System

Abstract

An object of a system according to an embodiment is to generate an optimal layout based on an image of a room desired by a user and real environment data.SOLUTION: A system according to an embodiment includes an image input unit, a real environment data input unit, a layout generation unit, and a product information output unit. The image input unit inputs an image of a room desired by a user. The real environment data input unit inputs real environment data of a target room. The layout generation unit generates a layout based on the data input by the image input unit and the real environment data input unit. The product information output unit outputs specific product information based on the layout generated by the layout generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to generate an optimal layout based on the user's desired room image and the actual room environment.

[0005] The system according to the embodiment aims to generate an optimal layout based on a user's desired image of a room and real-world data. [Means for solving the problem]

[0006] The system according to the embodiment includes an image input unit, a real environment data input unit, a layout generation unit, and a product information output unit. The image input unit inputs an image of a room desired by a user. The real environment data input unit inputs real environment data of the target room. The layout generation unit generates a layout based on the data input by the image input unit and the real environment data input unit. The product information output unit outputs specific product information based on the layout generated by the layout generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an optimal layout based on the user's desired image of the room and real-world data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The layout proposal system according to an embodiment of the present invention is a system in which a user inputs an image of a desired room, and a generation AI proposes an optimal layout based on real-world data. This allows the layout proposal system to propose an optimal layout based on the user's wishes and the real-world environment.

[0029] A layout proposal system according to an embodiment includes an image input unit, a real-world data input unit, a layout generation unit, and a product information output unit. The image input unit inputs a user's desired image of a room. For example, the user may input, "I want a modern style for my living room." The image input unit may also input, "I want more storage space in my kitchen." The image input unit may also input, "I want a natural atmosphere." The real-world data input unit inputs real-world data of the target room. For example, LiDAR data may be used to obtain the exact dimensions of the room. The real-world data input unit may also use images and videos to grasp the color and lighting conditions of the room. The real-world data input unit may also grasp the layout of existing furniture. The layout generation unit generates a layout based on the data input by the image input unit and the real-world data input unit. For example, the generation AI generates an optimal layout based on the user's desired image and real-world data. The layout generation unit may also make specific suggestions, such as "Place the sofa by the window and hang the TV on the wall." The layout generation unit can also use the generation AI to make suggestions such as "Place the dining table in the center and place the chairs around it." The product information output unit outputs specific product information based on the layout generated by the layout generation unit. For example, the product information output unit provides specific product information in the form of "This sofa is model X from manufacturer A." The product information output unit can also provide product information in the form of "This television is model Y from manufacturer B." The product information output unit can also provide product information in the form of "This dining table is model Z from manufacturer C." This allows the layout proposal system to propose an optimal layout based on the user's wishes and the actual environment.

[0030] The image input unit analyzes a user's past chat history, learns their preferences and tendencies, and can make more personalized layout suggestions. For example, the image input unit analyzes a user's past chat history and learns their preferences and tendencies. For example, based on previously entered requests such as "modern style" or "I want more storage space," the generation AI proposes a layout that suits the user's preferences. The image input unit also extracts the user's preferences from the chat history and makes personalized layout suggestions. For example, if a user previously inputs that they "like a natural atmosphere," the image input unit will propose a layout that makes extensive use of wood. The image input unit also analyzes a user's chat history and learns specific keywords and phrases. For example, based on a request such as "I want a spacious space," the generation AI will propose a spacious layout. This makes it possible to make more personalized layout suggestions based on the user's past chat history.

[0031] The image input unit allows the generation AI to automatically present related inspiration images and design examples in response to an image entered by the user, thereby expanding the options available. For example, if a user enters "modern living room," the image input unit automatically presents related inspiration images and design examples. For example, it may display multiple images of modern living rooms for the user to choose from. If a user enters "I want to increase storage space," the image input unit automatically presents related design examples. For example, it may display specific layout examples for increasing storage space. If a user enters "natural atmosphere," the image input unit automatically presents related inspiration images. For example, it may display multiple images of natural designs that make extensive use of wood. This allows the generation AI to expand the options available by presenting related inspiration images and design examples in response to the user's input.

[0032] The real environment data input unit collects environmental data such as temperature and humidity in addition to LiDAR data, and can propose layouts that take comfort into consideration. The real environment data input unit, for example, collects environmental data such as temperature and humidity in addition to LiDAR data, and proposes layouts that take comfort into consideration. For example, it proposes a layout that has a cooling effect for a room with a high temperature. The real environment data input unit also proposes layouts that improve comfort based on the environmental data. For example, it proposes a layout that places a dehumidifier in a room with a high humidity. The real environment data input unit also collects environmental data such as temperature and humidity in real time, and proposes layouts that take comfort into consideration. For example, it proposes a layout that places a heater in a room with a low temperature. This makes it possible to propose comfortable layouts that take environmental data such as temperature and humidity into consideration.

[0033] The real environment data input unit updates the real environment data in real time and can propose a layout that corresponds to a dynamically changing environment. The real environment data input unit, for example, updates the real environment data in real time and proposes a layout that corresponds to a dynamically changing environment. For example, different layouts are proposed for daytime and nighttime. The real environment data input unit also proposes dynamically changing layouts based on the environmental data that is updated in real time. For example, different layouts are proposed for each season. The real environment data input unit also collects real environment data in real time and proposes a layout that corresponds to a changing environment. For example, the layout is adjusted according to changes in weather. This makes it possible to propose a layout that corresponds to a dynamically changing environment based on the environmental data that is updated in real time.

[0034] The real environment data input unit can add a function that allows a user to scan a room using a smartphone or tablet using AR technology. The real environment data input unit provides a function that allows a user to scan a room using a smartphone or tablet using AR technology. For example, the real environment data input unit scans the room using a camera to scan the entire image of the room. The real environment data input unit also collects real environment data by scanning the room using a smartphone or tablet. For example, the real environment data input unit uses AR technology to acquire the dimensions and shape of the room. The real environment data input unit also utilizes AR technology to collect real environment data in real time by having the user scan the room using a smartphone or tablet. For example, the color tone and lighting conditions of the room can be grasped. This makes it easier to collect real environment data by having the user scan the room using AR technology.

[0035] The real environment data input unit can integrate data from different rooms and buildings and propose layouts for multiple rooms or entire floors at once. The real environment data input unit, for example, integrates data from different rooms and buildings and proposes layouts for multiple rooms or entire floors at once. For example, it integrates and proposes the layout of a living room and a kitchen. The real environment data input unit also collects data from multiple rooms and floors and proposes an integrated layout. For example, it proposes the layout of an entire office at once. The real environment data input unit also integrates data from different rooms and buildings and proposes an overall layout. For example, it proposes the layout of an entire house at once. This makes it possible to integrate data from different rooms and buildings and propose layouts for multiple rooms or entire floors at once.

[0036] The layout generation unit can add a function that allows users to provide real-time feedback on the layout proposed by the generation AI and instantly modify the layout based on that feedback. The layout generation unit provides, for example, a function that allows users to provide real-time feedback on the layout proposed by the generation AI and instantly modify the layout based on that feedback. For example, if a user inputs, "I want to change the position of the sofa," the generation AI instantly changes the position of the sofa. The layout generation unit also builds a system that allows users to provide real-time feedback and modify the layout based on that feedback. For example, if a user inputs, "Move this table a little more to the left," the generation AI instantly adjusts the position of the table. The layout generation unit also adds a function that allows users to provide real-time feedback on the layout proposed by the generation AI and modify the layout based on that feedback. For example, if a user inputs, "I want to change this lighting," the generation AI instantly changes the position of the lighting. This allows users to provide real-time feedback and instantly modify the layout based on that feedback.

[0037] The layout generation unit allows the generation AI to automatically estimate the cost of a proposed layout and propose the optimal layout according to the budget. For example, if a user inputs "my budget is 100,000 yen," the layout generation unit automatically estimates the cost of the proposed layout and proposes the optimal layout according to the budget. For example, if a user inputs "my budget is 100,000 yen," the system proposes the optimal layout within that budget. The layout generation unit also builds a system that automatically estimates the cost of a layout proposed by the generation AI and proposes a layout according to the budget. For example, if a user inputs "my budget is 50,000 yen," the system proposes the optimal furniture and appliances within that budget. The layout generation unit also adds a function where the generation AI automatically estimates the cost of a proposed layout and proposes the optimal layout according to the budget. For example, if a user inputs "my budget is 200,000 yen," the system proposes the optimal layout within that budget. This allows the generation AI to automatically estimate the cost of a proposed layout and propose the optimal layout according to the budget.

[0038] The layout generation unit can add a function that allows a user to experience a proposed layout in a virtual space using VR technology. The layout generation unit provides, for example, a function that allows a user to experience a proposed layout in a virtual space using VR technology. For example, the user puts on a VR headset and checks the proposed layout in the virtual space. The layout generation unit also uses VR technology to build a system that allows the user to experience the proposed layout in a virtual space. For example, the user checks the furniture arrangement in the virtual space and reflects it in the actual room. The layout generation unit also adds a function that allows a user to experience the proposed layout in a virtual space using VR technology. For example, the user walks around the room layout in the virtual space and checks the actual arrangement. This allows the user to experience the proposed layout in a virtual space using VR technology.

[0039] The layout generation unit can incorporate an ecological perspective into the layout proposal and recommend environmentally friendly materials and energy-efficient home appliances. The layout generation unit, for example, incorporates an ecological perspective into the layout proposal and recommends environmentally friendly materials and energy-efficient home appliances. For example, it proposes furniture made from renewable materials and energy-efficient home appliances. The layout generation unit also builds a system that makes layout proposals taking ecological perspectives into consideration. For example, it prioritizes the proposal of environmentally friendly materials and energy-efficient home appliances. The layout generation unit also adds a function that incorporates an ecological perspective into the layout proposal and recommends environmentally friendly materials and energy-efficient home appliances. For example, if a user inputs "I would like an environmentally friendly layout," a proposal that meets that request is made. This makes it possible to incorporate an ecological perspective into the layout proposal and recommend environmentally friendly materials and energy-efficient home appliances.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The layout proposal system can further include a voice input unit. The voice input unit allows the user to input the image of the desired room by voice. For example, the user can input by voice, "I want a modern style for the living room." The voice input unit can also input by voice, "I want more storage space in the kitchen." The voice input unit can also input by voice, "I want a natural atmosphere." This allows the user to input their preferences by voice without using a keyboard, allowing for more intuitive operation.

[0042] The layout proposal system may further include a health data input unit that acquires the user's health data. The health data input unit acquires, for example, the user's heart rate and stress level and proposes a layout based on this. For example, if the user's heart rate is high, a layout that promotes relaxation may be proposed. The health data input unit may also acquire the user's sleep data and propose a layout that provides a comfortable sleeping environment. This makes it possible to propose an optimal layout based on the user's health condition.

[0043] The layout proposal system can further include a lifestyle data input unit that acquires lifestyle data of the user. The lifestyle data input unit acquires, for example, data on the user's hobbies and daily activities, and proposes a layout based on this. For example, for a user whose hobby is reading, a layout that includes a reading space can be proposed. The lifestyle data input unit can also acquire the user's exercise habits and propose a layout that includes an exercise space. This makes it possible to propose an optimal layout that matches the user's lifestyle.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The image input unit inputs the user's desired image of the room. For example, the user may input, "I want a modern style for my living room." The user can also input, "I want more storage space in the kitchen" or "I want a natural atmosphere." Step 2: The real-world data input unit inputs the real-world data of the target room. For example, LiDAR data can be used to obtain the exact dimensions of the room. Images and videos can also be used to understand the color and lighting conditions of the room, and the layout of existing furniture. Step 3: The layout generation unit generates a layout based on the data input by the image input unit and the real-world data input unit. For example, the generation AI generates an optimal layout based on the user's desired image and real-world data. Specific suggestions include "Place the sofa by the window and hang the TV on the wall" or "Place the dining table in the center and place chairs around it." Step 4: The product information output unit outputs specific product information based on the layout generated by the layout generation unit. For example, the product information is provided in the form of "This sofa is model X from manufacturer A," "This TV is model Y from manufacturer B," or "This dining table is model Z from manufacturer C."

[0046] (Example 2) The layout proposal system according to an embodiment of the present invention is a system in which a user inputs an image of a desired room, and a generation AI proposes an optimal layout based on real-world data. This allows the layout proposal system to propose an optimal layout based on the user's wishes and the real-world environment.

[0047] A layout proposal system according to an embodiment includes an image input unit, a real-world data input unit, a layout generation unit, and a product information output unit. The image input unit inputs a user's desired image of a room. For example, the user may input, "I want a modern style for my living room." The image input unit may also input, "I want more storage space in my kitchen." The image input unit may also input, "I want a natural atmosphere." The real-world data input unit inputs real-world data of the target room. For example, LiDAR data may be used to obtain the exact dimensions of the room. The real-world data input unit may also use images and videos to grasp the color and lighting conditions of the room. The real-world data input unit may also grasp the layout of existing furniture. The layout generation unit generates a layout based on the data input by the image input unit and the real-world data input unit. For example, the generation AI generates an optimal layout based on the user's desired image and real-world data. The layout generation unit may also make specific suggestions, such as "Place the sofa by the window and hang the TV on the wall." The layout generation unit can also use the generation AI to make suggestions such as "Place the dining table in the center and place the chairs around it." The product information output unit outputs specific product information based on the layout generated by the layout generation unit. For example, the product information output unit provides specific product information in the form of "This sofa is model X from manufacturer A." The product information output unit can also provide product information in the form of "This television is model Y from manufacturer B." The product information output unit can also provide product information in the form of "This dining table is model Z from manufacturer C." This allows the layout proposal system to propose an optimal layout based on the user's wishes and the actual environment.

[0048] The image input unit analyzes a user's past chat history, learns their preferences and tendencies, and can make more personalized layout suggestions. For example, the image input unit analyzes a user's past chat history and learns their preferences and tendencies. For example, based on previously entered requests such as "modern style" or "I want more storage space," the generation AI proposes a layout that suits the user's preferences. The image input unit also extracts the user's preferences from the chat history and makes personalized layout suggestions. For example, if a user previously inputs that they "like a natural atmosphere," the image input unit will propose a layout that makes extensive use of wood. The image input unit also analyzes a user's chat history and learns specific keywords and phrases. For example, based on a request such as "I want a spacious space," the generation AI will propose a spacious layout. This makes it possible to make more personalized layout suggestions based on the user's past chat history.

[0049] The image input unit allows the generation AI to automatically present related inspiration images and design examples in response to an image entered by the user, thereby expanding the options available. For example, if a user enters "modern living room," the image input unit automatically presents related inspiration images and design examples. For example, it may display multiple images of modern living rooms for the user to choose from. If a user enters "I want to increase storage space," the image input unit automatically presents related design examples. For example, it may display specific layout examples for increasing storage space. If a user enters "natural atmosphere," the image input unit automatically presents related inspiration images. For example, it may display multiple images of natural designs that make extensive use of wood. This allows the generation AI to expand the options available by presenting related inspiration images and design examples in response to the user's input.

[0050] The image input unit can use the emotion estimation function to analyze the emotion of the user when entering text and make suggestions that will elicit positive emotions. The image input unit, for example, analyzes the emotion of the user when entering text in a chat and makes suggestions that will elicit positive emotions. For example, if the user enters "tired," the image input unit suggests a layout that will help them relax. The image input unit also uses the emotion estimation function to analyze the emotion of the user when entering text in real time. For example, if the user enters "fun," the image input unit suggests a layout with a bright and cheerful atmosphere. The image input unit also analyzes the user's emotion and makes suggestions that will elicit positive emotions. For example, if the user enters "feeling stressed," the image input unit suggests a relaxing layout that will help reduce stress. This makes it possible to analyze the user's emotion and make suggestions that will elicit positive emotions.

[0051] The real environment data input unit collects environmental data such as temperature and humidity in addition to LiDAR data, and can propose layouts that take comfort into consideration. The real environment data input unit, for example, collects environmental data such as temperature and humidity in addition to LiDAR data, and proposes layouts that take comfort into consideration. For example, it proposes a layout that has a cooling effect for a room with a high temperature. The real environment data input unit also proposes layouts that improve comfort based on the environmental data. For example, it proposes a layout that places a dehumidifier in a room with a high humidity. The real environment data input unit also collects environmental data such as temperature and humidity in real time, and proposes layouts that take comfort into consideration. For example, it proposes a layout that places a heater in a room with a low temperature. This makes it possible to propose comfortable layouts that take environmental data such as temperature and humidity into consideration.

[0052] The real environment data input unit updates the real environment data in real time and can propose a layout that corresponds to a dynamically changing environment. The real environment data input unit, for example, updates the real environment data in real time and proposes a layout that corresponds to a dynamically changing environment. For example, different layouts are proposed for daytime and nighttime. The real environment data input unit also proposes dynamically changing layouts based on the environmental data that is updated in real time. For example, different layouts are proposed for each season. The real environment data input unit also collects real environment data in real time and proposes a layout that corresponds to a changing environment. For example, the layout is adjusted according to changes in weather. This makes it possible to propose a layout that corresponds to a dynamically changing environment based on the environmental data that is updated in real time.

[0053] The real environment data input unit can use the emotion estimation function to estimate the user's emotion based on the room's environmental data and propose a layout according to the emotion. The real environment data input unit, for example, estimates the user's emotion based on the room's environmental data and proposes a layout according to the emotion. For example, a layout that allows for relaxation is proposed in a room with a high temperature. The real environment data input unit also uses the emotion estimation function to analyze the user's emotion based on the environmental data. For example, a layout that reduces stress is proposed in a room with high humidity. The real environment data input unit also estimates the user's emotion based on the environmental data and proposes a layout according to the emotion. For example, a layout with a bright atmosphere is proposed in a room with dim lighting. This makes it possible to estimate the user's emotion based on the environmental data and propose a layout according to the emotion.

[0054] The real environment data input unit can add a function that allows a user to scan a room using a smartphone or tablet using AR technology. The real environment data input unit provides a function that allows a user to scan a room using a smartphone or tablet using AR technology. For example, the real environment data input unit scans the room using a camera to scan the entire image of the room. The real environment data input unit also collects real environment data by scanning the room using a smartphone or tablet. For example, the real environment data input unit uses AR technology to acquire the dimensions and shape of the room. The real environment data input unit also utilizes AR technology to collect real environment data in real time by having the user scan the room using a smartphone or tablet. For example, the color tone and lighting conditions of the room can be grasped. This makes it easier to collect real environment data by having the user scan the room using AR technology.

[0055] The real environment data input unit can integrate data from different rooms and buildings and propose layouts for multiple rooms or entire floors at once. The real environment data input unit, for example, integrates data from different rooms and buildings and proposes layouts for multiple rooms or entire floors at once. For example, it integrates and proposes the layout of a living room and a kitchen. The real environment data input unit also collects data from multiple rooms and floors and proposes an integrated layout. For example, it proposes the layout of an entire office at once. The real environment data input unit also integrates data from different rooms and buildings and proposes an overall layout. For example, it proposes the layout of an entire house at once. This makes it possible to integrate data from different rooms and buildings and propose layouts for multiple rooms or entire floors at once.

[0056] The real environment data input unit can use the emotion estimation function to collect the user's emotional responses to different environmental data and propose optimal environmental conditions. The real environment data input unit, for example, uses the emotion estimation function to collect the user's emotional responses to different environmental data and propose optimal environmental conditions. For example, the real environment data input unit analyzes the user's emotional responses to temperature and humidity. The real environment data input unit also proposes optimal environmental conditions based on the user's emotional response data. For example, the real environment data input unit collects the user's emotional responses to lighting brightness and color. The real environment data input unit also uses the emotion estimation function to collect the user's emotional responses to different environmental data in real time and propose optimal environmental conditions. For example, the real environment data input unit analyzes the emotional responses to acoustic environments. This allows the user's emotional responses to different environmental data to be collected and optimal environmental conditions to be proposed.

[0057] The layout generation unit can add a function that allows users to provide real-time feedback on the layout proposed by the generation AI and instantly modify the layout based on that feedback. The layout generation unit provides, for example, a function that allows users to provide real-time feedback on the layout proposed by the generation AI and instantly modify the layout based on that feedback. For example, if a user inputs, "I want to change the position of the sofa," the generation AI instantly changes the position of the sofa. The layout generation unit also builds a system that allows users to provide real-time feedback and modify the layout based on that feedback. For example, if a user inputs, "Move this table a little more to the left," the generation AI instantly adjusts the position of the table. The layout generation unit also adds a function that allows users to provide real-time feedback on the layout proposed by the generation AI and modify the layout based on that feedback. For example, if a user inputs, "I want to change this lighting," the generation AI instantly changes the position of the lighting. This allows users to provide real-time feedback and instantly modify the layout based on that feedback.

[0058] The layout generation unit allows the generation AI to automatically estimate the cost of a proposed layout and propose the optimal layout according to the budget. For example, if a user inputs "my budget is 100,000 yen," the layout generation unit automatically estimates the cost of the proposed layout and proposes the optimal layout according to the budget. For example, if a user inputs "my budget is 100,000 yen," the system proposes the optimal layout within that budget. The layout generation unit also builds a system that automatically estimates the cost of a layout proposed by the generation AI and proposes a layout according to the budget. For example, if a user inputs "my budget is 50,000 yen," the system proposes the optimal furniture and appliances within that budget. The layout generation unit also adds a function where the generation AI automatically estimates the cost of a proposed layout and proposes the optimal layout according to the budget. For example, if a user inputs "my budget is 200,000 yen," the system proposes the optimal layout within that budget. This allows the generation AI to automatically estimate the cost of a proposed layout and propose the optimal layout according to the budget.

[0059] The layout generation unit can use the emotion estimation function to analyze the user's emotional response to the proposed layout and preferentially suggest layouts that provide high emotional satisfaction. The layout generation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the proposed layout and preferentially suggest layouts that provide high emotional satisfaction. For example, if the user responds, "I like this layout," the layout generation unit preferentially suggests that layout. The layout generation unit also analyzes the user's emotional response to the proposed layout and builds a system that proposes layouts that provide high emotional satisfaction. For example, if the user responds, "I like this design," the layout generation unit preferentially suggests that design. The layout generation unit also uses the emotion estimation function to analyze the user's emotional response to the proposed layout in real time and preferentially suggest layouts that provide high emotional satisfaction. For example, if the user responds, "This layout is great," the layout generation unit preferentially suggests that layout. In this way, the user's emotional response to the proposed layout can be analyzed and the layout that provides high emotional satisfaction can be preferentially suggested.

[0060] The layout generation unit can add a function that allows a user to experience a proposed layout in a virtual space using VR technology. The layout generation unit provides, for example, a function that allows a user to experience a proposed layout in a virtual space using VR technology. For example, the user puts on a VR headset and checks the proposed layout in the virtual space. The layout generation unit also uses VR technology to build a system that allows the user to experience the proposed layout in a virtual space. For example, the user checks the furniture arrangement in the virtual space and reflects it in the actual room. The layout generation unit also adds a function that allows a user to experience the proposed layout in a virtual space using VR technology. For example, the user walks around the room layout in the virtual space and checks the actual arrangement. This allows the user to experience the proposed layout in a virtual space using VR technology.

[0061] The layout generation unit can incorporate an ecological perspective into the layout proposal and recommend environmentally friendly materials and energy-efficient home appliances. The layout generation unit, for example, incorporates an ecological perspective into the layout proposal and recommends environmentally friendly materials and energy-efficient home appliances. For example, it proposes furniture made from renewable materials and energy-efficient home appliances. The layout generation unit also builds a system that makes layout proposals taking ecological perspectives into consideration. For example, it prioritizes the proposal of environmentally friendly materials and energy-efficient home appliances. The layout generation unit also adds a function that incorporates an ecological perspective into the layout proposal and recommends environmentally friendly materials and energy-efficient home appliances. For example, if a user inputs "I would like an environmentally friendly layout," a proposal that meets that request is made. This makes it possible to incorporate an ecological perspective into the layout proposal and recommend environmentally friendly materials and energy-efficient home appliances.

[0062] The layout generation unit can use the emotion estimation function to compare the user's emotional responses to different layout proposals and designate the layout that receives the most positive response as the final proposal. For example, the layout generation unit can use the emotion estimation function to compare the user's emotional responses to different layout proposals and designate the layout that receives the most positive response as the final proposal. For example, if the user responds, "I like this layout the best," that layout is designated as the final proposal. The layout generation unit can also analyze the user's emotional responses to different layout proposals and build a system that proposes the layout that receives the most positive response. For example, if the user responds, "This layout is the best," that layout is designated as the final proposal. The layout generation unit can also use the emotion estimation function to compare the user's emotional responses to different layout proposals in real time and designate the layout that receives the most positive response as the final proposal. For example, if the user responds, "This design is the best," that design is designated as the final proposal. In this way, the user's emotional responses to different layout proposals can be compared and the layout that receives the most positive response can be designated as the final proposal.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The layout proposal system can further include a voice input unit. The voice input unit allows the user to input the image of the desired room by voice. For example, the user can input by voice, "I want a modern style for the living room." The voice input unit can also input by voice, "I want more storage space in the kitchen." The voice input unit can also input by voice, "I want a natural atmosphere." This allows the user to input their preferences by voice without using a keyboard, allowing for more intuitive operation.

[0065] The layout proposal system may further include a health data input unit that acquires the user's health data. The health data input unit acquires, for example, the user's heart rate and stress level and proposes a layout based on this. For example, if the user's heart rate is high, a layout that promotes relaxation may be proposed. The health data input unit may also acquire the user's sleep data and propose a layout that provides a comfortable sleeping environment. This makes it possible to propose an optimal layout based on the user's health condition.

[0066] The layout proposal system can further include a lifestyle data input unit that acquires lifestyle data of the user. The lifestyle data input unit acquires, for example, data on the user's hobbies and daily activities, and proposes a layout based on this. For example, for a user whose hobby is reading, a layout that includes a reading space can be proposed. The lifestyle data input unit can also acquire the user's exercise habits and propose a layout that includes an exercise space. This makes it possible to propose an optimal layout that matches the user's lifestyle.

[0067] The layout proposal system may further include an emotion estimation unit that estimates a user's emotion and proposes a layout based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or voice tone to estimate the emotion. For example, if the user is tired, the emotion estimation unit may propose a layout that allows the user to relax. The emotion estimation unit may also analyze the user's emotion in real time and propose a layout according to the emotion. This makes it possible to propose an optimal layout according to the user's emotion.

[0068] The layout proposal system may further include an emotion estimation unit that estimates a user's emotion and proposes a layout based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or voice tone to estimate the emotion. For example, if the user is tired, the emotion estimation unit may propose a layout that allows the user to relax. The emotion estimation unit may also analyze the user's emotion in real time and propose a layout according to the emotion. This makes it possible to propose an optimal layout according to the user's emotion.

[0069] The layout proposal system may further include an emotion estimation unit that estimates a user's emotion and proposes a layout based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or voice tone to estimate the emotion. For example, if the user is tired, the emotion estimation unit may propose a layout that allows the user to relax. The emotion estimation unit may also analyze the user's emotion in real time and propose a layout according to the emotion. This makes it possible to propose an optimal layout according to the user's emotion.

[0070] The layout proposal system may further include an emotion estimation unit that estimates a user's emotion and proposes a layout based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or voice tone to estimate the emotion. For example, if the user is tired, the emotion estimation unit may propose a layout that allows the user to relax. The emotion estimation unit may also analyze the user's emotion in real time and propose a layout according to the emotion. This makes it possible to propose an optimal layout according to the user's emotion.

[0071] The layout proposal system may further include an emotion estimation unit that estimates a user's emotion and proposes a layout based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or voice tone to estimate the emotion. For example, if the user is tired, the emotion estimation unit may propose a layout that allows the user to relax. The emotion estimation unit may also analyze the user's emotion in real time and propose a layout according to the emotion. This makes it possible to propose an optimal layout according to the user's emotion.

[0072] The layout proposal system may further include an emotion estimation unit that estimates a user's emotion and proposes a layout based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or voice tone to estimate the emotion. For example, if the user is tired, the emotion estimation unit may propose a layout that allows the user to relax. The emotion estimation unit may also analyze the user's emotion in real time and propose a layout according to the emotion. This makes it possible to propose an optimal layout according to the user's emotion.

[0073] The layout proposal system may further include an emotion estimation unit that estimates a user's emotion and proposes a layout based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or voice tone to estimate the emotion. For example, if the user is tired, the emotion estimation unit may propose a layout that allows the user to relax. The emotion estimation unit may also analyze the user's emotion in real time and propose a layout according to the emotion. This makes it possible to propose an optimal layout according to the user's emotion.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The image input unit inputs the user's desired image of the room. For example, the user may input, "I want a modern style for my living room." The user can also input, "I want more storage space in the kitchen" or "I want a natural atmosphere." Step 2: The real-world data input unit inputs the real-world data of the target room. For example, LiDAR data can be used to obtain the exact dimensions of the room. Images and videos can also be used to understand the color and lighting conditions of the room, and the layout of existing furniture. Step 3: The layout generation unit generates a layout based on the data input by the image input unit and the real-world data input unit. For example, the generation AI generates an optimal layout based on the user's desired image and real-world data. Specific suggestions include "Place the sofa by the window and hang the TV on the wall" or "Place the dining table in the center and place chairs around it." Step 4: The product information output unit outputs specific product information based on the layout generated by the layout generation unit. For example, the product information is provided in the form of "This sofa is model X from manufacturer A," "This TV is model Y from manufacturer B," or "This dining table is model Z from manufacturer C."

[0076] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0077] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0078] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0082] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0083] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0084] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0085] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0086] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0087] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0090] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0117] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0126] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0127] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0128] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0129] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0130] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0131] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0132] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0133] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0134] 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.

[0135] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0136] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0137] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0138] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0139] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0140] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0141] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0142] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image input unit for inputting an image of a room desired by a user; a real environment data input unit for inputting real environment data of a target room; a layout generation unit that generates a layout based on the data input by the image input unit and the real environment data input unit; a product information output unit that outputs specific product information based on the layout generated by the layout generation unit. A system characterized by:

2. The image input unit Analyzes the user's past chat history, learns their preferences and tendencies, and proposes more personalized layouts.

2. The system of claim 1.

3. The image input unit When a user inputs an image, the AI ​​automatically suggests related inspiration images and design examples, expanding the options available.

2. The system of claim 1.

4. The image input unit Analyzes the user's emotions when typing and makes suggestions that elicit positive emotions.

2. The system of claim 1.

Images