System

Abstract

An object of a system according to an embodiment is to support a user to easily create an ideal room.SOLUTION: A system according to an embodiment includes a current status analysis unit, a proposal unit, a visualization unit, a purchase list generation unit, and an execution support unit. The current state analyzing unit analyzes a current state of the room by using the generated AI. The proposal unit proposes an ideal room for the user on the basis of the current state of the room analyzed by the current state analysis unit. The visualization unit visualizes the layout and interior proposed by the proposal unit. The purchase list generation unit generates a purchase list of the interior and the furniture proposed by the proposal unit. The execution support unit supports execution of the content proposed by the proposal 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 lack specific suggestions and support to help users realize their ideal room, and there is room for improvement.

[0005] The system according to the embodiment aims to support users in easily creating their ideal room. [Means for solving the problem]

[0006] The system according to the embodiment includes a current situation analysis unit, a proposal unit, a visualization unit, a purchase list generation unit, and an execution support unit. The current situation analysis unit analyzes the current situation of the room using a generation AI. The proposal unit proposes the user's ideal room based on the current situation of the room analyzed by the current situation analysis unit. The visualization unit visualizes the layout and interior proposed by the proposal unit. The purchase list generation unit generates a purchase list of the interior and furniture proposed by the proposal unit. The execution support unit provides support when implementing the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can support the user in easily creating their ideal room. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The room support system according to the embodiment of the present invention is a system that analyzes the current state of a room and proposes a room layout and interior design based on the user's ideals, thereby enabling the user to easily create their ideal room.

[0029] A room support system according to an embodiment includes a current state analysis unit, a proposal unit, a visualization unit, a purchase list generation unit, and an execution support unit. The current state analysis unit analyzes the current state of a room using a generation AI. For example, the current state analysis unit analyzes information such as room photos and dimensions provided by a user to determine the room's size, furniture layout, wall color, etc. The proposal unit proposes the user's ideal room based on the current state of the room analyzed by the current state analysis unit. For example, the proposal unit collects information about the user's ideal room and proposes the room's layout and interior design based on that information. The visualization unit visualizes the layout and interior design proposed by the proposal unit. For example, the visualization unit uses 3D models and simulations to enable the user to visually confirm the proposal content. The purchase list generation unit generates a purchase list of the interior design and furniture proposed by the proposal unit. For example, the purchase list generation unit lists detailed information and purchasing locations of the proposed furniture and interior design, allowing the user to easily purchase them. The execution support unit provides support for implementing the content proposed by the proposal unit. For example, the execution support unit specifically instructs the user on how to arrange furniture and the procedure for installing interior items, thereby enabling the room support system according to the embodiment to easily create the user's ideal room.

[0030] The current situation analysis unit analyzes information such as photos and dimensions of the room, and can grasp the size of the room, the arrangement of furniture, the color of the walls, and so on. For example, the current situation analysis unit analyzes photos of the room and identifies the material of the furniture. For example, it identifies materials such as wood, metal, plastic, etc., and estimates the number of years of use based on that. The current situation analysis unit also analyzes scratches and fading on the surface of the furniture to estimate the number of years of use. For example, it evaluates the degree of fading of wooden furniture and the degree of rust on metal furniture. The current situation analysis unit also analyzes structural damage and deformation of the furniture to evaluate the degree of deterioration. For example, it detects bent chair legs and cracks on the surface of a table. This allows the current state of the room to be accurately grasped.

[0031] The suggestion unit can collect information about the user's ideal room and, based on that, suggest room layouts and interiors. The suggestion unit, for example, analyzes how light enters and the position of shadows from a photo of the room and suggests optimal lighting arrangements. For example, the suggestion unit analyzes a photo of the room and identifies the position and size of windows. This allows the way natural light enters to be understood. Furthermore, to analyze how light enters, the suggestion unit measures the brightness of each part of the room and identifies the position of shadows. For example, it simulates changes in light during the day. Furthermore, to suggest optimal lighting arrangements, the suggestion unit places appropriate lighting in dark or shadowy parts of the room. For example, it suggests indirect lighting or spotlights. This makes it possible to make suggestions based on the user's ideals.

[0032] The visualization unit can visualize the proposed layout and interior design using a 3D model or simulation. For example, the visualization unit uses an emotion estimation function to analyze the user's emotions regarding the current state of the room and identify stressful elements. For example, the visualization unit analyzes a photo of the room and evaluates the room's color and design elements to estimate the user's emotions. For example, dark colors and crowded layouts can cause stress. The visualization unit also collects the user's reactions to each part of the room to analyze the user's emotions. For example, it identifies the stress the user feels regarding specific furniture or layouts. The visualization unit also analyzes the noise level and temperature in the room to identify stressful elements. For example, it identifies areas with a lot of noise or an uncomfortable temperature. This allows the user to visually confirm the proposed content.

[0033] The shopping list generation unit can list detailed information about the proposed interior and furniture items and where to purchase them. For example, the shopping list generation unit analyzes the acoustic characteristics in a current room analysis and proposes an optimal acoustic environment. For example, the shopping list generation unit evaluates the shape and materials of the room to analyze the acoustic characteristics of the room. For example, it analyzes the effects of wall and floor materials on sound reflection and absorption. The shopping list generation unit also measures the acoustic characteristics of each part of the room to optimize the acoustic environment. For example, it identifies areas with a lot of echo and reverberation. The shopping list generation unit also suggests the placement of acoustic panels and carpets to propose an optimal acoustic environment. For example, it suggests the placement of sound-absorbing material to reduce sound reflection. This allows the user to smoothly gather the necessary items based on the suggestions.

[0034] The execution support unit can provide specific instructions on how to arrange furniture and procedures for setting up interior decor when implementing the suggestions. For example, the execution support unit collects temperature and humidity data in analyzing the current state of a room and makes suggestions for maintaining a comfortable environment. For example, the execution support unit installs sensors to measure the temperature and humidity in the room and collects data. For example, a temperature sensor or a humidity sensor is used. The execution support unit also analyzes the temperature and humidity data and makes suggestions for maintaining a comfortable environment. For example, it suggests using an air conditioner or a humidifier. The execution support unit also analyzes fluctuations in temperature and humidity and suggests optimal settings for each season. For example, it suggests different settings for summer and winter. This allows the user to accurately implement the suggestions.

[0035] The current state analysis unit can analyze information such as photos and dimensions of the room to estimate the material and age of the furniture and evaluate its degree of deterioration. For example, the current state analysis unit analyzes photos of the room to identify the material of the furniture. For example, it identifies materials such as wood, metal, and plastic, and estimates the age based on that. The current state analysis unit also analyzes scratches and discoloration on the surface of the furniture to estimate the age. For example, it evaluates the degree of discoloration on wooden furniture and the degree of rust on metal furniture. The current state analysis unit also analyzes structural damage and deformation of the furniture to evaluate its degree of deterioration. For example, it detects bent chair legs and cracks on the surface of a table. This makes it possible to evaluate the degree of deterioration of the furniture and make appropriate suggestions.

[0036] The current situation analysis unit can analyze how light enters and the location of shadows from a photo of the room and propose the optimal lighting layout. For example, the current situation analysis unit analyzes a photo of the room and identifies the location and size of windows. This allows the unit to understand how natural light enters. In addition, to analyze how light enters, the current situation analysis unit measures the brightness of each part of the room and identifies the location of shadows. For example, it simulates changes in light during the day. In addition, in order to propose the optimal lighting layout, the current situation analysis unit places appropriate lighting in dark or shadowy parts of the room. For example, it proposes indirect lighting or spotlights. This allows the lighting layout of the room to be optimized.

[0037] The current situation analysis unit can analyze the acoustic characteristics of a room and propose an optimal acoustic environment. For example, the current situation analysis unit evaluates the shape and materials of the room to analyze the acoustic characteristics of the room. For example, it analyzes the effect that wall and floor materials have on sound reflection and absorption. The current situation analysis unit also measures the acoustic characteristics of each part of the room to optimize the acoustic environment. For example, it identifies areas with a lot of echo and reverberation. The current situation analysis unit also proposes the placement of acoustic panels and carpets to propose an optimal acoustic environment. For example, it proposes the placement of sound-absorbing materials to reduce sound reflection. This allows the acoustic environment of the room to be optimized.

[0038] The current situation analysis unit can collect data on room temperature and humidity and make suggestions for maintaining a comfortable environment. For example, the current situation analysis unit installs sensors to measure the room temperature and humidity and collects data. For example, it uses temperature sensors and humidity sensors. The current situation analysis unit also analyzes the temperature and humidity data and makes suggestions for maintaining a comfortable environment. For example, it suggests using an air conditioner or humidifier. The current situation analysis unit also analyzes fluctuations in temperature and humidity and suggests optimal settings for each season. For example, it suggests different settings for summer and winter. This allows the room temperature and humidity to be optimized and a comfortable environment to be maintained.

[0039] The suggestion unit can analyze the user's past interior selection history and make suggestions based on their preferences. For example, the suggestion unit collects the user's past interior selection history, and the generation AI analyzes that data. For example, suggestions are made based on data on furniture and interior items purchased in the past. The suggestion unit also analyzes the past selection history to identify the user's preferences. For example, it grasps trends in specific design styles and colors. In addition, the suggestion unit suggests optimal layouts and interior items based on the past selection history in order to make suggestions based on the user's preferences. For example, it makes suggestions that match the user's preferred style. This makes it possible to make suggestions based on the user's preferences.

[0040] The suggestion unit can analyze the user's lifestyle and daily movement lines and propose optimal furniture layouts. The suggestion unit, for example, analyzes daily movement lines to understand the user's lifestyle. For example, it identifies movement patterns within a room and frequently used areas. The suggestion unit also analyzes daily movement lines and proposes optimal furniture layouts. For example, it arranges furniture so as not to obstruct movement lines. The suggestion unit also proposes optimal layouts based on movement line data to make proposals tailored to the user's lifestyle. For example, it arranges necessary furniture in frequently used areas. This makes it possible to arrange furniture optimally based on the user's lifestyle.

[0041] The suggestion unit can suggest interior changes for each season to create a sense of the season. For example, the suggestion unit collects design elements according to the season in order to suggest interior changes for each season. For example, bright colors and floral designs are suggested for spring. The suggestion unit also suggests interior changes for each season to create a sense of the season. For example, cool designs and materials are suggested for summer. The suggestion unit also suggests furniture and decorations according to the season in order to suggest interior changes for each season. For example, warm designs and materials are suggested for winter. This makes it possible to make interior changes that create a sense of the season.

[0042] The proposal department can recommend eco-friendly materials and products and make environmentally conscious proposals. For example, the proposal department collects eco-friendly materials and products and incorporates them into proposals for ideal rooms. For example, it proposes renewable materials and energy-efficient products. In addition, the proposal department recommends eco-friendly materials and products in order to make environmentally conscious proposals. For example, it proposes furniture made with low-VOC (volatile organic compound) paints and recycled materials. In addition, the proposal department incorporates environmentally conscious design elements in order to recommend eco-friendly materials and products. For example, it proposes natural materials and energy-efficient lighting. This makes it possible to make environmentally conscious proposals.

[0043] The visualization unit can display the proposal content in AR, allowing the user to check it in the actual room. For example, to display the proposal content in AR, the visualization unit scans the current state of the room and overlays the proposed layout and interior. For example, the AR display is performed using a smartphone or tablet. The visualization unit also uses AR technology to display the proposal content in real time, allowing the user to check it in the actual room. For example, it simulates furniture placement and color changes using AR. Furthermore, to display the proposal content in AR, the visualization unit allows the user to check it while walking around the room. For example, the proposal content is visually confirmed using AR glasses. This allows the user to check the proposal content in the actual room.

[0044] The visualization unit allows the user to experience the proposal content in VR, allowing the user to walk around the room in a virtual space. For example, the visualization unit creates a 3D model of the room so that the user can experience the proposal content in VR, allowing the user to walk around in the virtual space. For example, the proposal content is experienced using a VR headset. The visualization unit also displays the room in real time using VR technology so that the user can walk around in the virtual space. For example, the visualization unit simulates furniture placement and color changes in VR. The visualization unit also allows the user to freely move around the room and check the proposal content so that the user can experience it in VR. For example, the user can walk around the room using a VR controller. This allows the user to experience the proposal content in a virtual space.

[0045] The visualization unit can compare and display different design styles when visualizing the proposal content, allowing the user to select one. For example, the visualization unit compares and displays different design styles when visualizing the proposal content. For example, styles such as modern, classic, and minimalist are simultaneously displayed. The visualization unit also displays the visualized proposal content interactively so that the user can select a different design style. For example, the style can be switched by clicking or tapping. The visualization unit also visualizes the features and advantages of each style to compare and display different design styles. For example, the color usage and furniture arrangement of each style are displayed in detail. This allows the user to compare and select different design styles.

[0046] The visualization unit can simulate how the content of a proposal looks at different times of the day and night when visualizing the content of a proposal. For example, the visualization unit simulates how the content of a proposal looks at different times of the day and night when visualizing the content of a proposal. For example, the visualization unit displays the difference between natural light during the day and artificial lighting at night. The visualization unit also changes the lighting conditions in the room to simulate how the content of a proposal looks at different times of the day and night. For example, the visualization unit reproduces the brightness of daytime and the darkness of nighttime. The visualization unit also interactively displays the visualized content of a proposal so that the user can check how the content of a proposal looks at different times of the day and night. For example, the visualization unit uses a slider to switch between different time periods. This allows for a simulation of how the content of a proposal looks at different times of the day and night.

[0047] The shopping list generation unit compares the prices of the proposed furniture and interior items and can suggest the optimal purchasing source. For example, the shopping list generation unit collects the prices of the proposed furniture and interior items, and the generation AI performs the price comparison. For example, it collects price data from multiple online stores. The shopping list generation unit also collects detailed information about the proposed furniture and interior items to compare prices. For example, it evaluates prices based on product specifications and reviews. The shopping list generation unit also presents the most cost-effective option based on the price comparison data to suggest the optimal purchasing source. For example, it considers not only price but also delivery conditions and warranty details. This allows it to suggest the optimal purchasing source.

[0048] The shopping list generation unit can include reviews and ratings of the proposed items in the shopping list for user reference. The shopping list generation unit, for example, collects reviews and ratings of the proposed items and includes them in the shopping list. For example, it collects data from online stores and review sites. The shopping list generation unit also displays the reviews and ratings of the proposed items in the shopping list for user reference. For example, it displays star ratings and user comments. The shopping list generation unit also allows the user to make an optimal selection based on the reviews and ratings of the proposed items. For example, it prioritizes the inclusion of highly rated items in the list. This allows the user to refer to the reviews and ratings.

[0049] The shopping list generator can include alternatives and options for the proposed items in the shopping list, allowing the user to have choices. For example, the shopping list generator collects alternatives and options for the proposed items and includes them in the shopping list. For example, different brands or models in the same category are suggested. The shopping list generator also displays alternatives and options in the shopping list, allowing the user to have choices. For example, the shopping list generator allows the user to compare differences in price or functionality. The shopping list generator also allows the user to make the best choice based on the alternatives and options for the proposed items. For example, the shopping list generator presents options that match the user's preferences and budget. This allows the user to have choices.

[0050] The purchase list generation unit can include installation and maintenance information for the items proposed in the purchase list, allowing the user to use the items for a long period of time. The purchase list generation unit, for example, collects installation and maintenance information for the proposed items and includes it in the purchase list. For example, it describes assembly procedures and maintenance frequency. The purchase list generation unit also displays the installation and maintenance information in the purchase list, allowing the user to use the items for a long period of time. For example, it provides an installation video or a maintenance guide. The purchase list generation unit also allows the user to make an optimal selection based on the installation and maintenance information for the proposed items. For example, it prioritizes items that are easy to maintain in the list. This allows the user to use the items for a long period of time.

[0051] The execution support unit can use the generation AI to provide furniture assembly procedures in the form of a video, allowing the user to easily assemble the furniture. For example, in order to provide furniture assembly procedures in the form of a video, the execution support unit uses the generation AI to analyze the assembly procedures and create a video. For example, a video is provided that explains each step in detail. The execution support unit also visually explains the assembly procedures in the video, allowing the user to easily assemble the furniture. For example, a video may show how to attach parts and how to use tools. In addition, in order to provide assembly procedures in the form of a video, the execution support unit allows the user to proceed with the assembly while watching the video. For example, the video can be paused as the user proceeds with the work. This allows the user to easily assemble the furniture.

[0052] The execution support unit can simulate the proposed layout and confirm the optimal arrangement. For example, to simulate the proposed layout, the execution support unit uses a generation AI to create a 3D model of the room and conduct the simulation. For example, it simulates furniture placement and traffic flow. The execution support unit also visually displays the simulation results so that the user can confirm the optimal placement. For example, it confirms changes to furniture placement and color through simulation. The execution support unit also allows the user to adjust the placement while viewing the simulation results in order to simulate the proposed layout. For example, it changes the position of furniture based on the simulation results. This allows the user to confirm the optimal placement.

[0053] The execution support unit can provide online consultation with a professional interior designer, allowing the user to receive professional advice. For example, the execution support unit introduces a video call or chat function to provide online consultation with a professional interior designer. For example, the user can consult with the designer in real time. The execution support unit also allows the interior designer to make suggestions based on photos and dimensions of the user's room so that the user can receive professional advice. For example, the designer may propose specific layouts and interiors. The execution support unit also allows the user to receive feedback from the designer through the online consultation and adjust the proposal. For example, the designer may propose customizations tailored to the user's wishes. This allows the user to receive professional advice.

[0054] The execution support unit can provide a function that allows the proposed layout to be changed or fine-tuned in real time. The execution support unit, for example, develops interactive tools to provide a function that allows the proposed layout to be changed or fine-tuned in real time. For example, the execution support unit allows the user to change the furniture arrangement by drag and drop. The execution support unit also allows the generation AI to instantly propose a new layout so that the user can change the proposed layout in real time. For example, when the user changes the position of furniture, the generation AI proposes a new arrangement. The execution support unit also provides a function that allows the user to make adjustments while checking the changes so that the proposed layout can be fine-tuned in real time. For example, the changed layout can be displayed as a 3D model. This allows the layout to be changed or fine-tuned in real time.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The suggestion unit can analyze the user's lifestyle and daily movement lines and propose optimal furniture layouts. For example, the suggestion unit analyzes daily movement lines to understand the user's lifestyle. For example, it identifies movement patterns within a room and frequently used areas. The suggestion unit also analyzes daily movement lines and proposes optimal furniture layouts. For example, it arranges furniture so as not to obstruct movement lines. The suggestion unit also proposes optimal layouts based on movement line data to make proposals tailored to the user's lifestyle. For example, it arranges necessary furniture in frequently used areas. This makes it possible to arrange furniture optimally based on the user's lifestyle.

[0057] The suggestion unit can suggest interior changes for each season to create a sense of the season. For example, the suggestion unit collects design elements according to the season in order to suggest interior changes for each season. For example, bright colors and floral designs are suggested for spring. The suggestion unit also suggests interior changes for each season to create a sense of the season. For example, cool designs and materials are suggested for summer. The suggestion unit also suggests furniture and decorations according to the season in order to suggest interior changes for each season. For example, warm designs and materials are suggested for winter. This makes it possible to make interior changes that create a sense of the season.

[0058] The proposal department can recommend eco-friendly materials and products and make environmentally conscious proposals. For example, the proposal department collects eco-friendly materials and products and incorporates them into proposals for ideal rooms. For example, it proposes renewable materials and energy-efficient products. The proposal department also recommends eco-friendly materials and products in order to make environmentally conscious proposals. For example, it proposes furniture made with low-VOC (volatile organic compound) paints and recycled materials. The proposal department also incorporates environmentally conscious design elements in order to recommend eco-friendly materials and products. For example, it proposes natural materials and energy-efficient lighting. This makes it possible to make environmentally conscious proposals.

[0059] The suggestion unit can analyze the user's past interior selection history and make suggestions based on their preferences. For example, the suggestion unit collects the user's past interior selection history, and the generation AI analyzes that data. For example, suggestions are made based on data on furniture and interior items purchased in the past. The suggestion unit also analyzes the past selection history to identify the user's preferences. For example, it grasps trends in specific design styles and colors. In addition, the suggestion unit suggests optimal layouts and interior items based on the past selection history in order to make suggestions based on the user's preferences. For example, it makes suggestions that match the user's preferred style. This makes it possible to make suggestions based on the user's preferences.

[0060] The visualization unit can display the proposal content in AR, allowing the user to check it in the actual room. For example, to display the proposal content in AR, the visualization unit scans the current state of the room and overlays the proposed layout and interior. For example, the AR display is performed using a smartphone or tablet. The visualization unit also uses AR technology to display the proposal content in real time, allowing the user to check it in the actual room. For example, it simulates furniture placement and color changes using AR. The visualization unit also displays the proposal content in AR, allowing the user to check it while walking around the room. For example, the proposal content is visually confirmed using AR glasses. This allows the user to check the proposal content in the actual room.

[0061] The visualization unit allows the user to experience the proposal content in VR, allowing the user to walk around the room in a virtual space. For example, the visualization unit creates a 3D model of the room so that the user can experience the proposal content in VR, allowing the user to walk around in the virtual space. For example, the proposal content is experienced using a VR headset. The visualization unit also uses VR technology to display the room in real time so that the user can walk around in the virtual space. For example, the visualization unit simulates furniture placement and color changes in VR. The visualization unit also allows the user to freely move around the room and check the proposal content so that the user can experience it in VR. For example, the user can walk around the room using a VR controller. This allows the user to experience the proposal content in a virtual space.

[0062] When visualizing the proposal content, the visualization unit can compare and display different design styles to allow the user to select one. For example, when visualizing the proposal content, the visualization unit compares and displays different design styles. For example, styles such as modern, classic, and minimalist are simultaneously displayed. The visualization unit also displays the visualized proposal content interactively so that the user can select a different design style. For example, the style can be switched by clicking or tapping. The visualization unit also visualizes the features and advantages of each style to compare and display different design styles. For example, the color usage and furniture arrangement of each style are displayed in detail. This allows the user to compare and select different design styles.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The current state analysis unit uses the generative AI to analyze the current state of the room. For example, it analyzes information such as photos and dimensions of the room provided by the user to determine the size of the room, furniture layout, wall color, etc. Step 2: The proposal unit proposes the user's ideal room based on the current state of the room analyzed by the current state analysis unit. For example, it collects information about the user's ideal room and proposes the room layout and interior based on that information. Step 3: The visualization unit visualizes the layout and interior proposed by the proposal unit, for example, using a 3D model or simulation to allow the user to visually confirm the proposal. Step 4: The purchase list generation unit generates a purchase list of the interior and furniture items suggested by the suggestion unit. For example, it lists detailed information about the suggested furniture and interior items and where to purchase them, making it easy for the user to purchase them. Step 5: The implementation support section provides support for implementing the proposals made by the proposal section, for example by providing specific instructions on how to arrange furniture and the steps for installing interior items.

[0065] (Example 2) The room support system according to the embodiment of the present invention is a system that analyzes the current state of a room and proposes a room layout and interior design based on the user's ideals, thereby enabling the user to easily create their ideal room.

[0066] A room support system according to an embodiment includes a current state analysis unit, a proposal unit, a visualization unit, a purchase list generation unit, and an execution support unit. The current state analysis unit analyzes the current state of a room using a generation AI. For example, the current state analysis unit analyzes information such as room photos and dimensions provided by a user to determine the room's size, furniture layout, wall color, etc. The proposal unit proposes the user's ideal room based on the current state of the room analyzed by the current state analysis unit. For example, the proposal unit collects information about the user's ideal room and proposes the room's layout and interior design based on that information. The visualization unit visualizes the layout and interior design proposed by the proposal unit. For example, the visualization unit uses 3D models and simulations to enable the user to visually confirm the proposal content. The purchase list generation unit generates a purchase list of the interior design and furniture proposed by the proposal unit. For example, the purchase list generation unit lists detailed information and purchasing locations of the proposed furniture and interior design, allowing the user to easily purchase them. The execution support unit provides support for implementing the content proposed by the proposal unit. For example, the execution support unit specifically instructs the user on how to arrange furniture and the procedure for installing interior items, thereby enabling the room support system according to the embodiment to easily create the user's ideal room.

[0067] The current situation analysis unit analyzes information such as photos and dimensions of the room, and can grasp the size of the room, the arrangement of furniture, the color of the walls, and so on. For example, the current situation analysis unit analyzes photos of the room and identifies the material of the furniture. For example, it identifies materials such as wood, metal, plastic, etc., and estimates the number of years of use based on that. The current situation analysis unit also analyzes scratches and fading on the surface of the furniture to estimate the number of years of use. For example, it evaluates the degree of fading of wooden furniture and the degree of rust on metal furniture. The current situation analysis unit also analyzes structural damage and deformation of the furniture to evaluate the degree of deterioration. For example, it detects bent chair legs and cracks on the surface of a table. This allows the current state of the room to be accurately grasped.

[0068] The suggestion unit can collect information about the user's ideal room and, based on that, suggest room layouts and interiors. The suggestion unit, for example, analyzes how light enters and the position of shadows from a photo of the room and suggests optimal lighting arrangements. For example, the suggestion unit analyzes a photo of the room and identifies the position and size of windows. This allows the way natural light enters to be understood. Furthermore, to analyze how light enters, the suggestion unit measures the brightness of each part of the room and identifies the position of shadows. For example, it simulates changes in light during the day. Furthermore, to suggest optimal lighting arrangements, the suggestion unit places appropriate lighting in dark or shadowy parts of the room. For example, it suggests indirect lighting or spotlights. This makes it possible to make suggestions based on the user's ideals.

[0069] The visualization unit can visualize the proposed layout and interior design using a 3D model or simulation. For example, the visualization unit uses an emotion estimation function to analyze the user's emotions regarding the current state of the room and identify stressful elements. For example, the visualization unit analyzes a photo of the room and evaluates the room's color and design elements to estimate the user's emotions. For example, dark colors and crowded layouts can cause stress. The visualization unit also collects the user's reactions to each part of the room to analyze the user's emotions. For example, it identifies the stress the user feels regarding specific furniture or layouts. The visualization unit also analyzes the noise level and temperature in the room to identify stressful elements. For example, it identifies areas with a lot of noise or an uncomfortable temperature. This allows the user to visually confirm the proposed content.

[0070] The shopping list generation unit can list detailed information about the proposed interior and furniture items and where to purchase them. For example, the shopping list generation unit analyzes the acoustic characteristics in a current room analysis and proposes an optimal acoustic environment. For example, the shopping list generation unit evaluates the shape and materials of the room to analyze the acoustic characteristics of the room. For example, it analyzes the effects of wall and floor materials on sound reflection and absorption. The shopping list generation unit also measures the acoustic characteristics of each part of the room to optimize the acoustic environment. For example, it identifies areas with a lot of echo and reverberation. The shopping list generation unit also suggests the placement of acoustic panels and carpets to propose an optimal acoustic environment. For example, it suggests the placement of sound-absorbing material to reduce sound reflection. This allows the user to smoothly gather the necessary items based on the suggestions.

[0071] The execution support unit can provide specific instructions on how to arrange furniture and procedures for setting up interior decor when implementing the suggestions. For example, the execution support unit collects temperature and humidity data in analyzing the current state of a room and makes suggestions for maintaining a comfortable environment. For example, the execution support unit installs sensors to measure the temperature and humidity in the room and collects data. For example, a temperature sensor or a humidity sensor is used. The execution support unit also analyzes the temperature and humidity data and makes suggestions for maintaining a comfortable environment. For example, it suggests using an air conditioner or a humidifier. The execution support unit also analyzes fluctuations in temperature and humidity and suggests optimal settings for each season. For example, it suggests different settings for summer and winter. This allows the user to accurately implement the suggestions.

[0072] The current state analysis unit can analyze information such as photos and dimensions of the room to estimate the material and age of the furniture and evaluate its degree of deterioration. For example, the current state analysis unit analyzes photos of the room to identify the material of the furniture. For example, it identifies materials such as wood, metal, and plastic, and estimates the age based on that. The current state analysis unit also analyzes scratches and discoloration on the surface of the furniture to estimate the age. For example, it evaluates the degree of discoloration on wooden furniture and the degree of rust on metal furniture. The current state analysis unit also analyzes structural damage and deformation of the furniture to evaluate its degree of deterioration. For example, it detects bent chair legs and cracks on the surface of a table. This makes it possible to evaluate the degree of deterioration of the furniture and make appropriate suggestions.

[0073] The current situation analysis unit can analyze how light enters and the location of shadows from a photo of the room and propose the optimal lighting layout. For example, the current situation analysis unit analyzes a photo of the room and identifies the location and size of windows. This allows the unit to understand how natural light enters. In addition, to analyze how light enters, the current situation analysis unit measures the brightness of each part of the room and identifies the location of shadows. For example, it simulates changes in light during the day. In addition, in order to propose the optimal lighting layout, the current situation analysis unit places appropriate lighting in dark or shadowy parts of the room. For example, it proposes indirect lighting or spotlights. This allows the lighting layout of the room to be optimized.

[0074] The current situation analysis unit can use the emotion estimation function to analyze the user's emotions regarding the current state of the room and identify stressful factors. The current situation analysis unit, for example, analyzes a photo of the room and evaluates the color and design elements of the room to estimate the user's emotions. For example, dark colors and crowded layouts can cause stress. The current situation analysis unit also collects the user's reactions to each part of the room to analyze the user's emotions. For example, it identifies the stress the user feels from specific furniture or layouts. The current situation analysis unit also analyzes the noise level and temperature in the room to identify stressful factors. For example, it identifies areas with a lot of noise or an uncomfortable temperature. This makes it possible to identify the user's stress factors and make suggestions for improvement.

[0075] The current situation analysis unit can analyze the acoustic characteristics of a room and propose an optimal acoustic environment. For example, the current situation analysis unit evaluates the shape and materials of the room to analyze the acoustic characteristics of the room. For example, it analyzes the effect that wall and floor materials have on sound reflection and absorption. The current situation analysis unit also measures the acoustic characteristics of each part of the room to optimize the acoustic environment. For example, it identifies areas with a lot of echo and reverberation. The current situation analysis unit also proposes the placement of acoustic panels and carpets to propose an optimal acoustic environment. For example, it proposes the placement of sound-absorbing materials to reduce sound reflection. This allows the acoustic environment of the room to be optimized.

[0076] The current situation analysis unit can collect data on room temperature and humidity and make suggestions for maintaining a comfortable environment. For example, the current situation analysis unit installs sensors to measure the room temperature and humidity and collects data. For example, it uses temperature sensors and humidity sensors. The current situation analysis unit also analyzes the temperature and humidity data and makes suggestions for maintaining a comfortable environment. For example, it suggests using an air conditioner or humidifier. The current situation analysis unit also analyzes fluctuations in temperature and humidity and suggests optimal settings for each season. For example, it suggests different settings for summer and winter. This allows the room temperature and humidity to be optimized and a comfortable environment to be maintained.

[0077] The current situation analysis unit uses the emotion estimation function to analyze the emotions of all family members regarding the current state of the room and can make suggestions that will satisfy everyone. For example, the current situation analysis unit collects reactions to each part of the room to analyze the emotions of all family members. For example, it identifies the emotions that all family members feel toward specific furniture or layouts. The current situation analysis unit also evaluates the colors and design elements of the room to analyze the emotions of all family members. For example, it identifies the colors and designs that everyone prefers. The current situation analysis unit also proposes the optimal layout and interior design based on the emotional data of all family members to make suggestions that will satisfy everyone. For example, it proposes an arrangement that will allow everyone to be comfortable. This makes it possible to make suggestions that will satisfy all family members.

[0078] The suggestion unit can analyze the user's past interior selection history and make suggestions based on their preferences. For example, the suggestion unit collects the user's past interior selection history, and the generation AI analyzes that data. For example, suggestions are made based on data on furniture and interior items purchased in the past. The suggestion unit also analyzes the past selection history to identify the user's preferences. For example, it grasps trends in specific design styles and colors. In addition, the suggestion unit suggests optimal layouts and interior items based on the past selection history in order to make suggestions based on the user's preferences. For example, it makes suggestions that match the user's preferred style. This makes it possible to make suggestions based on the user's preferences.

[0079] The suggestion unit can analyze the user's lifestyle and daily movement lines and propose optimal furniture layouts. The suggestion unit, for example, analyzes daily movement lines to understand the user's lifestyle. For example, it identifies movement patterns within a room and frequently used areas. The suggestion unit also analyzes daily movement lines and proposes optimal furniture layouts. For example, it arranges furniture so as not to obstruct movement lines. The suggestion unit also proposes optimal layouts based on movement line data to make proposals tailored to the user's lifestyle. For example, it arranges necessary furniture in frequently used areas. This makes it possible to arrange furniture optimally based on the user's lifestyle.

[0080] The suggestion unit uses the emotion estimation function to analyze the emotions the user has toward the ideal room and can make suggestions that will elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the emotions the user has toward the ideal room. For example, it collects emotional reactions when the user sees an image of the ideal room. The suggestion unit also suggests optimal layouts and interiors based on the user's emotion data to elicit positive emotions. For example, it suggests designs that will make the user feel joyful and satisfied. The suggestion unit also analyzes the user's emotions and identifies elements that will elicit positive emotions toward the ideal room. For example, it identifies that specific colors and design elements elicit positive emotions. This makes it possible to make suggestions that will elicit positive emotions from the user.

[0081] The suggestion unit can suggest interior changes for each season to create a sense of the season. For example, the suggestion unit collects design elements according to the season in order to suggest interior changes for each season. For example, bright colors and floral designs are suggested for spring. The suggestion unit also suggests interior changes for each season to create a sense of the season. For example, cool designs and materials are suggested for summer. The suggestion unit also suggests furniture and decorations according to the season in order to suggest interior changes for each season. For example, warm designs and materials are suggested for winter. This makes it possible to make interior changes that create a sense of the season.

[0082] The proposal department can recommend eco-friendly materials and products and make environmentally conscious proposals. For example, the proposal department collects eco-friendly materials and products and incorporates them into proposals for ideal rooms. For example, it proposes renewable materials and energy-efficient products. In addition, the proposal department recommends eco-friendly materials and products in order to make environmentally conscious proposals. For example, it proposes furniture made with low-VOC (volatile organic compound) paints and recycled materials. In addition, the proposal department incorporates environmentally conscious design elements in order to recommend eco-friendly materials and products. For example, it proposes natural materials and energy-efficient lighting. This makes it possible to make environmentally conscious proposals.

[0083] The suggestion unit uses the emotion estimation function to analyze the emotions of the user's pet and suggest a room where the pet can also live comfortably. The suggestion unit uses the emotion estimation function, for example, to analyze the emotions of the user's pet. For example, it analyzes the pet's behavior and reactions and identifies a comfortable environment. The suggestion unit also suggests the optimal layout and interior design based on the pet's emotion data to suggest a room where the pet can also live comfortably. For example, it suggests a space where the pet can relax. The suggestion unit also analyzes the pet's emotions and identifies elements that will allow the pet to live comfortably. For example, it identifies that certain materials and design elements are comfortable for the pet. This makes it possible to suggest a room where the pet can also live comfortably.

[0084] The visualization unit can display the proposal content in AR, allowing the user to check it in the actual room. For example, to display the proposal content in AR, the visualization unit scans the current state of the room and overlays the proposed layout and interior. For example, the AR display is performed using a smartphone or tablet. The visualization unit also uses AR technology to display the proposal content in real time, allowing the user to check it in the actual room. For example, it simulates furniture placement and color changes using AR. Furthermore, to display the proposal content in AR, the visualization unit allows the user to check it while walking around the room. For example, the proposal content is visually confirmed using AR glasses. This allows the user to check the proposal content in the actual room.

[0085] The visualization unit allows the user to experience the proposal content in VR, allowing the user to walk around the room in a virtual space. For example, the visualization unit creates a 3D model of the room so that the user can experience the proposal content in VR, allowing the user to walk around in the virtual space. For example, the proposal content is experienced using a VR headset. The visualization unit also displays the room in real time using VR technology so that the user can walk around in the virtual space. For example, the visualization unit simulates furniture placement and color changes in VR. The visualization unit also allows the user to freely move around the room and check the proposal content so that the user can experience it in VR. For example, the user can walk around the room using a VR controller. This allows the user to experience the proposal content in a virtual space.

[0086] The visualization unit can use the emotion estimation function to provide real-time feedback on the user's emotions regarding the visualized proposal content and optimize it. The visualization unit uses the emotion estimation function, for example, to provide real-time feedback on the user's emotions regarding the visualized proposal content. For example, the visualization unit analyzes the user's facial expressions and voice and calculates an emotion score. The visualization unit also optimizes the visualized proposal content based on the user's emotion data. For example, it preferentially displays designs and layouts that indicate positive emotions in the user. The visualization unit also uses the emotion estimation function to collect the user's emotional reactions to the visualized proposal content in real time and adjust the proposal content based on the data. For example, it changes elements that indicate negative emotions in the user. This allows the proposal content to be optimized based on the user's emotions.

[0087] The visualization unit can compare and display different design styles when visualizing the proposal content, allowing the user to select one. For example, the visualization unit compares and displays different design styles when visualizing the proposal content. For example, styles such as modern, classic, and minimalist are simultaneously displayed. The visualization unit also displays the visualized proposal content interactively so that the user can select a different design style. For example, the style can be switched by clicking or tapping. The visualization unit also visualizes the features and advantages of each style to compare and display different design styles. For example, the color usage and furniture arrangement of each style are displayed in detail. This allows the user to compare and select different design styles.

[0088] The visualization unit can simulate how the content of a proposal looks at different times of the day and night when visualizing the content of a proposal. For example, the visualization unit simulates how the content of a proposal looks at different times of the day and night when visualizing the content of a proposal. For example, the visualization unit displays the difference between natural light during the day and artificial lighting at night. The visualization unit also changes the lighting conditions in the room to simulate how the content of a proposal looks at different times of the day and night. For example, the visualization unit reproduces the brightness of daytime and the darkness of nighttime. The visualization unit also interactively displays the visualized content of a proposal so that the user can check how the content of a proposal looks at different times of the day and night. For example, the visualization unit uses a slider to switch between different time periods. This allows for a simulation of how the content of a proposal looks at different times of the day and night.

[0089] The visualization unit uses the emotion estimation function to analyze the emotions of all family members regarding the visualized proposal content, and can make proposals that will satisfy everyone. The visualization unit uses the emotion estimation function, for example, to analyze the emotions of all family members regarding the visualized proposal content. For example, it analyzes the facial expressions and voices of all family members and calculates an emotion score. The visualization unit also optimizes the visualized proposal content based on the emotion data of all family members. For example, it prioritizes displaying designs and layouts that show positive emotions among all family members. The visualization unit also uses the emotion estimation function to collect the emotional reactions of all family members to the visualized proposal content in real time, and adjusts the proposal content based on that data. For example, it changes elements that show negative emotions among all family members. This makes it possible to make proposals that will satisfy all family members.

[0090] The shopping list generation unit compares the prices of the proposed furniture and interior items and can suggest the optimal purchasing source. For example, the shopping list generation unit collects the prices of the proposed furniture and interior items, and the generation AI performs the price comparison. For example, it collects price data from multiple online stores. The shopping list generation unit also collects detailed information about the proposed furniture and interior items to compare prices. For example, it evaluates prices based on product specifications and reviews. The shopping list generation unit also presents the most cost-effective option based on the price comparison data to suggest the optimal purchasing source. For example, it considers not only price but also delivery conditions and warranty details. This allows it to suggest the optimal purchasing source.

[0091] The shopping list generation unit can include reviews and ratings of the proposed items in the shopping list for user reference. The shopping list generation unit, for example, collects reviews and ratings of the proposed items and includes them in the shopping list. For example, it collects data from online stores and review sites. The shopping list generation unit also displays the reviews and ratings of the proposed items in the shopping list for user reference. For example, it displays star ratings and user comments. The shopping list generation unit also allows the user to make an optimal selection based on the reviews and ratings of the proposed items. For example, it prioritizes the inclusion of highly rated items in the list. This allows the user to refer to the reviews and ratings.

[0092] The shopping list generation unit uses the emotion estimation function to analyze the user's emotion regarding the shopping list and make suggestions that will increase the user's motivation to purchase. The shopping list generation unit uses the emotion estimation function, for example, to analyze the user's emotion regarding the shopping list. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The shopping list generation unit also makes suggestions that will increase the user's motivation to purchase based on the user's emotion data. For example, it preferentially displays items for which the user shows positive emotions. The shopping list generation unit also uses the emotion estimation function to collect the user's emotional reactions to the shopping list in real time and adjusts the suggestion content based on that data. For example, it changes items for which the user shows negative emotions. This makes it possible to make suggestions that will increase the user's motivation to purchase.

[0093] The shopping list generator can include alternatives and options for the proposed items in the shopping list, allowing the user to have choices. For example, the shopping list generator collects alternatives and options for the proposed items and includes them in the shopping list. For example, different brands or models in the same category are suggested. The shopping list generator also displays alternatives and options in the shopping list, allowing the user to have choices. For example, the shopping list generator allows the user to compare differences in price or functionality. The shopping list generator also allows the user to make the best choice based on the alternatives and options for the proposed items. For example, the shopping list generator presents options that match the user's preferences and budget. This allows the user to have choices.

[0094] The purchase list generation unit can include installation and maintenance information for the items proposed in the purchase list, allowing the user to use the items for a long period of time. The purchase list generation unit, for example, collects installation and maintenance information for the proposed items and includes it in the purchase list. For example, it describes assembly procedures and maintenance frequency. The purchase list generation unit also displays the installation and maintenance information in the purchase list, allowing the user to use the items for a long period of time. For example, it provides an installation video or a maintenance guide. The purchase list generation unit also allows the user to make an optimal selection based on the installation and maintenance information for the proposed items. For example, it prioritizes items that are easy to maintain in the list. This allows the user to use the items for a long period of time.

[0095] The shopping list generation unit uses the emotion estimation function to analyze the emotions of all family members regarding the shopping list and can make proposals that satisfy all family members. The shopping list generation unit uses the emotion estimation function, for example, to analyze the emotions of all family members regarding the shopping list. For example, it analyzes the facial expressions and voices of all family members and calculates an emotion score. The shopping list generation unit also makes proposals that satisfy all family members based on the emotion data of all family members. For example, it prioritizes displaying items that all family members express positive emotions about. The shopping list generation unit also uses the emotion estimation function to collect the emotional reactions of all family members regarding the shopping list in real time and adjusts the proposal content based on that data. For example, it changes items that all family members express negative emotions about. This makes it possible to make proposals that satisfy all family members.

[0096] The execution support unit can use the generation AI to provide furniture assembly procedures in the form of a video, allowing the user to easily assemble the furniture. For example, in order to provide furniture assembly procedures in the form of a video, the execution support unit uses the generation AI to analyze the assembly procedures and create a video. For example, a video is provided that explains each step in detail. The execution support unit also visually explains the assembly procedures in the video, allowing the user to easily assemble the furniture. For example, a video may show how to attach parts and how to use tools. In addition, in order to provide assembly procedures in the form of a video, the execution support unit allows the user to proceed with the assembly while watching the video. For example, the video can be paused as the user proceeds with the work. This allows the user to easily assemble the furniture.

[0097] The execution support unit can simulate the proposed layout and confirm the optimal arrangement. For example, to simulate the proposed layout, the execution support unit uses a generation AI to create a 3D model of the room and conduct the simulation. For example, it simulates furniture placement and traffic flow. The execution support unit also visually displays the simulation results so that the user can confirm the optimal placement. For example, it confirms changes to furniture placement and color through simulation. The execution support unit also allows the user to adjust the placement while viewing the simulation results in order to simulate the proposed layout. For example, it changes the position of furniture based on the simulation results. This allows the user to confirm the optimal placement.

[0098] The execution support unit can use the emotion estimation function to analyze the user's emotions during execution support and make suggestions to reduce stress. The execution support unit uses the emotion estimation function, for example, to analyze the user's emotions during execution support. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The execution support unit also makes suggestions to reduce stress based on the user's emotion data. For example, it suggests a method to simplify a task that causes the user stress. The execution support unit also uses the emotion estimation function to collect the user's emotional reactions during execution support in real time and adjust the suggestion content based on that data. For example, it suggests music or aromas that will help the user relax. This makes it possible to make suggestions to reduce the user's stress.

[0099] The execution support unit can provide online consultation with a professional interior designer, allowing the user to receive professional advice. For example, the execution support unit introduces a video call or chat function to provide online consultation with a professional interior designer. For example, the user can consult with the designer in real time. The execution support unit also allows the interior designer to make suggestions based on photos and dimensions of the user's room so that the user can receive professional advice. For example, the designer may propose specific layouts and interiors. The execution support unit also allows the user to receive feedback from the designer through the online consultation and adjust the proposal. For example, the designer may propose customizations tailored to the user's wishes. This allows the user to receive professional advice.

[0100] The execution support unit can provide a function that allows the proposed layout to be changed or fine-tuned in real time. The execution support unit, for example, develops interactive tools to provide a function that allows the proposed layout to be changed or fine-tuned in real time. For example, the execution support unit allows the user to change the furniture arrangement by drag and drop. The execution support unit also allows the generation AI to instantly propose a new layout so that the user can change the proposed layout in real time. For example, when the user changes the position of furniture, the generation AI proposes a new arrangement. The execution support unit also provides a function that allows the user to make adjustments while checking the changes so that the proposed layout can be fine-tuned in real time. For example, the changed layout can be displayed as a 3D model. This allows the layout to be changed or fine-tuned in real time.

[0101] The execution support unit can use the emotion estimation function to analyze the emotions of all family members during execution support, allowing everyone to work cooperatively. The execution support unit uses the emotion estimation function, for example, to analyze the emotions of all family members during execution support. For example, it analyzes the facial expressions and voices of all family members and calculates an emotion score. The execution support unit also makes suggestions based on the emotion data of all family members to enable everyone to work cooperatively. For example, it suggests ways to divide up tasks and cooperate that are enjoyable for all family members. The execution support unit also uses the emotion estimation function to collect the emotional responses of all family members during execution support in real time, and adjusts the suggestions based on that data. For example, it suggests creating an environment where everyone can relax. This allows all family members to work cooperatively.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The suggestion unit can analyze the user's lifestyle and daily movement lines and propose optimal furniture layouts. For example, the suggestion unit analyzes daily movement lines to understand the user's lifestyle. For example, it identifies movement patterns within a room and frequently used areas. The suggestion unit also analyzes daily movement lines and proposes optimal furniture layouts. For example, it arranges furniture so as not to obstruct movement lines. The suggestion unit also proposes optimal layouts based on movement line data to make proposals tailored to the user's lifestyle. For example, it arranges necessary furniture in frequently used areas. This makes it possible to arrange furniture optimally based on the user's lifestyle.

[0104] The suggestion unit can suggest interior changes for each season to create a sense of the season. For example, the suggestion unit collects design elements according to the season in order to suggest interior changes for each season. For example, bright colors and floral designs are suggested for spring. The suggestion unit also suggests interior changes for each season to create a sense of the season. For example, cool designs and materials are suggested for summer. The suggestion unit also suggests furniture and decorations according to the season in order to suggest interior changes for each season. For example, warm designs and materials are suggested for winter. This makes it possible to make interior changes that create a sense of the season.

[0105] The proposal department can recommend eco-friendly materials and products and make environmentally conscious proposals. For example, the proposal department collects eco-friendly materials and products and incorporates them into proposals for ideal rooms. For example, it proposes renewable materials and energy-efficient products. The proposal department also recommends eco-friendly materials and products in order to make environmentally conscious proposals. For example, it proposes furniture made with low-VOC (volatile organic compound) paints and recycled materials. The proposal department also incorporates environmentally conscious design elements in order to recommend eco-friendly materials and products. For example, it proposes natural materials and energy-efficient lighting. This makes it possible to make environmentally conscious proposals.

[0106] The suggestion unit can analyze the user's past interior selection history and make suggestions based on their preferences. For example, the suggestion unit collects the user's past interior selection history, and the generation AI analyzes that data. For example, suggestions are made based on data on furniture and interior items purchased in the past. The suggestion unit also analyzes the past selection history to identify the user's preferences. For example, it grasps trends in specific design styles and colors. In addition, the suggestion unit suggests optimal layouts and interior items based on the past selection history in order to make suggestions based on the user's preferences. For example, it makes suggestions that match the user's preferred style. This makes it possible to make suggestions based on the user's preferences.

[0107] The suggestion unit can use the emotion estimation function to analyze the emotions the user has toward the ideal room and make suggestions that will elicit positive emotions. For example, the suggestion unit uses the emotion estimation function to analyze the emotions the user has toward the ideal room. For example, the suggestion unit collects emotional reactions when the user sees an image of the ideal room. The suggestion unit also suggests optimal layouts and interiors based on the user's emotion data to elicit positive emotions. For example, the suggestion unit suggests designs that will make the user feel joyful and satisfied. The suggestion unit also analyzes the user's emotions and identifies elements that will elicit positive emotions toward the ideal room. For example, the suggestion unit identifies that specific colors and design elements elicit positive emotions. This makes it possible to make suggestions that will elicit positive emotions from the user.

[0108] The suggestion unit can use the emotion estimation function to analyze the emotions of the user's pet and suggest a room where the pet can also live comfortably. For example, the suggestion unit uses the emotion estimation function to analyze the emotions of the user's pet. For example, it analyzes the pet's behavior and reactions and identifies a comfortable environment. The suggestion unit also suggests the optimal layout and interior design based on the pet's emotion data to suggest a room where the pet can also live comfortably. For example, it suggests a space where the pet can relax. The suggestion unit also analyzes the pet's emotions and identifies elements that will allow the pet to live comfortably. For example, it identifies that certain materials and design elements are comfortable for the pet. This makes it possible to suggest a room where the pet can also live comfortably.

[0109] The visualization unit can display the proposal content in AR, allowing the user to check it in the actual room. For example, to display the proposal content in AR, the visualization unit scans the current state of the room and overlays the proposed layout and interior. For example, the AR display is performed using a smartphone or tablet. The visualization unit also uses AR technology to display the proposal content in real time, allowing the user to check it in the actual room. For example, it simulates furniture placement and color changes using AR. The visualization unit also displays the proposal content in AR, allowing the user to check it while walking around the room. For example, the proposal content is visually confirmed using AR glasses. This allows the user to check the proposal content in the actual room.

[0110] The visualization unit allows the user to experience the proposal content in VR, allowing the user to walk around the room in a virtual space. For example, the visualization unit creates a 3D model of the room so that the user can experience the proposal content in VR, allowing the user to walk around in the virtual space. For example, the proposal content is experienced using a VR headset. The visualization unit also uses VR technology to display the room in real time so that the user can walk around in the virtual space. For example, the visualization unit simulates furniture placement and color changes in VR. The visualization unit also allows the user to freely move around the room and check the proposal content so that the user can experience it in VR. For example, the user can walk around the room using a VR controller. This allows the user to experience the proposal content in a virtual space.

[0111] The visualization unit can use the emotion estimation function to provide real-time feedback on the user's emotions regarding the visualized proposal content and optimize it. For example, the visualization unit uses the emotion estimation function to provide real-time feedback on the user's emotions regarding the visualized proposal content. For example, the visualization unit analyzes the user's facial expressions and voice and calculates an emotion score. The visualization unit also optimizes the visualized proposal content based on the user's emotion data. For example, it preferentially displays designs and layouts that indicate positive emotions in the user. The visualization unit also uses the emotion estimation function to collect the user's emotional reactions to the visualized proposal content in real time and adjust the proposal content based on the data. For example, it changes elements that indicate negative emotions in the user. This allows the proposal content to be optimized based on the user's emotions.

[0112] When visualizing the proposal content, the visualization unit can compare and display different design styles to allow the user to select one. For example, when visualizing the proposal content, the visualization unit compares and displays different design styles. For example, styles such as modern, classic, and minimalist are simultaneously displayed. The visualization unit also displays the visualized proposal content interactively so that the user can select a different design style. For example, the style can be switched by clicking or tapping. The visualization unit also visualizes the features and advantages of each style to compare and display different design styles. For example, the color usage and furniture arrangement of each style are displayed in detail. This allows the user to compare and select different design styles.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The current state analysis unit uses the generative AI to analyze the current state of the room. For example, it analyzes information such as photos and dimensions of the room provided by the user to determine the size of the room, furniture layout, wall color, etc. Step 2: The proposal unit proposes the user's ideal room based on the current state of the room analyzed by the current state analysis unit. For example, it collects information about the user's ideal room and proposes the room layout and interior based on that information. Step 3: The visualization unit visualizes the layout and interior proposed by the proposal unit, for example, using a 3D model or simulation to allow the user to visually confirm the proposal. Step 4: The purchase list generation unit generates a purchase list of the interior and furniture items suggested by the suggestion unit. For example, it lists detailed information about the suggested furniture and interior items and where to purchase them, making it easy for the user to purchase them. Step 5: The implementation support section provides support for implementing the proposals made by the proposal section, for example by providing specific instructions on how to arrange furniture and the steps for installing interior items.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A current state analysis unit that uses generative AI to analyze the current state of the room, a proposal unit that proposes an ideal room for a user based on the current state of the room analyzed by the current state analysis unit; a visualization unit that visualizes the layout and interior proposed by the proposal unit; a purchase list generation unit that generates a purchase list of the interior and furniture proposed by the proposal unit; an execution support unit that provides support when executing the content proposed by the proposal unit; A system characterized by:

2. The current situation analysis unit Analyze information such as photos and dimensions of the room to understand the size of the room, the layout of the furniture, the color of the walls, etc.

2. The system of claim 1.

3. The proposal unit Collecting information about the user's ideal room and proposing the layout and interior of the room based on that information 2. The system of claim 1.

4. The visualization unit The layout and the interior proposed by the proposal unit are visualized using the 3D model or simulation.

2. The system of claim 1.

5. The purchase list generation unit Listing detailed information and purchasing locations of the interior and furniture proposed by the proposal unit 2. The system of claim 1.

6. The execution support unit Specifically instructing how to arrange the furniture and the interior installation procedure when implementing the proposal content proposed by the proposal unit 2. The system of claim 1.

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