system

Abstract

The system according to this embodiment aims to propose a room layout that is appropriate for the actual environment based on the user's image. [Solution] The system according to the embodiment comprises a reception unit, a data input unit, a generation unit, and a proposal unit. The reception unit inputs the user's image via chat. The data input unit inputs actual environment data of the target room. The generation unit analyzes the data input by the reception unit and the data input unit and generates a layout that matches the actual environment. The proposal unit proposes the layout generated by the generation unit to the user.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to propose a layout of a room based on a user's image according to the actual environment. <000002b>

[0005] The system according to the embodiment aims to propose a layout of a room suitable for the actual environment based on a user's image.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a data input unit, a generation unit, and a proposal unit. The reception unit inputs the user's image via chat. The data input unit inputs real-world environment data of the target room. The generation unit analyzes the data input by the reception unit and the data input unit and generates a layout that matches the real environment. The proposal unit proposes the layout generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can propose a room layout that is suitable for the actual environment based on the user's image. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 3o, and a storage 32. The processor 28, the RAM 3o, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The layout generation system according to an embodiment of the present invention is a system that generates and proposes a layout that suits the actual environment by allowing the user to input their image via chat and input actual environment data of the target room (data read by LiDAR, images, videos). In this layout generation system, the user inputs their image via chat and actual environment data of the target room, and the generating AI analyzes this data to generate and propose a layout that suits the actual environment. The generated layout includes the furniture and appliances to be placed, which are output as products sold by manufacturers. For example, the user inputs specific requests via chat, such as "I want a modern living room" or "I want a bedroom with a natural atmosphere." This information is input to the generating AI. Next, the user inputs actual environment data of the target room. For example, this includes 3D data of the room read by LiDAR, images of the room, videos, etc. This data is input to the generating AI, and the actual dimensions and shape of the room, the arrangement of existing furniture, etc. are analyzed. Based on the user's image and the actual environment data, the generating AI generates the optimal layout. For example, for a user who wants a modern living room, the generating AI selects modern furniture and appliances and places them according to the dimensions and shape of the room. In this process, the generating AI refers to a product database sold by manufacturers and suggests specific products. The generated layout is then presented to the user. The user can review the suggested layout and request modifications as needed. For example, they can give instructions to the generating AI via chat if they want to change the placement of specific furniture or request suggestions for different furniture. This system allows users to easily generate and suggest room layouts that match their vision. Furthermore, because it is based on real-world data, the suggested layouts are suitable for the actual room. In addition, since the suggested furniture and appliances are products sold by manufacturers, users can purchase them directly. Thus, the layout generation system can generate and suggest the optimal layout based on the user's vision and real-world data.

[0029] The layout generation system according to the embodiment comprises a reception unit, a data input unit, a generation unit, and a proposal unit. The reception unit receives the user's image via chat. The user's image includes, but is not limited to, specific requests such as "I want a modern living room" or "I want a bedroom with a natural atmosphere." The reception unit receives the user's request in, for example, text format. The reception unit can also receive the user's request using voice input. For example, the user can input "I want a modern living room" by voice. The data input unit receives real-world environment data of the target room. Real-world environment data includes, but is not limited to, 3D data of the room read by LiDAR, images of the room, videos, etc. The data input unit can acquire 3D data of the room using, for example, a LiDAR sensor. The data input unit can also acquire images and videos of the room using a camera. For example, the user can take an image of the room with their smartphone camera and input it to the data input unit. The generation unit uses a generation AI to analyze the data input by the reception unit and the data input unit and generates a layout that suits the real environment. The generation unit generates an optimal layout based, for example, the user's image and real-world environment data. The generation unit uses a generation AI to select furniture and appliances according to the user's requests and arrange them to fit the dimensions and shape of the room. For example, for a user who desires a modern living room, the generation AI selects modern furniture and appliances and arranges them to fit the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. The suggestion unit proposes the layout generated by the generation unit to the user. The suggestion unit displays the generated layout to the user, for example. The suggestion unit can also accept user feedback. For example, if the user wants to change the placement of specific furniture or wants suggestions for different furniture, they can give instructions to the generation AI via chat. As a result, the layout generation system according to this embodiment can generate and propose an optimal layout based on the user's image and real-world environment data.

[0030] The generation unit can generate the optimal layout based on the user's image and real-world environment data. For example, the generation unit generates the optimal layout based on the user's image and real-world environment data. The generation unit uses generation AI to select furniture and appliances according to the user's requests and arrange them according to the dimensions and shape of the room. For example, for a user who wants a modern living room, the generation AI selects modern furniture and appliances and arranges them according to the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. In this way, the generation unit can generate the optimal layout based on the user's image and real-world environment data.

[0031] The generation unit can select specific furniture and appliances by referring to a product database sold by manufacturers. For example, the generation unit selects specific furniture and appliances by referring to a product database sold by manufacturers. Using generation AI, the generation unit selects furniture and appliances that meet the user's needs and arranges them according to the room's dimensions and shape. For example, for a user who desires a modern living room, the generation AI selects modern furniture and appliances and arranges them according to the room's dimensions and shape. The generation unit proposes specific products by referring to a product database sold by manufacturers. This allows the generation unit to select specific furniture and appliances by referring to a product database sold by manufacturers.

[0032] The proposal unit can suggest the generated layout to the user and receive user feedback. For example, the proposal unit can display the generated layout to the user. The proposal unit suggests the layout generated by the generation unit to the user. The proposal unit can also receive user feedback. For example, if the user wants to change the placement of certain furniture or wants suggestions for different furniture, they can give instructions to the generation AI via chat. This allows the proposal unit to suggest the generated layout to the user and receive user feedback.

[0033] The generation unit can regenerate the layout based on user feedback. For example, the generation unit regenerates the layout based on user feedback. The generation unit uses generation AI to select furniture and appliances according to the user's requests and arranges them to fit the dimensions and shape of the room. For example, for a user who wants a modern living room, the generation AI selects modern furniture and appliances and arranges them to fit the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. This allows the generation unit to regenerate the layout based on user feedback.

[0034] The input interface can analyze the user's past image input history and provide the optimal input interface. For example, the input interface can automatically display images that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input interface can predict and suggest images that the user will use at a specific time period based on their past input history. In this way, the input interface can provide the optimal input interface by analyzing the user's past image input history.

[0035] The reception system can filter images based on the user's current projects and areas of interest when they are entered. For example, the reception system will prioritize displaying images related to the user's current projects. It can also suggest relevant images based on the user's areas of interest. Furthermore, the reception system can filter and display images related to projects the user has shown interest in in the past. In this way, the reception system can provide highly relevant images by filtering based on the user's current projects and areas of interest.

[0036] The reception system can prioritize retrieving highly relevant images when users input images, taking into account their geographical location. For example, it can prioritize displaying images related to the user's current location. It can also prioritize displaying images related to places the user has visited in the past. Furthermore, it can prioritize displaying images related to places the user plans to visit in the future. In this way, the reception system can provide highly relevant images while considering the user's geographical location.

[0037] The reception system can analyze the user's social media activity when an image is input and retrieve relevant images. For example, the reception system can prioritize displaying images that the user has shared on social media. It can also prioritize displaying images that the user has "liked" on social media. Furthermore, it can prioritize displaying images related to accounts that the user follows on social media. In this way, the reception system can provide relevant images by analyzing the user's social media activity.

[0038] The data entry unit can analyze the user's past data entry history when acquiring real-world data and select the optimal acquisition method. For example, the data entry unit can prioritize suggesting data acquisition methods that the user has used in the past. Furthermore, the data entry unit can suggest the optimal data acquisition timing based on the user's past data entry history. In addition, the data entry unit can select the optimal data acquisition method based on the user's past data entry history. Thus, the data entry unit can select the optimal data acquisition method by analyzing the user's past data entry history.

[0039] The data entry unit can filter real-world data based on the user's current lifestyle and areas of interest. For example, the data entry unit can prioritize acquiring data related to projects the user is currently working on. It can also prioritize acquiring relevant data based on the user's areas of interest. Furthermore, it can acquire optimal data based on the user's current lifestyle. As a result, the data entry unit can provide highly relevant data by filtering based on the user's current lifestyle and areas of interest.

[0040] The data entry unit can prioritize the acquisition of highly relevant data when acquiring real-world data, taking into account the user's geographical location. For example, the data entry unit can prioritize the acquisition of data related to the area where the user is currently located. It can also prioritize the acquisition of data related to places the user has visited in the past. Furthermore, it can prioritize the acquisition of data related to places the user plans to visit in the future. In this way, the data entry unit can provide highly relevant data while taking into account the user's geographical location.

[0041] The data entry unit can analyze users' social media activity and retrieve relevant data when acquiring real-world data. For example, the data entry unit can prioritize retrieving data that users have shared on social media. It can also prioritize retrieving data that users have "liked" on social media. Furthermore, the data entry unit can prioritize retrieving data related to accounts that users follow on social media. In this way, the data entry unit can provide relevant data by analyzing users' social media activity.

[0042] The generation unit can analyze the user's past layout history and apply the optimal generation algorithm when generating a layout. For example, the generation unit can prioritize suggesting layout styles previously used by the user. It can also select the optimal generation algorithm based on the user's past layout history. Furthermore, the generation unit can generate the optimal layout based on the user's past layout history. In this way, the generation unit can apply the optimal generation algorithm by analyzing the user's past layout history.

[0043] The generation unit can customize the layout during generation based on the user's current lifestyle and areas of interest. For example, the generation unit can prioritize generating layouts related to the user's current projects. It can also generate relevant layouts based on the user's areas of interest. Furthermore, it can generate the optimal layout based on the user's current lifestyle. In this way, the generation unit can provide a customized layout based on the user's current lifestyle and areas of interest.

[0044] The generation unit can generate the optimal layout by considering the user's geographical location information during layout generation. For example, the generation unit can prioritize generating layouts related to the area the user is currently in. It can also prioritize generating layouts related to places the user has visited in the past. Furthermore, it can prioritize generating layouts related to places the user plans to visit in the future. In this way, the generation unit can provide the optimal layout by considering the user's geographical location information.

[0045] The generation unit can analyze the user's social media activity when generating layouts and generate relevant layouts. For example, the generation unit can prioritize generating layouts that the user has shared on social media. It can also prioritize generating layouts that the user has "liked" on social media. Furthermore, the generation unit can prioritize generating layouts related to accounts that the user follows on social media. In this way, the generation unit can provide relevant layouts by analyzing the user's social media activity.

[0046] The proposal department can analyze the user's past feedback history when making a proposal and select the most suitable proposal method. For example, the proposal department can prioritize providing proposal styles that the user has preferred in the past. Furthermore, the proposal department can select the optimal timing for proposals based on the user's past feedback history. In addition, the proposal department can select the most suitable proposal content based on the user's past feedback history. Thus, by analyzing the user's past feedback history, the proposal department can provide the most suitable proposal method.

[0047] The proposal department can customize proposals based on the user's current life circumstances and areas of interest. For example, it can prioritize proposals related to projects the user is currently working on. It can also make relevant proposals based on the user's areas of interest. Furthermore, it can make optimal proposals based on the user's current life circumstances. In this way, the proposal department can provide more appropriate proposals by customizing proposals based on the user's current life circumstances and areas of interest.

[0048] The suggestion function can provide optimal suggestions by considering the user's geographical location. For example, it can prioritize suggestions related to the user's current location. It can also prioritize suggestions related to places the user has visited in the past. Furthermore, it can prioritize suggestions related to places the user plans to visit in the future. In this way, the suggestion function can provide optimal suggestions by considering the user's geographical location.

[0049] The suggestion department can analyze a user's social media activity and make relevant suggestions when making proposals. For example, the suggestion department can make suggestions based on what the user has shared on social media. It can also make suggestions based on what the user has "liked" on social media. Furthermore, the suggestion department can make suggestions related to the accounts the user follows on social media. In this way, the suggestion department can provide relevant suggestions by analyzing the user's social media activity.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception unit receives user input via chat. This input includes, but is not limited to, specific requests such as "I want a modern living room" or "I want a bedroom with a natural atmosphere." The reception unit accepts user requests in text format, for example. It can also accept requests via voice input; for example, a user could say "I want a modern living room" by voice. The data input unit inputs real-world data of the target room. This real-world data includes, but is not limited to, 3D data of the room read by LiDAR, images, or videos of the room. The data input unit can acquire 3D data of the room using a LiDAR sensor, for example. It can also acquire images or videos of the room using a camera; for example, a user could take an image of the room with their smartphone camera and input it into the data input unit. The generation unit uses generation AI to analyze the data input by the reception and data input units and generate a layout that suits the real environment. For example, the generation unit generates an optimal layout based on the user's image and the real-world data. The generation unit uses a generation AI to select furniture and appliances according to the user's requests and arrange them to fit the dimensions and shape of the room. For example, for a user who desires a modern living room, the generation AI selects modern furniture and appliances and arranges them to fit the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. The suggestion unit proposes the layout generated by the generation unit to the user. The suggestion unit displays the generated layout to the user, for example. The suggestion unit can also accept user feedback. For example, if the user wants to change the placement of specific furniture or wants suggestions for different furniture, they can give instructions to the generation AI via chat. As a result, the layout generation system according to this embodiment can generate and propose the optimal layout based on the user's image and real-world environment data.

[0052] The generation unit can generate the optimal layout based on the user's image and real-world environment data. For example, the generation unit generates the optimal layout based on the user's image and real-world environment data. The generation unit uses generation AI to select furniture and appliances according to the user's requests and arrange them according to the dimensions and shape of the room. For example, for a user who wants a modern living room, the generation AI selects modern furniture and appliances and arranges them according to the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. In this way, the generation unit can generate the optimal layout based on the user's image and real-world environment data.

[0053] The generation unit can select specific furniture and appliances by referring to a product database sold by manufacturers. For example, the generation unit selects specific furniture and appliances by referring to a product database sold by manufacturers. Using generation AI, the generation unit selects furniture and appliances that meet the user's needs and arranges them according to the room's dimensions and shape. For example, for a user who desires a modern living room, the generation AI selects modern furniture and appliances and arranges them according to the room's dimensions and shape. The generation unit proposes specific products by referring to a product database sold by manufacturers. This allows the generation unit to select specific furniture and appliances by referring to a product database sold by manufacturers.

[0054] The proposal unit can suggest the generated layout to the user and receive user feedback. For example, the proposal unit can display the generated layout to the user. The proposal unit suggests the layout generated by the generation unit to the user. The proposal unit can also receive user feedback. For example, if the user wants to change the placement of certain furniture or wants suggestions for different furniture, they can give instructions to the generation AI via chat. This allows the proposal unit to suggest the generated layout to the user and receive user feedback.

[0055] The generation unit can regenerate the layout based on user feedback. For example, the generation unit regenerates the layout based on user feedback. The generation unit uses generation AI to select furniture and appliances according to the user's requests and arranges them to fit the dimensions and shape of the room. For example, for a user who wants a modern living room, the generation AI selects modern furniture and appliances and arranges them to fit the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. This allows the generation unit to regenerate the layout based on user feedback.

[0056] The input interface can analyze the user's past image input history and provide the optimal input interface. For example, the input interface can automatically display images that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input interface can predict and suggest images that the user will use at a specific time period based on their past input history. In this way, the input interface can provide the optimal input interface by analyzing the user's past image input history.

[0057] The reception system can filter images based on the user's current projects and areas of interest when they are entered. For example, the reception system will prioritize displaying images related to the user's current projects. It can also suggest relevant images based on the user's areas of interest. Furthermore, the reception system can filter and display images related to projects the user has shown interest in in the past. In this way, the reception system can provide highly relevant images by filtering based on the user's current projects and areas of interest.

[0058] The reception system can prioritize retrieving highly relevant images when users input images, taking into account their geographical location. For example, it can prioritize displaying images related to the user's current location. It can also prioritize displaying images related to places the user has visited in the past. Furthermore, it can prioritize displaying images related to places the user plans to visit in the future. In this way, the reception system can provide highly relevant images while considering the user's geographical location.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives the user's vision via chat. This vision includes specific requests, such as "I want a modern living room" or "I want a bedroom with a natural atmosphere." The reception desk can also receive user requests via text or voice input. Step 2: The data input unit inputs real-world data of the target room. This real-world data includes 3D data of the room read by LiDAR, as well as images and videos of the room. The data input unit can acquire 3D data, images, and videos of the room using LiDAR sensors and cameras. Step 3: The generation unit uses generation AI to analyze the data entered by the reception unit and data entry unit and generate a layout that suits the actual environment. Based on the user's image and the actual environment data, the generation unit generates the optimal layout, selects furniture and appliances according to the user's requests, and arranges them according to the dimensions and shape of the room. The generation unit refers to the product database sold by manufacturers and proposes specific products. Step 4: The suggestion unit proposes the layout generated by the generation unit to the user. The suggestion unit can display the generated layout to the user and accept user feedback. Users can give instructions to the generation AI via chat, such as changing the placement of specific furniture or requesting suggestions for different furniture.

[0061] (Example of form 2) The layout generation system according to an embodiment of the present invention is a system that generates and proposes a layout that suits the actual environment by allowing the user to input their image via chat and input actual environment data of the target room (data read by LiDAR, images, videos). In this layout generation system, the user inputs their image via chat and actual environment data of the target room, and the generating AI analyzes this data to generate and propose a layout that suits the actual environment. The generated layout includes the furniture and appliances to be placed, which are output as products sold by manufacturers. For example, the user inputs specific requests via chat, such as "I want a modern living room" or "I want a bedroom with a natural atmosphere." This information is input to the generating AI. Next, the user inputs actual environment data of the target room. For example, this includes 3D data of the room read by LiDAR, images of the room, videos, etc. This data is input to the generating AI, and the actual dimensions and shape of the room, the arrangement of existing furniture, etc. are analyzed. Based on the user's image and the actual environment data, the generating AI generates the optimal layout. For example, for a user who wants a modern living room, the generating AI selects modern furniture and appliances and places them according to the dimensions and shape of the room. In this process, the generating AI refers to a product database sold by manufacturers and suggests specific products. The generated layout is then presented to the user. The user can review the suggested layout and request modifications as needed. For example, they can give instructions to the generating AI via chat if they want to change the placement of specific furniture or request suggestions for different furniture. This system allows users to easily generate and suggest room layouts that match their vision. Furthermore, because it is based on real-world data, the suggested layouts are suitable for the actual room. In addition, since the suggested furniture and appliances are products sold by manufacturers, users can purchase them directly. Thus, the layout generation system can generate and suggest the optimal layout based on the user's vision and real-world data.

[0062] The layout generation system according to the embodiment comprises a reception unit, a data input unit, a generation unit, and a proposal unit. The reception unit receives the user's image via chat. The user's image includes, but is not limited to, specific requests such as "I want a modern living room" or "I want a bedroom with a natural atmosphere." The reception unit receives the user's request in, for example, text format. The reception unit can also receive the user's request using voice input. For example, the user can input "I want a modern living room" by voice. The data input unit receives real-world environment data of the target room. Real-world environment data includes, but is not limited to, 3D data of the room read by LiDAR, images of the room, videos, etc. The data input unit can acquire 3D data of the room using, for example, a LiDAR sensor. The data input unit can also acquire images and videos of the room using a camera. For example, the user can take an image of the room with their smartphone camera and input it to the data input unit. The generation unit uses a generation AI to analyze the data input by the reception unit and the data input unit and generates a layout that suits the real environment. The generation unit generates an optimal layout based, for example, the user's image and real-world environment data. The generation unit uses a generation AI to select furniture and appliances according to the user's requests and arrange them to fit the dimensions and shape of the room. For example, for a user who desires a modern living room, the generation AI selects modern furniture and appliances and arranges them to fit the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. The suggestion unit proposes the layout generated by the generation unit to the user. The suggestion unit displays the generated layout to the user, for example. The suggestion unit can also accept user feedback. For example, if the user wants to change the placement of specific furniture or wants suggestions for different furniture, they can give instructions to the generation AI via chat. As a result, the layout generation system according to this embodiment can generate and propose an optimal layout based on the user's image and real-world environment data.

[0063] The generation unit can generate the optimal layout based on the user's image and real-world environment data. For example, the generation unit generates the optimal layout based on the user's image and real-world environment data. The generation unit uses generation AI to select furniture and appliances according to the user's requests and arrange them according to the dimensions and shape of the room. For example, for a user who wants a modern living room, the generation AI selects modern furniture and appliances and arranges them according to the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. In this way, the generation unit can generate the optimal layout based on the user's image and real-world environment data.

[0064] The generation unit can select specific furniture and appliances by referring to a product database sold by manufacturers. For example, the generation unit selects specific furniture and appliances by referring to a product database sold by manufacturers. Using generation AI, the generation unit selects furniture and appliances that meet the user's needs and arranges them according to the room's dimensions and shape. For example, for a user who desires a modern living room, the generation AI selects modern furniture and appliances and arranges them according to the room's dimensions and shape. The generation unit proposes specific products by referring to a product database sold by manufacturers. This allows the generation unit to select specific furniture and appliances by referring to a product database sold by manufacturers.

[0065] The proposal unit can suggest the generated layout to the user and receive user feedback. For example, the proposal unit can display the generated layout to the user. The proposal unit suggests the layout generated by the generation unit to the user. The proposal unit can also receive user feedback. For example, if the user wants to change the placement of certain furniture or wants suggestions for different furniture, they can give instructions to the generation AI via chat. This allows the proposal unit to suggest the generated layout to the user and receive user feedback.

[0066] The generation unit can regenerate the layout based on user feedback. For example, the generation unit regenerates the layout based on user feedback. The generation unit uses generation AI to select furniture and appliances according to the user's requests and arranges them to fit the dimensions and shape of the room. For example, for a user who wants a modern living room, the generation AI selects modern furniture and appliances and arranges them to fit the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. This allows the generation unit to regenerate the layout based on user feedback.

[0067] The reception system can estimate the user's emotions and adjust the image input method based on the estimated emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick image input. This allows the reception system to provide more appropriate input by adjusting the image input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The input interface can analyze the user's past image input history and provide the optimal input interface. For example, the input interface can automatically display images that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input interface can predict and suggest images that the user will use at a specific time period based on their past input history. In this way, the input interface can provide the optimal input interface by analyzing the user's past image input history.

[0069] The reception system can filter images based on the user's current projects and areas of interest when they are entered. For example, the reception system will prioritize displaying images related to the user's current projects. It can also suggest relevant images based on the user's areas of interest. Furthermore, the reception system can filter and display images related to projects the user has shown interest in in the past. In this way, the reception system can provide highly relevant images by filtering based on the user's current projects and areas of interest.

[0070] The reception system can estimate the user's emotions and prioritize the input images based on the estimated emotions. For example, if the user is excited, the reception system may prioritize displaying visually stimulating images. It may also prioritize displaying calming images if the user is relaxed. Furthermore, if the user is stressed, it may prioritize displaying simple, visually less burdensome images. This allows the reception system to provide more appropriate images by prioritizing input images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The reception system can prioritize retrieving highly relevant images when users input images, taking into account their geographical location. For example, it can prioritize displaying images related to the user's current location. It can also prioritize displaying images related to places the user has visited in the past. Furthermore, it can prioritize displaying images related to places the user plans to visit in the future. In this way, the reception system can provide highly relevant images while considering the user's geographical location.

[0072] The reception system can analyze the user's social media activity when an image is input and retrieve relevant images. For example, the reception system can prioritize displaying images that the user has shared on social media. It can also prioritize displaying images that the user has "liked" on social media. Furthermore, it can prioritize displaying images related to accounts that the user follows on social media. In this way, the reception system can provide relevant images by analyzing the user's social media activity.

[0073] The data entry unit can estimate the user's emotions and adjust the timing of real-world data acquisition based on the estimated emotions. For example, if the user is relaxed, the data entry unit will acquire data at a leisurely pace. Conversely, if the user is in a hurry, the data entry unit can acquire data quickly. Furthermore, if the user is stressed, the data entry unit can provide a simpler data acquisition method. This allows the data entry unit to acquire more appropriate data by adjusting the timing of real-world data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The data entry unit can analyze the user's past data entry history when acquiring real-world data and select the optimal acquisition method. For example, the data entry unit can prioritize suggesting data acquisition methods that the user has used in the past. Furthermore, the data entry unit can suggest the optimal data acquisition timing based on the user's past data entry history. In addition, the data entry unit can select the optimal data acquisition method based on the user's past data entry history. Thus, the data entry unit can select the optimal data acquisition method by analyzing the user's past data entry history.

[0075] The data entry unit can filter real-world data based on the user's current lifestyle and areas of interest. For example, the data entry unit can prioritize acquiring data related to projects the user is currently working on. It can also prioritize acquiring relevant data based on the user's areas of interest. Furthermore, it can acquire optimal data based on the user's current lifestyle. As a result, the data entry unit can provide highly relevant data by filtering based on the user's current lifestyle and areas of interest.

[0076] The data entry unit can estimate the user's emotions and determine the priority of real-world data to acquire based on the estimated emotions. For example, if the user is excited, the data entry unit will prioritize acquiring visually stimulating data. It can also prioritize acquiring calming data if the user is relaxed. Furthermore, if the user is stressed, it can prioritize acquiring simple and visually less burdensome data. This allows the data entry unit to provide more appropriate data by prioritizing real-world data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The data entry unit can prioritize the acquisition of highly relevant data when acquiring real-world data, taking into account the user's geographical location. For example, the data entry unit can prioritize the acquisition of data related to the area where the user is currently located. It can also prioritize the acquisition of data related to places the user has visited in the past. Furthermore, it can prioritize the acquisition of data related to places the user plans to visit in the future. In this way, the data entry unit can provide highly relevant data while taking into account the user's geographical location.

[0078] The data entry unit can analyze users' social media activity and retrieve relevant data when acquiring real-world data. For example, the data entry unit can prioritize retrieving data that users have shared on social media. It can also prioritize retrieving data that users have "liked" on social media. Furthermore, the data entry unit can prioritize retrieving data related to accounts that users follow on social media. In this way, the data entry unit can provide relevant data by analyzing users' social media activity.

[0079] The generation unit can estimate the user's emotions and adjust the layout generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a layout that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also provide a layout that can be generated in the shortest possible time. Furthermore, if the user is excited, the generation unit can generate a layout with visually stimulating effects. In this way, the generation unit can provide a more appropriate layout by adjusting the layout generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The generation unit can analyze the user's past layout history and apply the optimal generation algorithm when generating a layout. For example, the generation unit can prioritize suggesting layout styles previously used by the user. It can also select the optimal generation algorithm based on the user's past layout history. Furthermore, the generation unit can generate the optimal layout based on the user's past layout history. In this way, the generation unit can apply the optimal generation algorithm by analyzing the user's past layout history.

[0081] The generation unit can customize the layout during generation based on the user's current lifestyle and areas of interest. For example, the generation unit can prioritize generating layouts related to the user's current projects. It can also generate relevant layouts based on the user's areas of interest. Furthermore, it can generate the optimal layout based on the user's current lifestyle. In this way, the generation unit can provide a customized layout based on the user's current lifestyle and areas of interest.

[0082] The generation unit can estimate the user's emotions and determine the priority of the generated layout based on the estimated emotions. For example, if the user is excited, the generation unit may prioritize displaying a visually stimulating layout. It can also prioritize displaying a calming layout if the user is relaxed. Furthermore, if the user is stressed, the generation unit may prioritize displaying a simple and visually less burdensome layout. This allows the generation unit to provide a more appropriate layout by prioritizing the generated layout according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The generation unit can generate the optimal layout by considering the user's geographical location information during layout generation. For example, the generation unit can prioritize generating layouts related to the area the user is currently in. It can also prioritize generating layouts related to places the user has visited in the past. Furthermore, it can prioritize generating layouts related to places the user plans to visit in the future. In this way, the generation unit can provide the optimal layout by considering the user's geographical location information.

[0084] The generation unit can analyze the user's social media activity when generating layouts and generate relevant layouts. For example, the generation unit can prioritize generating layouts that the user has shared on social media. It can also prioritize generating layouts that the user has "liked" on social media. Furthermore, the generation unit can prioritize generating layouts related to accounts that the user follows on social media. In this way, the generation unit can provide relevant layouts by analyzing the user's social media activity.

[0085] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion function can provide a simple and easily understandable suggestion. If the user is relaxed, it can also provide a suggestion that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a concise suggestion. In this way, the suggestion function can provide more appropriate suggestions by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The proposal department can analyze the user's past feedback history when making a proposal and select the most suitable proposal method. For example, the proposal department can prioritize providing proposal styles that the user has preferred in the past. Furthermore, the proposal department can select the optimal timing for proposals based on the user's past feedback history. In addition, the proposal department can select the most suitable proposal content based on the user's past feedback history. Thus, by analyzing the user's past feedback history, the proposal department can provide the most suitable proposal method.

[0087] The proposal department can customize proposals based on the user's current life circumstances and areas of interest. For example, it can prioritize proposals related to projects the user is currently working on. It can also make relevant proposals based on the user's areas of interest. Furthermore, it can make optimal proposals based on the user's current life circumstances. In this way, the proposal department can provide more appropriate proposals by customizing proposals based on the user's current life circumstances and areas of interest.

[0088] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is excited, the suggestion unit will prioritize visually stimulating suggestions. If the user is relaxed, the suggestion unit can also prioritize calming suggestions. Furthermore, if the user is stressed, the suggestion unit can prioritize simple and visually less burdensome suggestions. In this way, the suggestion unit can provide more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The suggestion function can provide optimal suggestions by considering the user's geographical location. For example, it can prioritize suggestions related to the user's current location. It can also prioritize suggestions related to places the user has visited in the past. Furthermore, it can prioritize suggestions related to places the user plans to visit in the future. In this way, the suggestion function can provide optimal suggestions by considering the user's geographical location.

[0090] The suggestion department can analyze a user's social media activity and make relevant suggestions when making proposals. For example, the suggestion department can make suggestions based on what the user has shared on social media. It can also make suggestions based on what the user has "liked" on social media. Furthermore, the suggestion department can make suggestions related to the accounts the user follows on social media. In this way, the suggestion department can provide relevant suggestions by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, data input unit, generation unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs the user's image in a chat-based manner. The data input unit acquires real-world data using the camera 42 and LiDAR sensor of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a layout using generation AI. The proposal unit displays the generated layout to the user using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, data input unit, generation unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and inputs the user's image in a chat-based manner. The data input unit acquires real-world data using the camera 42 and LiDAR sensor of the smart glasses 214. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a layout using generation AI. The proposal unit displays the generated layout to the user using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, data input unit, generation unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and inputs the user's image in a chat-based manner. The data input unit acquires real-world data using the camera 42 and LiDAR sensor of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a layout using generation AI. The proposal unit displays the generated layout to the user using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, data input unit, generation unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and inputs the user's image in a chat-based manner. The data input unit acquires real-world data using the camera 42 and LiDAR sensor of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a layout using generation AI. The proposal unit displays the generated layout to the user using the display of the robot 414.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The reception unit receives user input via chat. This input includes, but is not limited to, specific requests such as "I want a modern living room" or "I want a bedroom with a natural atmosphere." The reception unit accepts user requests in text format, for example. It can also accept requests via voice input; for example, a user could say "I want a modern living room" by voice. The data input unit inputs real-world data of the target room. This real-world data includes, but is not limited to, 3D data of the room read by LiDAR, images, or videos of the room. The data input unit can acquire 3D data of the room using a LiDAR sensor, for example. It can also acquire images or videos of the room using a camera; for example, a user could take an image of the room with their smartphone camera and input it into the data input unit. The generation unit uses generation AI to analyze the data input by the reception and data input units and generate a layout that suits the real environment. For example, the generation unit generates an optimal layout based on the user's image and the real-world data. The generation unit uses a generation AI to select furniture and appliances according to the user's requests and arrange them to fit the dimensions and shape of the room. For example, for a user who desires a modern living room, the generation AI selects modern furniture and appliances and arranges them to fit the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. The suggestion unit proposes the layout generated by the generation unit to the user. The suggestion unit displays the generated layout to the user, for example. The suggestion unit can also accept user feedback. For example, if the user wants to change the placement of specific furniture or wants suggestions for different furniture, they can give instructions to the generation AI via chat. As a result, the layout generation system according to this embodiment can generate and propose the optimal layout based on the user's image and real-world environment data.

[0093] The generation unit can generate the optimal layout based on the user's image and real-world environment data. For example, the generation unit generates the optimal layout based on the user's image and real-world environment data. The generation unit uses generation AI to select furniture and appliances according to the user's requests and arrange them according to the dimensions and shape of the room. For example, for a user who wants a modern living room, the generation AI selects modern furniture and appliances and arranges them according to the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. In this way, the generation unit can generate the optimal layout based on the user's image and real-world environment data.

[0094] The generation unit can select specific furniture and appliances by referring to a product database sold by manufacturers. For example, the generation unit selects specific furniture and appliances by referring to a product database sold by manufacturers. Using generation AI, the generation unit selects furniture and appliances that meet the user's needs and arranges them according to the room's dimensions and shape. For example, for a user who desires a modern living room, the generation AI selects modern furniture and appliances and arranges them according to the room's dimensions and shape. The generation unit proposes specific products by referring to a product database sold by manufacturers. This allows the generation unit to select specific furniture and appliances by referring to a product database sold by manufacturers.

[0095] The proposal unit can suggest the generated layout to the user and receive user feedback. For example, the proposal unit can display the generated layout to the user. The proposal unit suggests the layout generated by the generation unit to the user. The proposal unit can also receive user feedback. For example, if the user wants to change the placement of certain furniture or wants suggestions for different furniture, they can give instructions to the generation AI via chat. This allows the proposal unit to suggest the generated layout to the user and receive user feedback.

[0096] The generation unit can regenerate the layout based on user feedback. For example, the generation unit regenerates the layout based on user feedback. The generation unit uses generation AI to select furniture and appliances according to the user's requests and arranges them to fit the dimensions and shape of the room. For example, for a user who wants a modern living room, the generation AI selects modern furniture and appliances and arranges them to fit the dimensions and shape of the room. The generation unit refers to a product database sold by manufacturers and suggests specific products. This allows the generation unit to regenerate the layout based on user feedback.

[0097] The reception system can estimate the user's emotions and adjust the image input method based on the estimated emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick image input. This allows the reception system to provide more appropriate input by adjusting the image input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The input interface can analyze the user's past image input history and provide the optimal input interface. For example, the input interface can automatically display images that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input interface can predict and suggest images that the user will use at a specific time period based on their past input history. In this way, the input interface can provide the optimal input interface by analyzing the user's past image input history.

[0099] The reception system can filter images based on the user's current projects and areas of interest when they are entered. For example, the reception system will prioritize displaying images related to the user's current projects. It can also suggest relevant images based on the user's areas of interest. Furthermore, the reception system can filter and display images related to projects the user has shown interest in in the past. In this way, the reception system can provide highly relevant images by filtering based on the user's current projects and areas of interest.

[0100] The reception system can estimate the user's emotions and prioritize the input images based on the estimated emotions. For example, if the user is excited, the reception system may prioritize displaying visually stimulating images. It may also prioritize displaying calming images if the user is relaxed. Furthermore, if the user is stressed, it may prioritize displaying simple, visually less burdensome images. This allows the reception system to provide more appropriate images by prioritizing input images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The reception system can prioritize retrieving highly relevant images when users input images, taking into account their geographical location. For example, it can prioritize displaying images related to the user's current location. It can also prioritize displaying images related to places the user has visited in the past. Furthermore, it can prioritize displaying images related to places the user plans to visit in the future. In this way, the reception system can provide highly relevant images while considering the user's geographical location.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The reception desk receives the user's vision via chat. This vision includes specific requests, such as "I want a modern living room" or "I want a bedroom with a natural atmosphere." The reception desk can also receive user requests via text or voice input. Step 2: The data input unit inputs real-world data of the target room. This real-world data includes 3D data of the room read by LiDAR, as well as images and videos of the room. The data input unit can acquire 3D data, images, and videos of the room using LiDAR sensors and cameras. Step 3: The generation unit uses generation AI to analyze the data entered by the reception unit and data entry unit and generate a layout that suits the actual environment. Based on the user's image and the actual environment data, the generation unit generates the optimal layout, selects furniture and appliances according to the user's requests, and arranges them according to the dimensions and shape of the room. The generation unit refers to the product database sold by manufacturers and proposes specific products. Step 4: The suggestion unit proposes the layout generated by the generation unit to the user. The suggestion unit can display the generated layout to the user and accept user feedback. Users can give instructions to the generation AI via chat, such as changing the placement of specific furniture or requesting suggestions for different furniture.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0106] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0115] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0148] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0159] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0160] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0161] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0165] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0166] 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.

[0167] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0168] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0169] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0170] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0172] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0173] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0174] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users input their image via chat, A data input unit for inputting actual environmental data of the target room, A generation unit analyzes the data input by the reception unit and the data input unit and generates a layout that is appropriate for the actual environment, The system includes a proposal unit that proposes a layout generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is Generate the optimal layout based on user images and real-world environment data. The system according to feature 1.

3. The generating unit is Refer to the manufacturer's product database to select specific furniture and home appliances. The system according to feature 1.

4. The aforementioned proposal section is, The generated layout is presented to the user, and user feedback is solicited. The system according to feature 1.

5. The generating unit is Regenerate the layout based on user feedback. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the image input method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past image input history and provides the optimal input interface. The system according to feature 1.

8. The aforementioned reception unit is When inputting images, filtering is performed based on the user's current project and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input images based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When an image is input, the system prioritizes retrieving images that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

Images