System

Abstract

An object of a system according to an embodiment is to enable a user to easily propose and purchase furniture and interior arrangement suitable for a room.SOLUTION: A system includes a moving image photographing part, an analysis part, a proposal part, and a purchase part. The moving image photographing unit allows a user to photograph a moving image. The analysis unit analyzes the moving image data captured by the moving image capturing unit. The proposal unit proposes arrangement of furniture or an interior on the basis of the information of the room analyzed by the analysis unit. The purchase unit purchases the furniture or interior 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 technology had the problem that it was difficult and time-consuming to efficiently suggest furniture and interior layouts that suited a room.

[0005] The system according to the embodiment aims to allow users to easily propose and purchase furniture and interior layouts that suit their rooms. [Means for solving the problem]

[0006] The system according to the embodiment includes a video capture unit, an analysis unit, a proposal unit, and a purchase unit. The video capture unit allows a user to capture video. The analysis unit analyzes video data captured by the video capture unit. The proposal unit proposes furniture or interior layout based on room information analyzed by the analysis unit. The purchase unit purchases the furniture or interior proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily propose and purchase furniture and interior layouts that suit a 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 furniture recommendation system according to an embodiment of the present invention is a system that suggests furniture and interior layouts that suit a user's room simply by shooting a video. The entire process from shooting to suggesting the layout can be completed in less than three minutes. This allows even busy users to find the furniture and interior that best suits their room in a short amount of time.

[0029] A furniture recommendation system according to an embodiment includes a video capture unit, an analysis unit, a proposal unit, and a purchase unit. The video capture unit allows a user to capture a video. For example, the user can capture a video from each of the four corners of a room toward the center, allowing the user to grasp an overall view of the room. The video capture unit can capture the video using a smartphone or tablet camera. The analysis unit analyzes the video data captured by the video capture unit. For example, a generation AI recognizes the size, shape, and existing furniture layout of the room in the video and generates a 3D model of the room. The analysis unit can also perform analysis taking into account the resolution and frame rate of the video data. The proposal unit proposes furniture and interior layouts based on the room information analyzed by the analysis unit. For example, the generation AI selects optimal furniture and interior items based on the user's preferences and budget and places them on the 3D model of the room. The proposal unit allows the user to set a price range and design within the app. The purchase unit purchases the furniture and interior items proposed by the proposal unit. For example, the user can click on the proposed furniture to access detailed information and a purchase page. The purchase unit can also provide online shopping and payment methods. As a result, the furniture recommendation system according to the embodiment allows users to easily find and purchase furniture and interior items that suit their rooms.

[0030] The video capture unit records the user's voice and comments in real time, and the generation AI analyzes the content and reflects it in the proposal unit. For example, when a user comments on a specific part of a room while filming a video, the video capture unit records the audio data in real time and the generation AI analyzes it. For example, a comment such as "I want to hang a picture on this wall" is analyzed and reflected in the proposal. In addition, when a user expresses their opinion on furniture placement while filming a video, the video capture unit records the audio, which the generation AI analyzes and reflects in the proposal. For example, a proposal is made based on a comment such as "I want to put a bookshelf in this corner." In addition, when a user points out a specific problem in a room while filming a video, the video capture unit records the audio data, which the generation AI analyzes and proposes a solution. For example, a proposal is made based on a comment such as "This space is small, so I would like to add more storage." This makes it possible to make proposals that reflect the user's voice and comments.

[0031] When a user points at a specific piece of furniture or interior decor, the video capture unit can focus on that part for analysis. For example, when a user points at a specific piece of furniture or interior decor while recording a video, the video capture unit automatically focuses the camera on that part, and the generation AI analyzes it in detail. For example, the design and color of the sofa the user points at can be analyzed. Also, when a user points at a specific area while recording a video, the video capture unit can focus on analyzing that area, and the generation AI can suggest the optimal furniture layout. For example, it can suggest furniture that is suitable for the corner the user points at. Also, when a user points at a specific interior item while recording a video, the video capture unit can analyze detailed information about that item, and the generation AI can reflect this in its suggestions. For example, it can analyze the style and color of the painting the user points at. This allows for focused analysis of parts that the user is particularly interested in.

[0032] The analysis unit simultaneously records the lighting or acoustic environment of the room when shooting a video, and the generation AI can suggest the optimal furniture layout based on that information. For example, the analysis unit records the brightness and color temperature of the lighting in the room when shooting a video, and the generation AI suggests the optimal furniture layout based on that information. For example, the furniture layout may be adjusted according to the brightness of the lighting. The analysis unit also records the acoustic environment of the room when shooting a video, and the generation AI suggests the optimal furniture layout based on that information. For example, the furniture layout may be adjusted taking into account sound reverberation. The analysis unit also simultaneously records the lighting and acoustic environment in the room when shooting a video, and the generation AI suggests the optimal furniture layout based on that information. For example, the furniture layout may be adjusted according to the position of the lighting and the acoustic characteristics. This makes it possible to arrange furniture taking into account the lighting and acoustic environment.

[0033] The analysis unit also collects data from other devices carried by the user when shooting videos, allowing the generation AI to make suggestions based on their health condition and activity level. For example, the analysis unit collects heart rate and activity level data from the user's smartwatch when shooting videos, and the generation AI uses that information to suggest optimal furniture layouts. For example, it suggests furniture layouts that promote relaxation. Furthermore, the analysis unit allows the generation AI to suggest optimal bedroom furniture layouts based on sleep data collected from the user's smartwatch. For example, it suggests layouts that provide a comfortable sleeping environment. Furthermore, the analysis unit allows the generation AI to suggest furniture layouts that are good for health based on health data collected from the user's smartwatch when shooting videos. For example, it suggests chair placements that improve posture. This makes it possible to arrange furniture based on the user's health condition and activity level.

[0034] When analyzing the video data, the analysis unit also takes into account environmental data such as room temperature or humidity, allowing the generation AI to propose the optimal furniture layout. For example, the analysis unit collects room temperature data when analyzing the video data, and the generation AI proposes the optimal furniture layout based on that information. For example, cool furniture is placed in areas with high temperatures. The analysis unit also collects room humidity data when analyzing the video data, and the generation AI proposes the optimal furniture layout based on that information. For example, furniture with good ventilation is placed in areas with high humidity. The analysis unit also collects room temperature and humidity data simultaneously when analyzing the video data, and the generation AI proposes the optimal furniture layout based on that information. For example, a layout that takes into account the balance between temperature and humidity is proposed. This makes it possible to arrange furniture taking temperature and humidity into account.

[0035] When analyzing video data, the analysis unit performs a detailed analysis of the texture of the room's wallpaper or flooring, allowing the generation AI to suggest furniture materials that match it. For example, the analysis unit may perform a detailed analysis of the texture of the room's wallpaper when analyzing video data, allowing the generation AI to suggest the most suitable furniture material based on that information. For example, it may suggest a sofa material that matches the texture of the wallpaper. Furthermore, the analysis unit may perform a detailed analysis of the texture of the room's flooring when analyzing video data, allowing the generation AI to suggest the most suitable furniture material based on that information. For example, it may suggest a table material that matches the texture of the flooring. Furthermore, the analysis unit may simultaneously analyze the texture of the room's wallpaper and flooring when analyzing video data, allowing the generation AI to suggest the most suitable furniture material based on that information. For example, it may suggest furniture that harmonizes with the texture of the wallpaper and flooring. This makes it possible to suggest furniture materials that match the texture of the wallpaper and flooring.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The analysis unit collects the user's lifestyle data, and the generation AI can use that information to suggest optimal furniture layouts. For example, if a user frequently hosts home parties, the AI ​​will suggest furniture layouts that will allow guests to be comfortable. If a user works remotely, the AI ​​will suggest desk and chair layouts that will increase work efficiency. Furthermore, if the user has pets, the AI ​​will suggest furniture layouts that will keep the pets safe. This makes it possible to arrange furniture to suit the user's lifestyle.

[0038] The analysis unit allows the generation AI to propose optimal furniture layouts based on the user's past purchase history. For example, new furniture is proposed taking into account the style and color of furniture previously purchased by the user. The system also proposes optimal layouts based on the layout of furniture previously purchased by the user. Furthermore, the system considers how frequently the user has used furniture previously and proposes layouts centered on frequently used furniture. This makes it possible to arrange furniture based on the user's past purchase history.

[0039] The analysis unit allows the generation AI to suggest optimal furniture layouts based on the user's hobbies and interests. For example, if the user's hobby is reading, it will suggest a comfortable reading space. If the user enjoys music, it will suggest furniture layouts that take the acoustic environment into consideration. Furthermore, if the user is interested in art, it will suggest the optimal layout for displaying artwork. This makes it possible to arrange furniture according to the user's hobbies and interests.

[0040] The analysis unit allows the generation AI to propose optimal furniture layouts based on the user's family composition. For example, in a household with children, it will propose furniture layouts that prioritize safety. In a household with elderly people, it will propose furniture layouts that take barrier-free access into consideration. Furthermore, in a household with pets, it will propose furniture layouts that allow the pets to live comfortably. This makes it possible to arrange furniture according to the family composition.

[0041] The analysis unit allows the generative AI to suggest optimal furniture layouts based on the user's health condition. For example, for a user with lower back pain, it will suggest chair or sofa layouts that are gentle on the lower back. For a user with allergies, it will suggest furniture layouts that reduce allergens. Furthermore, for a user with poor eyesight, it will suggest furniture layouts that improve visibility. This makes it possible to arrange furniture according to the user's health condition.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The user shoots a video using the video capture unit. For example, the user can capture a video from each of the four corners of a room toward the center, allowing them to grasp the overall picture of the room. The video capture unit can also capture videos using the camera on a smartphone or tablet. Step 2: The analysis unit analyzes the video data captured by the video capture unit. For example, the generation AI recognizes the size, shape, and existing furniture layout of the room in the video and generates a 3D model of the room. The analysis unit can also take into account the resolution and frame rate of the video data during its analysis. Step 3: The proposal unit proposes furniture and interior layouts based on the room information analyzed by the analysis unit. For example, the generation AI selects optimal furniture and interior items based on the user's preferences and budget, and places them on the 3D model of the room. The proposal unit also allows the user to set price ranges and designs within the app. Step 4: The purchasing unit purchases the furniture and interior items suggested by the suggestion unit. For example, when the user clicks on the suggested furniture, they can access detailed information and a purchase page. The purchasing unit can also provide online shopping and payment methods.

[0044] (Example 2) The furniture recommendation system according to an embodiment of the present invention is a system that suggests furniture and interior layouts that suit a user's room simply by shooting a video. The entire process from shooting to suggesting the layout can be completed in less than three minutes. This allows even busy users to find the furniture and interior that best suits their room in a short amount of time.

[0045] A furniture recommendation system according to an embodiment includes a video capture unit, an analysis unit, a proposal unit, and a purchase unit. The video capture unit allows a user to capture a video. For example, the user can capture a video from each of the four corners of a room toward the center, allowing the user to grasp an overall view of the room. The video capture unit can capture the video using a smartphone or tablet camera. The analysis unit analyzes the video data captured by the video capture unit. For example, a generation AI recognizes the size, shape, and existing furniture layout of the room in the video and generates a 3D model of the room. The analysis unit can also perform analysis taking into account the resolution and frame rate of the video data. The proposal unit proposes furniture and interior layouts based on the room information analyzed by the analysis unit. For example, the generation AI selects optimal furniture and interior items based on the user's preferences and budget and places them on the 3D model of the room. The proposal unit allows the user to set a price range and design within the app. The purchase unit purchases the furniture and interior items proposed by the proposal unit. For example, the user can click on the proposed furniture to access detailed information and a purchase page. The purchase unit can also provide online shopping and payment methods. As a result, the furniture recommendation system according to the embodiment allows users to easily find and purchase furniture and interior items that suit their rooms.

[0046] The video capture unit records the user's voice and comments in real time, and the generation AI analyzes the content and reflects it in the proposal unit. For example, when a user comments on a specific part of a room while filming a video, the video capture unit records the audio data in real time and the generation AI analyzes it. For example, a comment such as "I want to hang a picture on this wall" is analyzed and reflected in the proposal. In addition, when a user expresses their opinion on furniture placement while filming a video, the video capture unit records the audio, which the generation AI analyzes and reflects in the proposal. For example, a proposal is made based on a comment such as "I want to put a bookshelf in this corner." In addition, when a user points out a specific problem in a room while filming a video, the video capture unit records the audio data, which the generation AI analyzes and proposes a solution. For example, a proposal is made based on a comment such as "This space is small, so I would like to add more storage." This makes it possible to make proposals that reflect the user's voice and comments.

[0047] When a user points at a specific piece of furniture or interior decor, the video capture unit can focus on that part for analysis. For example, when a user points at a specific piece of furniture or interior decor while recording a video, the video capture unit automatically focuses the camera on that part, and the generation AI analyzes it in detail. For example, the design and color of the sofa the user points at can be analyzed. Also, when a user points at a specific area while recording a video, the video capture unit can focus on analyzing that area, and the generation AI can suggest the optimal furniture layout. For example, it can suggest furniture that is suitable for the corner the user points at. Also, when a user points at a specific interior item while recording a video, the video capture unit can analyze detailed information about that item, and the generation AI can reflect this in its suggestions. For example, it can analyze the style and color of the painting the user points at. This allows for focused analysis of parts that the user is particularly interested in.

[0048] The video shooting unit can use the emotion estimation function to analyze the emotions felt by the user while shooting a video and provide shooting guidance to bring out positive emotions. The video shooting unit, for example, analyzes the user's facial expressions while shooting a video and uses the emotion estimation function to grasp the user's emotions in real time. For example, if the user is nervous, it provides guidance to help them relax. The video shooting unit also analyzes the emotions felt by the user while shooting a video and provides audio guidance to bring out positive emotions. For example, it plays an encouraging message to help the user enjoy shooting. The video shooting unit also uses the emotion estimation function to provide relaxing music and guidance to reduce stress and anxiety felt by the user while shooting a video. For example, it plays music that helps the user relax in the background. This allows the user to shoot with positive emotions.

[0049] The analysis unit simultaneously records the lighting or acoustic environment of the room when shooting a video, and the generation AI can suggest the optimal furniture layout based on that information. For example, the analysis unit records the brightness and color temperature of the lighting in the room when shooting a video, and the generation AI suggests the optimal furniture layout based on that information. For example, the furniture layout may be adjusted according to the brightness of the lighting. The analysis unit also records the acoustic environment of the room when shooting a video, and the generation AI suggests the optimal furniture layout based on that information. For example, the furniture layout may be adjusted taking into account sound reverberation. The analysis unit also simultaneously records the lighting and acoustic environment in the room when shooting a video, and the generation AI suggests the optimal furniture layout based on that information. For example, the furniture layout may be adjusted according to the position of the lighting and the acoustic characteristics. This makes it possible to arrange furniture taking into account the lighting and acoustic environment.

[0050] The analysis unit also collects data from other devices carried by the user when shooting videos, allowing the generation AI to make suggestions based on their health condition and activity level. For example, the analysis unit collects heart rate and activity level data from the user's smartwatch when shooting videos, and the generation AI uses that information to suggest optimal furniture layouts. For example, it suggests furniture layouts that promote relaxation. Furthermore, the analysis unit allows the generation AI to suggest optimal bedroom furniture layouts based on sleep data collected from the user's smartwatch. For example, it suggests layouts that provide a comfortable sleeping environment. Furthermore, the analysis unit allows the generation AI to suggest furniture layouts that are good for health based on health data collected from the user's smartwatch when shooting videos. For example, it suggests chair placements that improve posture. This makes it possible to arrange furniture based on the user's health condition and activity level.

[0051] The analysis unit is equipped with an emotion estimation function and can provide relaxing music or guidance to reduce stress or anxiety felt by the user while filming. The analysis unit, for example, analyzes the user's facial expressions and voice while filming a video and uses the emotion estimation function to detect stress or anxiety. For example, if the user is nervous, relaxing music is played. The analysis unit also uses the emotion estimation function to provide guidance to reduce stress felt by the user while filming. For example, audio guidance encouraging deep breathing is played. The analysis unit also provides relaxing music to reduce anxiety felt by the user while filming a video. For example, music that helps the user relax is played in the background. This allows the user to relax while filming.

[0052] When analyzing the video data, the analysis unit also takes into account environmental data such as room temperature or humidity, allowing the generation AI to propose the optimal furniture layout. For example, the analysis unit collects room temperature data when analyzing the video data, and the generation AI proposes the optimal furniture layout based on that information. For example, cool furniture is placed in areas with high temperatures. The analysis unit also collects room humidity data when analyzing the video data, and the generation AI proposes the optimal furniture layout based on that information. For example, furniture with good ventilation is placed in areas with high humidity. The analysis unit also collects room temperature and humidity data simultaneously when analyzing the video data, and the generation AI proposes the optimal furniture layout based on that information. For example, a layout that takes into account the balance between temperature and humidity is proposed. This makes it possible to arrange furniture taking temperature and humidity into account.

[0053] When analyzing video data, the analysis unit performs a detailed analysis of the texture of the room's wallpaper or flooring, allowing the generation AI to suggest furniture materials that match it. For example, the analysis unit may perform a detailed analysis of the texture of the room's wallpaper when analyzing video data, allowing the generation AI to suggest the most suitable furniture material based on that information. For example, it may suggest a sofa material that matches the texture of the wallpaper. Furthermore, the analysis unit may perform a detailed analysis of the texture of the room's flooring when analyzing video data, allowing the generation AI to suggest the most suitable furniture material based on that information. For example, it may suggest a table material that matches the texture of the flooring. Furthermore, the analysis unit may simultaneously analyze the texture of the room's wallpaper and flooring when analyzing video data, allowing the generation AI to suggest the most suitable furniture material based on that information. For example, it may suggest furniture that harmonizes with the texture of the wallpaper and flooring. This makes it possible to suggest furniture materials that match the texture of the wallpaper and flooring.

[0054] The analysis unit can use the emotion estimation function to analyze the emotions felt by the user when shooting a video and suggest a furniture arrangement based on those emotions. The analysis unit, for example, analyzes the user's facial expressions when analyzing the video data and uses the emotion estimation function to understand the emotions felt by the user. For example, if the user is relaxed, it suggests a furniture arrangement that allows for relaxation. The analysis unit also uses the emotion estimation function to analyze the emotions felt by the user when shooting a video and suggests a furniture arrangement based on those emotions. For example, if the user is having fun, it suggests a furniture arrangement that creates a fun atmosphere. The analysis unit also analyzes the user's emotions when analyzing the video data and suggests a furniture arrangement based on those emotions. For example, if the user is calm, it suggests a furniture arrangement that creates a calm atmosphere. This makes it possible to arrange furniture based on the user's emotions.

[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 analysis unit collects the user's lifestyle data, and the generation AI can use that information to suggest optimal furniture layouts. For example, if a user frequently hosts home parties, the AI ​​will suggest furniture layouts that will allow guests to be comfortable. If a user works remotely, the AI ​​will suggest desk and chair layouts that will increase work efficiency. Furthermore, if the user has pets, the AI ​​will suggest furniture layouts that will keep the pets safe. This makes it possible to arrange furniture to suit the user's lifestyle.

[0057] The analysis unit allows the generation AI to propose optimal furniture layouts based on the user's past purchase history. For example, new furniture is proposed taking into account the style and color of furniture previously purchased by the user. The system also proposes optimal layouts based on the layout of furniture previously purchased by the user. Furthermore, the system considers how frequently the user has used furniture previously and proposes layouts centered on frequently used furniture. This makes it possible to arrange furniture based on the user's past purchase history.

[0058] The analysis unit allows the generation AI to suggest optimal furniture layouts based on the user's hobbies and interests. For example, if the user's hobby is reading, it will suggest a comfortable reading space. If the user enjoys music, it will suggest furniture layouts that take the acoustic environment into consideration. Furthermore, if the user is interested in art, it will suggest the optimal layout for displaying artwork. This makes it possible to arrange furniture according to the user's hobbies and interests.

[0059] The analysis unit allows the generation AI to propose optimal furniture layouts based on the user's family composition. For example, in a household with children, it will propose furniture layouts that prioritize safety. In a household with elderly people, it will propose furniture layouts that take barrier-free access into consideration. Furthermore, in a household with pets, it will propose furniture layouts that allow the pets to live comfortably. This makes it possible to arrange furniture according to the family composition.

[0060] The analysis unit allows the generative AI to suggest optimal furniture layouts based on the user's health condition. For example, for a user with lower back pain, it will suggest chair or sofa layouts that are gentle on the lower back. For a user with allergies, it will suggest furniture layouts that reduce allergens. Furthermore, for a user with poor eyesight, it will suggest furniture layouts that improve visibility. This makes it possible to arrange furniture according to the user's health condition.

[0061] The analysis unit uses the emotion estimation function to analyze the emotions felt by the user when shooting the video, and can suggest furniture colors and designs based on those emotions. For example, if the user is relaxed, furniture with relaxing colors and designs is suggested. If the user is having fun, furniture with colors and designs that create a fun atmosphere is suggested. Furthermore, if the user is calm, furniture with colors and designs that create a calm atmosphere is suggested. In this way, furniture colors and designs can be suggested based on the user's emotions.

[0062] The analysis unit uses the emotion estimation function to analyze the emotions felt by the user when shooting a video and can suggest lighting based on those emotions. For example, if the user is relaxed, the analysis unit suggests relaxing lighting. If the user is having fun, the analysis unit suggests lighting that creates a fun atmosphere. If the user is calm, the analysis unit suggests lighting that creates a calm atmosphere. This makes it possible to suggest lighting based on the user's emotions.

[0063] The analysis unit uses the emotion estimation function to analyze the emotions felt by the user while shooting the video and can suggest interior accessories based on those emotions. For example, if the user is relaxed, the analysis unit suggests interior accessories that will help them relax. If the user is having fun, the analysis unit suggests interior accessories that will create a fun atmosphere. If the user is calm, the analysis unit suggests interior accessories that will create a calm atmosphere. This makes it possible to suggest interior accessories based on the user's emotions.

[0064] The analysis unit uses the emotion estimation function to analyze the emotion the user felt when shooting the video and can suggest curtains or blinds based on that emotion. For example, if the user is relaxed, relaxing curtains or blinds are suggested. If the user is having fun, curtains or blinds that create a fun atmosphere are suggested. Furthermore, if the user is calm, curtains or blinds that create a calm atmosphere are suggested. This makes it possible to suggest curtains or blinds based on the user's emotions.

[0065] The analysis unit uses the emotion estimation function to analyze the emotion the user felt when shooting the video and can suggest plants and flowers based on that emotion. For example, if the user is relaxed, relaxing plants and flowers are suggested. If the user is having fun, plants and flowers that create a fun atmosphere are suggested. Furthermore, if the user is calm, plants and flowers that create a calm atmosphere are suggested. This makes it possible to suggest plants and flowers based on the user's emotions.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The user shoots a video using the video capture unit. For example, the user can capture a video from each of the four corners of a room toward the center, allowing them to grasp the overall picture of the room. The video capture unit can also capture videos using the camera on a smartphone or tablet. Step 2: The analysis unit analyzes the video data captured by the video capture unit. For example, the generation AI recognizes the size, shape, and existing furniture layout of the room in the video and generates a 3D model of the room. The analysis unit can also take into account the resolution and frame rate of the video data during its analysis. Step 3: The proposal unit proposes furniture and interior layouts based on the room information analyzed by the analysis unit. For example, the generation AI selects optimal furniture and interior items based on the user's preferences and budget, and places them on the 3D model of the room. The proposal unit also allows the user to set price ranges and designs within the app. Step 4: The purchasing unit purchases the furniture and interior items suggested by the suggestion unit. For example, when the user clicks on the suggested furniture, they can access detailed information and a purchase page. The purchasing unit can also provide online shopping and payment methods.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0081] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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]

[0135] 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 video capture unit for a user to capture video; an analysis unit that analyzes the video data captured by the video capture unit; a proposal unit that proposes furniture or interior layouts based on the room information analyzed by the analysis unit; a purchasing unit that purchases the furniture or interior items suggested by the suggestion unit. A system characterized by:

2. The analysis unit When shooting video, the lighting and acoustic environment of the room are simultaneously recorded, and the generative AI uses this information to suggest optimal furniture arrangements.

2. The system of claim 1.

3. The proposal unit When suggesting furniture and interior design, the generative AI learns from the user's past purchase history and preferences to provide more personalized suggestions.

2. The system of claim 1.

4. The purchasing department Add a feature that allows users to virtually experience furniture placement in AR when reviewing proposals.

2. The system of claim 1.

5. The video shooting unit Analyzes the emotions users feel while taking photos and provides a shooting guide to bring out positive emotions 2. The system of claim 1.

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