects currently underway. The reception unit may also filter and receive highly relevant images based on the user's areas of interest. Furthermore, the reception unit may prioritize receiving images related to projects in which the user has previously shown interest. This allows for efficient image input by preferentially receiving images that correspond to the user's interests. 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 data on the user's projects and areas of interest into a generation AI and cause the generation AI to execute a process of filtering highly relevant images.

[0037] The reception unit may prioritize receiving highly relevant images based on the user's geographical location information when receiving images. For example, the reception unit may prioritize receiving highly relevant images in consideration of the user's geographical location information when receiving images. The user's geographical location information may include, but is not limited to, GPS data, IP address, etc. The reception unit filters highly relevant images based on the user's geographical location information. For example, the reception unit prioritizes receiving images related to the user's current location. The reception unit may also filter and receive highly relevant images based on the user's geographical location information. Furthermore, the reception unit may prioritize receiving images related to places the user has visited in the past. This enables suggestions tailored to the user's needs by receiving images based on the geographical location information. 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 may input the user's geographical location information to a generation AI and cause the generation AI to execute a process of filtering highly relevant images.

[0038] The reception unit may analyze the user's social media activity when receiving an image and receive related images. For example, the reception unit may analyze the user's social media activity when receiving an image and receive related images. The user's social media activity may include, but is not limited to, the content of posts and the number of likes. The reception unit may filter highly relevant images based on the user's social media activity. For example, the reception unit may preferentially receive images shared by the user on social media. The reception unit may also filter and receive highly relevant images based on the user's social media activity. Furthermore, the reception unit may preferentially receive images in which the user has shown interest on social media. This enables suggestions tailored to the user's interests by receiving images based on social media activity. 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 social media activity data into a generation AI and cause the generation AI to execute a process of filtering highly relevant images.

[0039] The analysis unit can optimize the analysis algorithm based on past analysis data during analysis. For example, the analysis unit optimizes the analysis algorithm by referring to past analysis data during analysis. Past analysis data includes, but is not limited to, past analysis results, data sets, etc. The analysis unit selects an optimal analysis algorithm based on past data. For example, the analysis unit selects an optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the analysis algorithm by referring to past analysis data. Furthermore, the analysis unit can analyze past analysis data and adjust parameters of the analysis algorithm. This improves the analysis accuracy by optimizing the analysis algorithm based on past data. 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 past analysis data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0040] The analysis unit can apply different analysis methods based on the room category during analysis. For example, the analysis unit applies different analysis methods depending on the room category during analysis. Room categories include, but are not limited to, living rooms, kitchens, and bedrooms. The analysis unit selects the optimal analysis method based on the room category. For example, for a living room, an analysis method that emphasizes furniture arrangement can be applied. For a kitchen, an analysis method that emphasizes storage space efficiency can be applied. Furthermore, for a bedroom, an analysis method that emphasizes comfort can be applied. This enables more appropriate analysis by applying an analysis method according to the room category. 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 category data into a generation AI and cause the generation AI to execute a process of selecting the optimal analysis method.

[0041] The analysis unit may perform the analysis based on the geographical distribution of rooms. For example, the analysis unit may perform the analysis while taking into account the geographical distribution of rooms. Examples of the geographical distribution of rooms include, but are not limited to, map data and location information. The analysis unit selects an optimal analysis method based on the geographical distribution of rooms. For example, the analysis unit may select an optimal analysis method based on the geographical distribution of rooms. The analysis result may also be adjusted taking into account the geographical distribution of rooms. Furthermore, parameters of the analysis algorithm may be adjusted based on the geographical distribution of rooms. This allows for more appropriate analysis results to be obtained through analysis based on the geographical distribution. 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 may input the geographical distribution data of rooms to a generation AI and cause the generation AI to execute a process of selecting an optimal analysis method.

[0042] The analysis unit can improve the accuracy of the analysis based on the related literature of the room during the analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the related literature of the room during the analysis. The related literature of the room includes, but is not limited to, academic papers, technical reports, etc. The analysis unit improves the accuracy of the analysis algorithm based on the related literature. For example, the analysis unit improves the accuracy of the analysis algorithm by referring to the related literature of the room. The analysis method can also be optimized based on the related literature of the room. Furthermore, the related literature of the room can be analyzed to improve the accuracy of the analysis results. As a result, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the related literature data of the room into the generation AI and cause the generation AI to improve the accuracy of the analysis algorithm.

[0043] The generation unit can optimize the generation algorithm based on past generation data when generating a layout. For example, the generation unit optimizes the generation algorithm by referring to past generation data when generating a layout. Past generation data includes, but is not limited to, past layouts, data sets, etc. The generation unit selects an optimal generation algorithm based on past data. For example, the generation unit selects an optimal generation algorithm based on past generation data. The generation unit can also improve the accuracy of the generation algorithm by referring to past generation data. Furthermore, the generation unit can analyze past generation data and adjust parameters of the generation algorithm. This improves generation accuracy by optimizing the generation algorithm based on past data. 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 may input past generation data into a generation AI and cause the generation AI to optimize the generation algorithm.

[0044] The generation unit can apply different generation methods based on the room category when generating a layout. For example, the generation unit applies different generation methods depending on the room category when generating a layout. Room categories include, but are not limited to, living rooms, kitchens, and bedrooms. The generation unit selects an optimal generation method based on the room category. For example, for a living room, a generation method that emphasizes furniture arrangement can be applied. For a kitchen, a generation method that emphasizes storage space efficiency can be applied. Furthermore, for a bedroom, a generation method that emphasizes comfort can be applied. This enables more appropriate layout generation by applying a generation method according to the room category. 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 may input room category data into the generation AI and cause the generation AI to execute a process of selecting an optimal generation method.

[0045] The generation unit may generate a layout based on the geographical distribution of rooms. For example, the generation unit may generate a layout while taking into account the geographical distribution of rooms. Examples of the geographical distribution of rooms include, but are not limited to, map data and location information. The generation unit selects an optimal generation method based on the geographical distribution of rooms. For example, the generation unit may select an optimal generation method based on the geographical distribution of rooms. Parameters of a generation algorithm may also be adjusted taking into account the geographical distribution of rooms. Furthermore, the generation result may be adjusted based on the geographical distribution of rooms. This allows a more appropriate layout to be obtained by generating a layout based on the geographical distribution. 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 may input geographical distribution data of rooms into a generation AI and cause the generation AI to execute a process of selecting an optimal generation method.

[0046] The generation unit can improve the accuracy of the generation based on room-related literature when generating a layout. For example, the generation unit improves the accuracy of the generation by referring to room-related literature when generating a layout. Room-related literature includes, but is not limited to, academic papers, technical reports, etc. The generation unit improves the accuracy of the generation algorithm based on the related literature. For example, the accuracy of the generation algorithm can be improved by referring to room-related literature. The generation method can also be optimized based on the room-related literature. Furthermore, the accuracy of the generation result can be improved by analyzing room-related literature. As a result, the accuracy of the generation can be improved by referring to the related literature. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input room-related literature data into the generation AI and cause the generation AI to improve the accuracy of the generation algorithm.

[0047] The suggestion unit can change 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. Examples of the importance of the product include, but are not limited to, sales data and customer reviews. The suggestion unit selects the optimal suggestion method based on the importance of the product. For example, for a product with high importance, the suggestion unit can make a suggestion including detailed information. For a product with low importance, the suggestion unit can make a suggestion including concise information. Furthermore, the level of detail of the suggestion can be adjusted according to the importance of the product. This improves user convenience by making suggestions according to the importance of the product. 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 importance data to the generation AI and cause the generation AI to execute a process of adjusting the level of detail of the suggestion.

[0048] The suggestion unit can apply different suggestion algorithms based on the product category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms based on the product category when making a suggestion. Product categories include, but are not limited to, furniture, home appliances, and decorative items. The suggestion unit selects an optimal suggestion algorithm based on the product category. For example, in the case of furniture, suggestions are made that emphasize placement efficiency. In addition, in the case of home appliances, suggestions can be made that take frequency of use into account. Furthermore, in the case of decorative items, suggestions can be made that emphasize design consistency. This enables more appropriate suggestions to be made by making suggestions based on the product category. 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 may input product category data into a generation AI and cause the generation AI to execute a process of selecting an optimal suggestion algorithm.

[0049] The suggestion unit can determine the order of suggestions based on the submission dates of the products when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the submission dates of the products when making suggestions. Examples of product submission dates include, but are not limited to, the sales start date, the campaign period, etc. The suggestion unit selects the optimal suggestion order based on the submission dates of the products. For example, products whose submission dates are close in time may be suggested first. Products whose submission dates are further away may also be suggested later. Furthermore, the priority of suggestions can be adjusted depending on the submission dates of the products. This improves user convenience by making suggestions based on the submission dates of the products. 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 submission date data into a generation AI and cause the generation AI to execute a process of determining the priority of suggestions.

[0050] The suggestion unit can change the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the products when making suggestions. Product relevance includes, but is not limited to, category matches, past purchase history, etc. The suggestion unit selects an optimal suggestion order based on the relevance of the products. For example, highly relevant products are suggested first. Also, less relevant products can be suggested later. Furthermore, the suggestion order can be adjusted according to the relevance of the products. This improves user convenience by making suggestions based on the relevance of the products. 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 relevance data to a generation AI and cause the generation AI to execute a process of determining the order of suggestions.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can analyze the user's past purchase history and make optimal product suggestions. For example, it can prioritize relevant products based on data on furniture and home appliances purchased by the user in the past. Also, if the user has a preference for a particular brand or style, it can make suggestions taking that information into account. Furthermore, it can also make product suggestions according to the season or event based on the user's purchase history. This makes it possible to make more personalized suggestions by utilizing the user's past purchase history.

[0053] The analysis unit can analyze the lighting conditions in a room and propose an optimal layout. For example, it proposes furniture placement taking into account the amount of natural light entering the room and the placement of the lighting. It can also propose optimal furniture colors and materials based on the color temperature and brightness of the lighting. It can also propose a layout that improves visual comfort depending on the lighting conditions in the room. This makes it possible to propose a layout that takes lighting conditions into account.

[0054] The suggestion unit can analyze the user's lifestyle data and make optimal product suggestions. For example, it can suggest highly relevant products based on data such as the user's exercise habits, diet, and hobbies. It can also suggest products useful for health management and relaxation based on the user's lifestyle. Furthermore, it can utilize the user's lifestyle data to make product suggestions according to the season or event. This makes it possible to make personalized suggestions based on the user's lifestyle.

[0055] The reception unit can analyze the user's device usage history and provide the optimal interface. For example, it can suggest the optimal interface based on data on devices and applications used by the user in the past. Also, if the user frequently uses a specific device, it can provide an interface optimized for that device. Furthermore, it can predict and suggest the interface to be used during a specific time period based on the user's device usage history. In this way, by utilizing the user's device usage history, it is possible to provide a more convenient interface.

[0056] The suggestion unit can analyze a user's social media activity and make optimal product suggestions. For example, it can suggest highly relevant products based on the posts and "likes" the user has shared on social media. It can also make suggestions taking into account information about the brands and influencers the user follows. It can also suggest trending and popular products based on the user's social media activity. This makes it possible to make more personalized product suggestions by utilizing the user's social media activity.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives an image of the user. The image of the user may include, but is not limited to, text, images, and voice. For example, the reception unit receives a specific request input by the user via chat, such as "I want to put a sofa in my living room" or "I want more storage space in my kitchen." Step 2: The analysis unit analyzes the real-world data of the target room based on the image received by the reception unit. The real-world data may include, but is not limited to, LiDAR data, image data, and temperature data. The analysis unit analyzes, for example, data read by LiDAR, images of the room, and videos to determine the dimensions and shape of the room, the layout of existing furniture, and so on. Step 3: The generation unit generates a layout based on the data analyzed by the analysis unit. The generation unit generates an optimal layout based on, for example, the user's image and real-world data. The generation unit uses a generation AI to generate an optimal layout based on the user's image and real-world data. The generation AI generates a layout using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The suggestion unit suggests products based on the layout generated by the generation unit. For example, the suggestion unit suggests furniture and home appliances sold by manufacturers. The suggestion unit can also suggest product and shelf layout methods based on behavioral economics. For example, to optimize the store layout, the suggestion unit analyzes customer behavior patterns and suggests shelf layouts.

[0059] (Example 2) A layout generation system according to an embodiment of the present invention generates and proposes layouts suited to the actual environment by allowing users to input their own image via chat and input real-world environmental data (e.g., data captured by LiDAR, images, and videos) of the target room. This layout generation system incorporates furniture and home appliances sold by manufacturers into the generated layout. It also includes a function for proposing product and shelf layouts based on behavioral economics as an extension for B2B services. For example, a user may input specific requests via chat, such as "I want to place a sofa in the living room" or "I want more storage space in the kitchen." This information is input into the generation AI. Next, the user inputs real-world environmental data for the target room. For example, the user may provide data captured by LiDAR, room images, and videos to the generation AI. The generation AI analyzes this data to determine the room's dimensions, shape, and the layout of existing furniture. The generation AI generates an optimal layout based on the user's image and real-world data. For example, when placing a sofa in the living room, the system proposes the optimal position based on the room's dimensions and shape. When adding storage space to the kitchen, the system proposes an efficient storage space layout, taking into account the layout of existing furniture and home appliances. The generated layout includes furniture and home appliances sold by the manufacturer. For example, sofas and storage furniture from a specific manufacturer are suggested. This allows users to purchase specific products. Furthermore, as an extension for B2B, the system also has a function to suggest product and shelf layout methods based on behavioral economics. For example, to optimize store layouts, the system analyzes customer behavior patterns and suggests shelf layouts. This is expected to increase store sales. This system allows users to easily generate and suggest room layouts that match their image. Furthermore, the extension for B2B can optimize store layouts and increase sales. This allows the layout generation system to generate optimal layouts based on the user's image and real-world data, and suggest specific products.

[0060] A layout generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit receives a user's image. The user's image may include, but is not limited to, text, images, and audio. The reception unit may receive, for example, a user's specific request via chat, such as "I want to place a sofa in my living room" or "I want more storage space in my kitchen." The analysis unit analyzes real-world data of the target room based on the image received by the reception unit. The real-world data may include, but is not limited to, LiDAR data, image data, and temperature data. The analysis unit analyzes, for example, data captured by LiDAR, room images, and videos to determine the dimensions and shape of the room, the layout of existing furniture, and the like. The generation unit generates a layout based on the data analyzed by the analysis unit. The generation unit generates an optimal layout based on, for example, the user's image and the real-world data. The generation unit generates an optimal layout based on the user's image and the real-world data using a generation AI. The generation AI generates a layout using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests products based on the layout generated by the generation unit. The suggestion unit suggests, for example, furniture and home appliances sold by manufacturers. The suggestion unit can also suggest products and shelf layout methods based on behavioral economics. For example, to optimize the store layout, the suggestion unit analyzes customer behavior patterns and suggests shelf layouts. As a result, the layout generation system according to the embodiment can generate an optimal layout based on the user's image and real-world data, and suggest specific products.

[0061] The suggestion unit can suggest furniture or home appliances. The suggestion unit suggests, for example, furniture or home appliances sold by a manufacturer. Suggested furniture and home appliances include, but are not limited to, sofas, televisions, refrigerators, etc. The suggestion unit suggests optimal furniture or home appliances based on the user's image and real-world data. For example, when placing a sofa in a living room, the suggestion unit can suggest a sofa from a specific manufacturer. Also, when adding storage space in a kitchen, the suggestion unit can suggest storage furniture from a specific manufacturer. This makes it easier for the user to purchase specific products by suggesting them. 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 suggest products using an AI model that suggests furniture or home appliances from a specific manufacturer based on the generated layout.

[0062] The analysis unit can analyze data read by LiDAR or images and videos of the room. The analysis unit analyzes, for example, data read by LiDAR. LiDAR data includes, for example, but is not limited to, the type of LiDAR sensor and the data resolution. The analysis unit can use the LiDAR data to determine the dimensions and shape of the room. The analysis unit can also analyze images and videos of the room. The images and videos of the room include, for example, but are not limited to, the type of camera and the shooting angle. The analysis unit can use the images and videos of the room to determine the existing furniture arrangement and room layout. This allows for detailed analysis of the real environment data to generate a more accurate layout. 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 analyze the data using an AI model that inputs LiDAR data, room images, and videos, and outputs the dimensions and shape of the room and the existing furniture arrangement.

[0063] The generation unit can generate a layout based on a user's image and real-world data. The generation unit generates an optimal layout based on, for example, the user's image and real-world data. The user's image includes, for example, but is not limited to, text, images, and audio. The real-world data includes, for example, but is not limited to, LiDAR data, image data, and temperature data. The generation unit uses a generation AI to generate an optimal layout based on the user's image and real-world data. The generation AI generates a layout using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes the user's image and real-world data as input and outputs an optimal layout. For example, when placing a sofa in a living room, the generation AI suggests the optimal position based on the dimensions and shape of the room. Also, when adding storage space in a kitchen, the generation AI suggests an efficient storage space arrangement taking into account the arrangement of existing furniture and appliances. This makes it possible to generate a layout that takes the user's image and real-world data into consideration. 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 generator can generate a layout using an AI model that takes a user's image and real-world data as input and outputs the optimal layout.

[0064] The suggestion unit can propose a product or shelf layout method based on behavioral economics. The suggestion unit proposes, for example, a product or shelf layout method based on behavioral economics. Examples of behavioral economics include, but are not limited to, the anchoring effect and social proof. The suggestion unit makes proposals to optimize store layouts using theories of behavioral economics. For example, the suggestion unit can analyze customer behavior patterns and propose shelf layouts. Examples of customer behavior patterns include, but are not limited to, purchase history and in-store movement paths. The suggestion unit analyzes customer behavior patterns and proposes optimal shelf layouts. This is expected to increase store sales through proposals based on behavioral economics. 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 can propose shelf layouts using an AI model that inputs customer behavior patterns and outputs optimal shelf layouts.

[0065] The analysis unit can analyze customer behavior patterns. The analysis unit analyzes, for example, customer behavior patterns. Customer behavior patterns include, but are not limited to, purchase history, paths taken within a store, and the like. The analysis unit can analyze customer behavior patterns and grasp customer behavior characteristics. For example, it analyzes the paths customers take within a store and the products they purchase. By analyzing customer behavior patterns, more effective product placement becomes possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze behavior patterns using an AI model that inputs customer behavior patterns and outputs customer behavior characteristics.

[0066] The reception unit can estimate the user's emotion and change the image reception method based on the estimated user emotion. For example, the reception unit estimates the user's emotion and adjusts the image reception method based on the estimated user emotion. User emotions include, but are not limited to, stress, relaxation, and hurry. The reception unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, if the user is stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow quick image input. This improves user satisfaction by providing a reception method that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI or without AI. For example, the reception unit can input the user's facial expression data and voice data into the generation AI and have the generation AI estimate emotions.

[0067] The reception unit can analyze the user's past image input history and select the optimal reception method. The reception unit, for example, analyzes the user's past image input history and selects the optimal reception method. The user's past image input history includes, for example, past input data, frequency, etc., but is not limited to these examples. The reception unit provides the optimal reception method based on the past input history. For example, the reception unit can automatically display images that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest images to be used in a specific time period based on the user's past input history. This improves user convenience by providing the optimal reception method based on the past history. Some or all of the above-described processing in 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 past input data to a generation AI and have the generation AI execute the optimal reception method.

[0068] The reception unit may filter images based on the user's current projects or areas of interest when receiving the images. For example, the reception unit may filter images based on the user's current projects or areas of interest when receiving the images. Examples of the user's current projects and areas of interest include, but are not limited to, survey results and past activity history. The reception unit identifies the user's projects and areas of interest and prioritizes receiving highly relevant images. For example, the reception unit may prioritize receiving images related to projects currently underway. The reception unit may also filter and receive highly relevant images based on the user's areas of interest. Furthermore, the reception unit may prioritize receiving images related to projects in which the user has previously shown interest. This allows for efficient image input by preferentially receiving images that correspond to the user's interests. 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 data on the user's projects and areas of interest into a generation AI and cause the generation AI to execute a process of filtering highly relevant images.

[0069] The reception unit can estimate the user's emotion and determine the order of images to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of images to be received based on the estimated user's emotion. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The reception unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, if the user is stressed, it can prioritize receiving images of high importance. Also, if the user is relaxed, it can prioritize receiving detailed images. Furthermore, if the user is in a hurry, it can prioritize receiving images that can be processed quickly. This allows for more appropriate image reception by setting priorities 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 can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data and voice data into the generation AI and have the generation AI estimate emotions.

[0070] The reception unit may prioritize receiving highly relevant images based on the user's geographical location information when receiving images. For example, the reception unit may prioritize receiving highly relevant images in consideration of the user's geographical location information when receiving images. The user's geographical location information may include, but is not limited to, GPS data, IP address, etc. The reception unit filters highly relevant images based on the user's geographical location information. For example, the reception unit prioritizes receiving images related to the user's current location. The reception unit may also filter and receive highly relevant images based on the user's geographical location information. Furthermore, the reception unit may prioritize receiving images related to places the user has visited in the past. This enables suggestions tailored to the user's needs by receiving images based on the geographical location information. 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 may input the user's geographical location information to a generation AI and cause the generation AI to execute a process of filtering highly relevant images.

[0071] The reception unit may analyze the user's social media activity when receiving an image and receive related images. For example, the reception unit may analyze the user's social media activity when receiving an image and receive related images. The user's social media activity may include, but is not limited to, the content of posts and the number of likes. The reception unit may filter highly relevant images based on the user's social media activity. For example, the reception unit may preferentially receive images shared by the user on social media. The reception unit may also filter and receive highly relevant images based on the user's social media activity. Furthermore, the reception unit may preferentially receive images in which the user has shown interest on social media. This enables suggestions tailored to the user's interests by receiving images based on social media activity. 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 social media activity data into a generation AI and cause the generation AI to execute a process of filtering highly relevant images.

[0072] The analysis unit can estimate the user's emotion and change the analysis method of the real-world data based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis method of the real-world data based on the estimated user's emotion. User emotions include, but are not limited to, stress, relaxation, and hurry. The analysis unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, if the user is stressed, a simple analysis method can be applied. If the user is relaxed, a detailed analysis method can be applied. Furthermore, if the user is hurrying, a method that can analyze quickly can be applied. This improves user satisfaction by providing an analysis method that corresponds 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 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data and voice data into the generation AI and have the generation AI estimate emotions.

[0073] The analysis unit can optimize the analysis algorithm based on past analysis data during analysis. For example, the analysis unit optimizes the analysis algorithm by referring to past analysis data during analysis. Past analysis data includes, but is not limited to, past analysis results, data sets, etc. The analysis unit selects an optimal analysis algorithm based on past data. For example, the analysis unit selects an optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the analysis algorithm by referring to past analysis data. Furthermore, the analysis unit can analyze past analysis data and adjust parameters of the analysis algorithm. This improves the analysis accuracy by optimizing the analysis algorithm based on past data. 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 past analysis data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0074] The analysis unit can apply different analysis methods based on the room category during analysis. For example, the analysis unit applies different analysis methods depending on the room category during analysis. Room categories include, but are not limited to, living rooms, kitchens, and bedrooms. The analysis unit selects the optimal analysis method based on the room category. For example, for a living room, an analysis method that emphasizes furniture arrangement can be applied. For a kitchen, an analysis method that emphasizes storage space efficiency can be applied. Furthermore, for a bedroom, an analysis method that emphasizes comfort can be applied. This enables more appropriate analysis by applying an analysis method according to the room category. 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 category data into a generation AI and cause the generation AI to execute a process of selecting the optimal analysis method.

[0075] The analysis unit can estimate the user's emotion and change the display method of the analysis results based on the estimated user emotion. For example, the analysis unit estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user emotion. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The analysis unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, if the user is stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is hurrying, a display method that focuses on the main points can be provided. This improves user satisfaction by providing a display method that corresponds 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data and voice data into the generation AI and have the generation AI estimate emotions.

[0076] The analysis unit may perform the analysis based on the geographical distribution of rooms. For example, the analysis unit may perform the analysis while taking into account the geographical distribution of rooms. Examples of the geographical distribution of rooms include, but are not limited to, map data and location information. The analysis unit selects an optimal analysis method based on the geographical distribution of rooms. For example, the analysis unit may select an optimal analysis method based on the geographical distribution of rooms. The analysis result may also be adjusted taking into account the geographical distribution of rooms. Furthermore, parameters of the analysis algorithm may be adjusted based on the geographical distribution of rooms. This allows for more appropriate analysis results to be obtained through analysis based on the geographical distribution. 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 may input the geographical distribution data of rooms to a generation AI and cause the generation AI to execute a process of selecting an optimal analysis method.

[0077] The analysis unit can improve the accuracy of the analysis based on the related literature of the room during the analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the related literature of the room during the analysis. The related literature of the room includes, but is not limited to, academic papers, technical reports, etc. The analysis unit improves the accuracy of the analysis algorithm based on the related literature. For example, the analysis unit improves the accuracy of the analysis algorithm by referring to the related literature of the room. The analysis method can also be optimized based on the related literature of the room. Furthermore, the related literature of the room can be analyzed to improve the accuracy of the analysis results. As a result, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the related literature data of the room into the generation AI and cause the generation AI to improve the accuracy of the analysis algorithm.

[0078] The generation unit can estimate the user's emotion and change the 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. User emotions include, but are not limited to, stress, relaxation, and hurry. The generation unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, if the user is stressed, a simple layout can be generated. Also, if the user is relaxed, a detailed layout can be generated. Furthermore, if the user is in a hurry, a layout that can be generated quickly can be provided. This improves user satisfaction by providing a layout generation method that corresponds 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 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 generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data and voice data into the generation AI and have the generation AI estimate emotions.

[0079] The generation unit can optimize the generation algorithm based on past generation data when generating a layout. For example, the generation unit optimizes the generation algorithm by referring to past generation data when generating a layout. Past generation data includes, but is not limited to, past layouts, data sets, etc. The generation unit selects an optimal generation algorithm based on past data. For example, the generation unit selects an optimal generation algorithm based on past generation data. The generation unit can also improve the accuracy of the generation algorithm by referring to past generation data. Furthermore, the generation unit can analyze past generation data and adjust parameters of the generation algorithm. This improves generation accuracy by optimizing the generation algorithm based on past data. 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 may input past generation data into a generation AI and cause the generation AI to optimize the generation algorithm.

[0080] The generation unit can apply different generation methods based on the room category when generating a layout. For example, the generation unit applies different generation methods depending on the room category when generating a layout. Room categories include, but are not limited to, living rooms, kitchens, and bedrooms. The generation unit selects an optimal generation method based on the room category. For example, for a living room, a generation method that emphasizes furniture arrangement can be applied. For a kitchen, a generation method that emphasizes storage space efficiency can be applied. Furthermore, for a bedroom, a generation method that emphasizes comfort can be applied. This enables more appropriate layout generation by applying a generation method according to the room category. 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 may input room category data into the generation AI and cause the generation AI to execute a process of selecting an optimal generation method.

[0081] The generation unit can estimate the user's emotion and determine the order of layouts to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and determines the priority of layouts to be generated based on the estimated user's emotion. User emotions include, but are not limited to, stress, relaxation, and hurry. The generation unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, if the user is stressed, layouts with high importance can be generated with priority. Furthermore, if the user is relaxed, layouts that can be generated quickly can be generated with priority. By setting priorities according to the user's emotion, more appropriate layouts can be generated. The 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 generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data and voice data into the generation AI and have the generation AI estimate emotions.

[0082] The generation unit may generate a layout based on the geographical distribution of rooms. For example, the generation unit may generate a layout while taking into account the geographical distribution of rooms. Examples of the geographical distribution of rooms include, but are not limited to, map data and location information. The generation unit selects an optimal generation method based on the geographical distribution of rooms. For example, the generation unit may select an optimal generation method based on the geographical distribution of rooms. Parameters of a generation algorithm may also be adjusted taking into account the geographical distribution of rooms. Furthermore, the generation result may be adjusted based on the geographical distribution of rooms. This allows a more appropriate layout to be obtained by generating a layout based on the geographical distribution. 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 may input geographical distribution data of rooms into a generation AI and cause the generation AI to execute a process of selecting an optimal generation method.

[0083] The generation unit can improve the accuracy of the generation based on room-related literature when generating a layout. For example, the generation unit improves the accuracy of the generation by referring to room-related literature when generating a layout. Room-related literature includes, but is not limited to, academic papers, technical reports, etc. The generation unit improves the accuracy of the generation algorithm based on the related literature. For example, the accuracy of the generation algorithm can be improved by referring to room-related literature. The generation method can also be optimized based on the room-related literature. Furthermore, the accuracy of the generation result can be improved by analyzing room-related literature. As a result, the accuracy of the generation can be improved by referring to the related literature. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input room-related literature data into the generation AI and cause the generation AI to improve the accuracy of the generation algorithm.

[0084] The suggestion unit can estimate the user's emotion and change the way suggestions are expressed based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the way suggestions are expressed based on the estimated user's emotion. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The suggestion unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, if the user is stressed, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This improves user satisfaction by providing a suggestion method that matches the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data and voice data into the generation AI and have the generation AI estimate emotions.

[0085] The suggestion unit can change 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. Examples of the importance of the product include, but are not limited to, sales data and customer reviews. The suggestion unit selects the optimal suggestion method based on the importance of the product. For example, for a product with high importance, the suggestion unit can make a suggestion including detailed information. For a product with low importance, the suggestion unit can make a suggestion including concise information. Furthermore, the level of detail of the suggestion can be adjusted according to the importance of the product. This improves user convenience by making suggestions according to the importance of the product. 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 importance data to the generation AI and cause the generation AI to execute a process of adjusting the level of detail of the suggestion.

[0086] The suggestion unit can apply different suggestion algorithms based on the product category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms based on the product category when making a suggestion. Product categories include, but are not limited to, furniture, home appliances, and decorative items. The suggestion unit selects an optimal suggestion algorithm based on the product category. For example, in the case of furniture, suggestions are made that emphasize placement efficiency. In addition, in the case of home appliances, suggestions can be made that take frequency of use into account. Furthermore, in the case of decorative items, suggestions can be made that emphasize design consistency. This enables more appropriate suggestions to be made by making suggestions based on the product category. 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 may input product category data into a generation AI and cause the generation AI to execute a process of selecting an optimal suggestion algorithm.

[0087] The suggestion unit can estimate the user's emotion and change 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. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The suggestion unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, if the user is stressed, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can provide a short, quickly understandable suggestion. This improves user satisfaction by providing a suggestion length that matches 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 can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's facial expression data and voice data into the generation AI and have the generation AI estimate emotions.

[0088] The suggestion unit can determine the order of suggestions based on the submission dates of the products when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the submission dates of the products when making suggestions. Examples of product submission dates include, but are not limited to, the sales start date, the campaign period, etc. The suggestion unit selects the optimal suggestion order based on the submission dates of the products. For example, products whose submission dates are close in time may be suggested first. Products whose submission dates are further away may also be suggested later. Furthermore, the priority of suggestions can be adjusted depending on the submission dates of the products. This improves user convenience by making suggestions based on the submission dates of the products. 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 submission date data into a generation AI and cause the generation AI to execute a process of determining the priority of suggestions.

[0089] The suggestion unit can change the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the products when making suggestions. Product relevance includes, but is not limited to, category matches, past purchase history, etc. The suggestion unit selects an optimal suggestion order based on the relevance of the products. For example, highly relevant products are suggested first. Also, less relevant products can be suggested later. Furthermore, the suggestion order can be adjusted according to the relevance of the products. This improves user convenience by making suggestions based on the relevance of the products. 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 relevance data to a generation AI and cause the generation AI to execute a process of determining the order of suggestions. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and proposal 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 control unit 46A of the smart device 14 and receives a user's image. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes real-world data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal layout. The proposal unit is realized by the control unit 46A of the smart device 14 and proposes products based on the generated layout. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and proposal 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 control unit 46A of the smart glasses 214 and receives a user's image. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes real-world data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal layout. The proposal unit is realized, for example, by the control unit 46A of the smart glasses 214 and proposes products 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 proposal 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 control unit 46A of the headset type terminal 314 and receives an image from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes real environment data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal layout. The proposal unit is realized, for example, by the control unit 46A of the headset type terminal 314 and proposes products 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 proposal 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 control unit 46A of the robot 414 and receives an image from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes real-world data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal layout. The proposal unit is realized, for example, by the control unit 46A of the robot 414 and proposes products based on the generated layout.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can analyze the user's past purchase history and make optimal product suggestions. For example, it can prioritize relevant products based on data on furniture and home appliances purchased by the user in the past. Also, if the user has a preference for a particular brand or style, it can make suggestions taking that information into account. Furthermore, it can also make product suggestions according to the season or event based on the user's purchase history. This makes it possible to make more personalized suggestions by utilizing the user's past purchase history.

[0092] The suggestion unit can estimate the user's emotions and adjust the price range of the products to be suggested based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize the suggestion of affordable products. If the user is relaxed, it can also suggest luxury products. Furthermore, if the user is in a hurry, it can also suggest products that can be purchased immediately. In this way, suggesting a price range according to the user's emotions improves user satisfaction.

[0093] The analysis unit can analyze the lighting conditions in a room and propose an optimal layout. For example, it proposes furniture placement taking into account the amount of natural light entering the room and the placement of the lighting. It can also propose optimal furniture colors and materials based on the color temperature and brightness of the lighting. It can also propose a layout that improves visual comfort depending on the lighting conditions in the room. This makes it possible to propose a layout that takes lighting conditions into account.

[0094] The generation unit can estimate the user's emotions and adjust the colors of the layout based on the estimated emotions. For example, if the user is feeling stressed, a layout with calm colors can be proposed. If the user is relaxed, a layout with bright and open colors can be proposed. Furthermore, if the user is in a hurry, a layout with simple and highly visible colors can be proposed. This makes it possible to propose a color layout according to the user's emotions.

[0095] The suggestion unit can analyze the user's lifestyle data and make optimal product suggestions. For example, it can suggest highly relevant products based on data such as the user's exercise habits, diet, and hobbies. It can also suggest products useful for health management and relaxation based on the user's lifestyle. Furthermore, it can utilize the user's lifestyle data to make product suggestions according to the season or event. This makes it possible to make personalized suggestions based on the user's lifestyle.

[0096] The analysis unit can estimate the user's emotions and change the method of providing feedback on the analysis results based on the estimated emotions. For example, if the user is feeling stressed, concise, positive feedback can be provided. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, quick feedback that focuses on the main points can be provided. In this way, providing a feedback method that corresponds to the user's emotions improves user satisfaction.

[0097] The reception unit can analyze the user's device usage history and provide the optimal interface. For example, it can suggest the optimal interface based on data on devices and applications used by the user in the past. Also, if the user frequently uses a specific device, it can provide an interface optimized for that device. Furthermore, it can predict and suggest the interface to be used during a specific time period based on the user's device usage history. In this way, by utilizing the user's device usage history, it is possible to provide a more convenient interface.

[0098] The generation unit can estimate the user's emotions and adjust the functionality of the layout based on the estimated emotions. For example, if the user is feeling stressed, a simple and functional layout can be proposed. If the user is relaxed, a layout that emphasizes design can be proposed. Furthermore, if the user is in a hurry, a layout that can be used quickly can be proposed. This makes it possible to propose layouts with functionality that correspond to the user's emotions.

[0099] The suggestion unit can analyze a user's social media activity and make optimal product suggestions. For example, it can suggest highly relevant products based on the posts and "likes" the user has shared on social media. It can also make suggestions taking into account information about the brands and influencers the user follows. It can also suggest trending and popular products based on the user's social media activity. This makes it possible to make more personalized product suggestions by utilizing the user's social media activity.

[0100] The analysis unit can estimate the user's emotions and change the priority of analysis based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize more important analyses. If the user is relaxed, it can also prioritize more detailed analyses. Furthermore, if the user is in a hurry, it can also prioritize analyses that can be completed quickly. In this way, by setting the priority of analysis according to the user's emotions, more appropriate analysis is possible.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives an image of the user. The image of the user may include, but is not limited to, text, images, and voice. For example, the reception unit receives a specific request input by the user via chat, such as "I want to put a sofa in my living room" or "I want more storage space in my kitchen." Step 2: The analysis unit analyzes the real-world data of the target room based on the image received by the reception unit. The real-world data may include, but is not limited to, LiDAR data, image data, and temperature data. The analysis unit analyzes, for example, data read by LiDAR, images of the room, and videos to determine the dimensions and shape of the room, the layout of existing furniture, and so on. Step 3: The generation unit generates a layout based on the data analyzed by the analysis unit. The generation unit generates an optimal layout based on, for example, the user's image and real-world data. The generation unit uses a generation AI to generate an optimal layout based on the user's image and real-world data. The generation AI generates a layout using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The suggestion unit suggests products based on the layout generated by the generation unit. For example, the suggestion unit suggests furniture and home appliances sold by manufacturers. The suggestion unit can also suggest product and shelf layout methods based on behavioral economics. For example, to optimize the store layout, the suggestion unit analyzes customer behavior patterns and suggests shelf layouts.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

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

[0124] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

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

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0147] 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.

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

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

[0150] In the 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.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

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

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 unit that receives an image of a user; an analysis unit that analyzes real environment data of a target room based on the image received by the reception unit; a generation unit that generates a layout based on the data analyzed by the analysis unit; a proposal unit that proposes products based on the layout generated by the generation unit. A system characterized by:

2. The proposal unit Suggest furniture or appliances 2. The system of claim 1.

3. The analysis unit Analyze data read by LiDAR or room images and videos 2. The system of claim 1.

4. The generation unit Generate layouts based on user image and real-world data 2. The system of claim 1.

5. The proposal unit Propose product or shelf placement based on behavioral economics 2. The system of claim 1.

6. The analysis unit Analyze customer behavior patterns 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and change the image acceptance method based on the estimated user emotions.

2. The system of claim 1.

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

Images