system

Abstract

The system according to this embodiment aims to efficiently understand the layout of a room and the placement of objects, and to make suggestions that meet the user's needs. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a reception unit, and a proposal unit. The acquisition unit acquires images or spatial data of a room. The analysis unit analyzes the data acquired by the acquisition unit to understand the room layout and the arrangement of objects. The reception unit accepts user requests based on the layout and arrangement understood by the analysis unit. The proposal unit proposes the arrangement of objects based on the requests received by the reception unit and the layout and arrangement understood by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently grasp the layout of a room and the arrangement of objects and make proposals according to the user's desires.

[0005] The system according to the embodiment aims to efficiently grasp the layout of a room and the arrangement of objects and make proposals according to the user's desires.

Means for Solving the Problems

[0006] [[ID=四十五]] The system according to this embodiment comprises an acquisition unit, an analysis unit, a reception unit, and a proposal unit. The acquisition unit acquires images or spatial data of a room. The analysis unit analyzes the data acquired by the acquisition unit to understand the room layout and the arrangement of objects. The reception unit receives user requests based on the layout and arrangement understood by the analysis unit. The proposal unit proposes the arrangement of objects based on the requests received by the reception unit and the layout and arrangement understood by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently grasp the layout of a room and the placement of objects, and make suggestions that meet the user's needs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI system according to an embodiment of the present invention is a system that proposes the placement of objects using image recognition and spatial awareness. This system acquires images and spatial data of a room using cameras and sensors, and the AI ​​analyzes the acquired data to understand the room layout and the placement of objects. Furthermore, the AI ​​proposes the optimal placement of objects based on the user's requests and the characteristics of the room, and this proposal is provided to the user in a visually easy-to-understand manner. For example, images and spatial data of a room are acquired using cameras and sensors. A camera that captures the overall view of the room and sensors that measure the dimensions of the room are used. This data is input to the AI. Next, the AI ​​analyzes the acquired data to understand the room layout and the placement of objects. The AI ​​uses image recognition technology to identify furniture and items in the room and understands their position and size. It also uses spatial awareness technology to analyze the dimensions and shape of the room. This allows the overall layout of the room to be understood. Furthermore, the AI ​​proposes the optimal placement of objects based on the user's requests and the characteristics of the room. For example, if a user inputs a request such as "I want to place a new sofa in the living room," the AI ​​considers the room layout and the placement of existing furniture and proposes the optimal place to put the sofa. This proposal is provided to the user in a visually easy-to-understand manner. For example, 3D models and AR (augmented reality) can be used to allow users to visualize the layout as if they were actually in the room. This enables users to efficiently optimize the room layout. For instance, when purchasing new furniture, users can check in advance where it will be placed most effectively. They can also receive suggestions for making the most efficient use of their room space. This improves the usability of the room and enhances the quality of life. As a result, the AI ​​system can efficiently optimize the room layout based on the user's requests.

[0029] The AI ​​system according to this embodiment comprises an acquisition unit, an analysis unit, a reception unit, and a proposal unit. The acquisition unit acquires images or spatial data of a room. The images or spatial data of a room include, but are not limited to, 2D images, 3D scan data, LiDAR data, etc. The acquisition unit can, for example, use a camera to capture an overall view of the room and acquire image data. The acquisition unit can also use sensors to measure the dimensions of the room and acquire spatial data. For example, the acquisition unit can use a LiDAR sensor to acquire 3D scan data of the room. The analysis unit analyzes the data acquired by the acquisition unit to understand the layout of the room and the placement of objects. The analysis unit uses image recognition technology to identify furniture and objects in the room and understand their positions and sizes. For example, the analysis unit uses an object detection algorithm to identify furniture and objects and understand their positions. The analysis unit can also use a deep learning model to understand the sizes of furniture and objects. Furthermore, the analysis unit uses spatial understanding technology to analyze the dimensions and shape of the room. For example, the analysis unit analyzes 3D scan data to understand the dimensions and shape of the room. The reception unit receives user requests based on the layout and arrangement determined by the analysis unit. The reception unit can receive user requests through an interface. For example, the reception unit can receive user requests in text format. The reception unit can also accept voice input and gesture input. The proposal unit proposes the placement of items based on the requests received by the reception unit and the layout and arrangement determined by the analysis unit. The proposal unit proposes the optimal placement of items based on the user's requests. For example, if the user inputs a request such as "I want to place a new sofa in the living room," the proposal unit will consider the room layout and the arrangement of existing furniture and propose the optimal place to put the sofa. The proposal unit provides the proposal in a visually easy-to-understand format. For example, the proposal unit can use 3D models or AR to allow the user to check the placement as if they were actually in the room. As a result, the AI ​​system according to the embodiment can efficiently optimize the room layout based on the user's requests.

[0030] The proposal function can provide users with suggestions in a visually easy-to-understand format using 3D models or AR. For example, the proposal function can use 3D rendering technology to display room layouts and object placements as 3D models. For instance, if a user wants to place a new sofa, the proposal function can generate a 3D model of the room and visually display the sofa's placement. The proposal function can also use AR kits to allow users to check the placement as if they were actually in the room. For example, the proposal function can overlay a 3D model onto a real-world image of the room through the camera of a smartphone or tablet. This allows users to visually check the room layout and decide on the optimal placement of objects. Furthermore, the proposal function has a function to explain the effects of the suggestions to the user. For example, the proposal function visually shows how the suggested arrangement makes effective use of the room space. It also explains how the suggested arrangement improves the usability of the room. In this way, the proposal function can provide suggestions in a user-friendly format and efficiently optimize the room layout.

[0031] The analysis unit can identify furniture and objects in a room using image recognition technology and determine their location and size. For example, the analysis unit can identify furniture and objects in a room using an object detection algorithm. For example, the analysis unit can identify furniture and objects in a room with high accuracy using a deep learning model. Furthermore, the analysis unit can also determine the location of furniture and objects using an object detection algorithm. For example, the analysis unit analyzes image data of the room to identify the location of furniture and objects. In addition, the analysis unit can determine the size of furniture and objects using a deep learning model. For example, the analysis unit analyzes image data of the room to determine the size of furniture and objects. This allows the analysis unit to accurately determine the location and size of furniture and objects in a room. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input image data of the room into a generating AI and have the generating AI perform the identification of furniture and objects.

[0032] The analysis unit can analyze the dimensions and shape of a room using spatial awareness technology. For example, the analysis unit can analyze the dimensions and shape of a room using 3D scanning technology. For example, the analysis unit can acquire 3D scan data of a room using LiDAR technology and analyze the dimensions and shape of the room. The analysis unit can also analyze the dimensions and shape of a room with high accuracy using spatial awareness technology. For example, the analysis unit can analyze 3D scan data of a room to identify the dimensions and shape of the room. This allows the analysis unit to accurately analyze the dimensions and shape of the room. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input 3D scan data of a room into a generating AI and have the generating AI perform the analysis of the dimensions and shape of the room.

[0033] The suggestion unit can propose the placement of items based on the user's requests, the characteristics of the room, and the arrangement of existing furniture. For example, if the user inputs a request such as "I want to place a new sofa in the living room," the suggestion unit will consider the room layout and the arrangement of existing furniture and propose the optimal place to put the sofa. The suggestion unit proposes the optimal placement of items considering the characteristics of the room and the arrangement of existing furniture. For example, the suggestion unit will consider characteristics such as the size, shape, and purpose of the room to propose the optimal placement. The suggestion unit can also propose the placement of new furniture considering the arrangement of existing furniture. For example, the suggestion unit will identify the placement of new furniture based on the arrangement of existing furniture. In this way, the suggestion unit can propose the optimal placement of items based on the user's requests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's requests, the characteristics of the room, and the arrangement of existing furniture into a generating AI and have the generating AI execute a proposal for the optimal placement of items.

[0034] The proposal unit can explain the effects of the proposal to the user. For example, the proposal unit can visually demonstrate how the proposed layout makes effective use of the room space. For example, the proposal unit can explain how the proposed layout improves the usability of the room. The proposal unit can also explain how the proposed layout improves the aesthetics of the room. For example, the proposal unit can visually demonstrate how the proposed layout affects the room's design. In this way, the proposal unit can explain the effects of the proposal to the user and deepen their understanding of the proposal. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the effects of the proposed layout into a generating AI and have the generating AI perform the explanation of the effects.

[0035] The image acquisition unit can select the optimal image acquisition method according to the room's lighting conditions and time of day. For example, during daylight hours when natural light is present, the acquisition unit adjusts the camera's exposure to acquire a clear image. For example, at night, the acquisition unit adjusts the camera's sensitivity to match the room's lighting conditions to acquire an image with less noise. Furthermore, if the room's lighting changes, the acquisition unit can automatically adjust the camera settings in real time to always acquire the optimal image. For example, the acquisition unit automatically adjusts the camera settings according to the room's lighting conditions and time of day to acquire the optimal image. In this way, the acquisition unit can acquire the optimal image according to the room's lighting conditions and time of day. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the room's lighting conditions and time of day into a generating AI and have the generating AI select the optimal image acquisition method.

[0036] The acquisition unit can simultaneously acquire environmental data such as room temperature and humidity during image acquisition and utilize it for analysis. For example, when acquiring images, the acquisition unit can acquire data from a room temperature sensor and reflect temperature changes in the analysis. For example, the acquisition unit can acquire data from a humidity sensor and optimize furniture placement when humidity is high. The acquisition unit can also acquire environmental data in real time and make placement suggestions according to the room conditions. For example, the acquisition unit can acquire data from temperature and humidity sensors in real time and utilize the room's environmental data in the analysis. This allows the acquisition unit to make more accurate placement suggestions by utilizing environmental data in the analysis. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input environmental data into a generating AI and have the generating AI perform data processing for use in analysis.

[0037] The data acquisition unit can acquire data at the optimal timing, taking into account the user's lifestyle patterns. For example, the data acquisition unit can acquire images of the room at the time the user wakes up in the morning and analyze the morning layout. For example, the data acquisition unit can acquire images of the room at the time the user returns home and analyze the layout after returning home. The data acquisition unit can also acquire images of the room at the time the user goes to bed and analyze the nighttime layout. For example, the data acquisition unit acquires data at the optimal timing, taking into account the user's lifestyle patterns. As a result, the data acquisition unit can acquire data at the optimal timing according to the user's lifestyle patterns. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's lifestyle pattern data into a generating AI and have the generating AI determine the optimal data acquisition timing.

[0038] The data acquisition unit can also acquire and integrate data from the user's smart home devices during the acquisition process. For example, the acquisition unit can acquire data from smart lights and reflect the room's lighting conditions in the analysis. For example, the acquisition unit can acquire data from smart thermostats and reflect the room's temperature conditions in the analysis. The acquisition unit can also acquire data from smart speakers and reflect the user's voice commands in the analysis. For example, the acquisition unit can acquire data from smart home devices in real time and perform analysis according to the room's conditions. This allows the acquisition unit to perform more detailed analysis by integrating data from smart home devices. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data from smart home devices into a generating AI and have the generating AI perform data integration and analysis.

[0039] The analysis unit can track changes in a room by referring to past data during analysis, thereby gaining a more accurate understanding of the layout. For example, the analysis unit can track changes in furniture placement by referring to past image data. For example, the analysis unit can track changes in room dimensions by referring to past spatial data. The analysis unit can also track changes in the state of a room by referring to past environmental data. For example, the analysis unit can identify changes in a room by referring to past layout data. In this way, the analysis unit can track changes in a room by referring to past data and gain a more accurate understanding of the layout. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the tracking of changes in the room.

[0040] The analysis unit can apply different analysis algorithms depending on the intended use of the room during analysis. For example, in the case of a living room, the analysis unit may apply an analysis algorithm that prioritizes a relaxing layout. For example, in the case of an office, the analysis unit may apply an analysis algorithm that prioritizes a layout that prioritizes work efficiency. Furthermore, in the case of a kitchen, the analysis unit may apply an analysis algorithm that prioritizes a layout that prioritizes traffic flow. In short, the analysis unit applies the most appropriate analysis algorithm depending on the intended use of the room. This allows the analysis unit to determine a more appropriate layout by applying the most appropriate analysis algorithm according to the intended use of the room. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input room intended use data into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0041] The analysis unit can optimize furniture placement by considering the acoustic characteristics of the room during analysis. For example, the analysis unit can analyze the acoustic characteristics of the room and propose a placement that minimizes sound reflection. For example, the analysis unit can analyze the acoustic characteristics of the room and propose a placement that optimizes sound diffusion. The analysis unit can also analyze the acoustic characteristics of the room and propose a placement that optimizes sound absorption. For example, the analysis unit optimizes furniture placement by considering the acoustic characteristics of the room. In this way, the analysis unit can propose a placement that optimizes sound reflection and diffusion by considering the acoustic characteristics of the room. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input room acoustic characteristic data into a generating AI and have the generating AI propose the optimal furniture placement.

[0042] The analysis unit can propose a layout that takes into account the energy efficiency of the room during the analysis. For example, the analysis unit can analyze the energy efficiency of the room and propose a layout that makes the most of natural light. For example, the analysis unit can analyze the energy efficiency of the room and propose a layout that optimizes heating and cooling efficiency. The analysis unit can also analyze the energy efficiency of the room and propose a layout that minimizes power consumption. For example, the analysis unit can propose an optimal layout that takes the energy efficiency of the room into consideration. In this way, the analysis unit can propose an energy-efficient layout by taking the energy efficiency of the room into consideration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input room energy efficiency data into a generating AI and have the generating AI propose an optimal layout.

[0043] The reception department can make optimal suggestions by referring to the user's past request history when receiving a request. For example, the reception department can make optimal suggestions based on requests previously entered by the user. For example, the reception department can predict and suggest similar requests from the user's past request history. The reception department can also analyze the user's past request history and make the most efficient suggestions. For example, the reception department can make optimal suggestions by referring to the user's past request history. In this way, the reception department can make optimal suggestions by referring to the user's past request history. Some or all of the above processing in the reception department may be performed using AI, for example, or without using AI. For example, the reception department can input the user's past request history data into a generating AI and have the generating AI execute the optimal suggestion.

[0044] The reception desk can ask customized questions based on the user's lifestyle and hobbies when receiving a request. For example, the reception desk can ask questions about the optimal furniture arrangement, taking into account the user's lifestyle. For example, the reception desk can ask questions about furniture arrangement related to the user's hobbies, taking into account the user's hobbies. The reception desk can also ask customized questions based on the user's lifestyle and hobbies and make optimal suggestions. For example, the reception desk can ask customized questions based on the user's lifestyle and hobbies. This allows the reception desk to make more appropriate suggestions by asking customized questions based on the user's lifestyle and hobbies. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's lifestyle and hobby data into a generating AI and have the generating AI generate customized questions.

[0045] The reception unit can accept user voice input and gesture input when receiving requests. For example, if the user enters a request by voice, the reception unit will accept the request using voice recognition technology. For example, if the user enters a request by gesture, the reception unit will accept the request using gesture recognition technology. Furthermore, if the user enters a request using a combination of voice and gesture, the reception unit can integrate both inputs and accept the request. For example, the reception unit will accept user voice input and gesture input using voice recognition technology and gesture recognition technology. In this way, the reception unit can provide a user-friendly interface by accepting voice input and gesture input. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user voice data and gesture data into a generating AI and have the generating AI perform the request reception.

[0046] The reception desk can make suggestions when receiving requests, taking into account the user's family structure and whether or not they have pets. For example, the reception desk can consider the user's family structure and suggest a layout that allows all family members to live comfortably. For example, if the user has pets, the reception desk can suggest a layout that takes the pet's safety into consideration. The reception desk can also suggest the optimal layout based on the user's family structure and whether or not they have pets. For example, the reception desk can suggest the optimal layout based on the user's family structure and whether or not they have pets. In this way, the reception desk can make more appropriate suggestions by considering the user's family structure and whether or not they have pets. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's family structure and pet status data into a generating AI and have the generating AI execute the optimal suggestion.

[0047] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past feedback. For example, the suggestion unit can improve the accuracy of its suggestions based on feedback previously provided by the user. For example, the suggestion unit can analyze the user's preferences and tendencies from their past feedback and make optimal suggestions. The suggestion unit can also customize the content of its suggestions by referring to the user's past feedback. For example, the suggestion unit can improve the accuracy of its suggestions based on the user's past feedback. In this way, the suggestion unit can improve the accuracy of its suggestions by referring to the user's past feedback. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past feedback data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0048] The suggestion unit can make different suggestions depending on the season and events in the room. For example, during Christmas, the suggestion unit will suggest the placement of a Christmas tree. For example, during birthday season, the suggestion unit will suggest furniture arrangements for a party. The suggestion unit can also suggest the optimal furniture arrangement for each season. For example, the suggestion unit will make different suggestions depending on the season and events in the room. In this way, the suggestion unit can suggest the optimal arrangement for the user by making suggestions according to the season and events in the room. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input seasonal and event data into a generating AI and have the generating AI execute the optimal suggestion.

[0049] The proposal unit can send notifications to the user's smartphone or tablet when a proposal is submitted. For example, the proposal unit can send a notification to the user's smartphone when the proposal is completed. For example, the proposal unit can display the proposal content on the user's tablet and prompt them to confirm it. The proposal unit can also send notifications to the user's device in real time if there are updates to the proposal. For example, the proposal unit can notify the user when the proposal is completed or updated. This allows the proposal unit to notify the user when the proposal is completed or updated, enabling the user to quickly review the proposal. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input notification data into a generating AI and have the generating AI execute the sending of notifications.

[0050] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can analyze the user's social media posts and suggest furniture arrangements that suit their preferences. For example, the suggestion unit can make suggestions based on the user's social media activity to match events that the user is interested in. The suggestion unit can also make suggestions by referring to the preferences of the user's social media followers and friends. For example, the suggestion unit can analyze the user's social media activity and make relevant suggestions. In this way, the suggestion unit can make suggestions based on the user's preferences and interests by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input social media data into a generating AI and have the generating AI execute relevant suggestions.

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

[0052] The analysis unit can analyze the acoustic characteristics of a room and propose an arrangement that minimizes sound reflection. For example, it can consider the materials of the room's walls and ceiling and propose an arrangement that optimizes sound absorption. It can also adjust the placement of furniture to optimize sound diffusion. Furthermore, the analysis unit can use acoustic simulation to evaluate the acoustic characteristics of the room in advance and propose an optimal arrangement. In this way, the analysis unit can optimize the acoustic environment by proposing an arrangement that takes the acoustic characteristics of the room into consideration.

[0053] The image acquisition unit can select the optimal image acquisition method according to the room's lighting conditions and time of day. For example, during the daytime when natural light is present, it adjusts the camera's exposure to acquire a clear image. At night, it adjusts the camera's sensitivity to match the room's lighting conditions to acquire an image with less noise. Furthermore, if the room's lighting changes, the acquisition unit can automatically adjust the camera settings in real time to always acquire the optimal image. As a result, the acquisition unit can acquire the optimal image according to the room's lighting conditions and time of day.

[0054] The analysis unit can track changes in a room by referring to past data, enabling a more accurate understanding of the layout. For example, it can track changes in furniture placement by referring to past image data, track changes in room dimensions by referring to past spatial data, and track changes in room conditions by referring to past environmental data. This allows the analysis unit to track changes in a room and understand the layout more accurately by referring to past data.

[0055] The proposal team can improve the accuracy of their proposals by referring to past user feedback. For example, they can improve the accuracy of proposals based on feedback previously provided by users. By analyzing preferences and trends from past user feedback, they can make optimal proposals. They can also customize the content of proposals by referring to past user feedback. In this way, the proposal team can improve the accuracy of their proposals by referring to past user feedback.

[0056] The analysis unit can propose layouts that take into account the energy efficiency of a room. For example, it can analyze the energy efficiency of a room and propose a layout that maximizes the use of natural light. It can also propose a layout that optimizes heating and cooling efficiency. Furthermore, it can propose a layout that minimizes power consumption. In this way, the analysis unit can propose highly energy-efficient layouts by considering the energy efficiency of the room.

[0057] The proposal department can offer different suggestions depending on the season and events in the room. For example, during Christmas, it can suggest the placement of a Christmas tree. During birthday season, it can suggest furniture arrangements for parties. It can also suggest the optimal furniture arrangement for each season. In this way, the proposal department can suggest the best possible arrangement for the user by providing suggestions tailored to the season and events in the room.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The acquisition unit acquires images or spatial data of the room. These include 2D images, 3D scan data, and LiDAR data. The acquisition unit uses a camera to capture an overall view of the room and acquires image data. It can also use sensors to measure the dimensions of the room and acquire spatial data. For example, a LiDAR sensor can be used to acquire 3D scan data of the room. Step 2: The analysis unit analyzes the data acquired by the acquisition unit to understand the room layout and the placement of objects. The analysis unit uses image recognition technology to identify furniture and objects in the room and understand their location and size. For example, it uses object detection algorithms or deep learning models to determine the location and size of furniture and objects. It also uses spatial awareness technology to analyze the dimensions and shape of the room. For example, it analyzes 3D scan data to understand the dimensions and shape of the room. Step 3: The reception desk receives user requests based on the layout and arrangement determined by the analysis desk. The reception desk can receive user requests through an interface. For example, it can receive requests entered by the user in text format, as well as voice input and gesture input. Step 4: The proposal department proposes the placement of items based on the requests received by the reception department and the layout and arrangements identified by the analysis department. The proposal department proposes the optimal placement of items based on the user's requests. For example, if a user inputs a request such as "I want to place a new sofa in the living room," the proposal department will consider the room layout and the arrangement of existing furniture and propose the optimal place to put the sofa. The proposal department provides the proposal in a visually easy-to-understand format. For example, it may use 3D models or AR to allow the user to check the placement as if they were actually in the room.

[0060] (Example of form 2) An AI system according to an embodiment of the present invention is a system that proposes the placement of objects using image recognition and spatial awareness. This system acquires images and spatial data of a room using cameras and sensors, and the AI ​​analyzes the acquired data to understand the room layout and the placement of objects. Furthermore, the AI ​​proposes the optimal placement of objects based on the user's requests and the characteristics of the room, and this proposal is provided to the user in a visually easy-to-understand manner. For example, images and spatial data of a room are acquired using cameras and sensors. A camera that captures the overall view of the room and sensors that measure the dimensions of the room are used. This data is input to the AI. Next, the AI ​​analyzes the acquired data to understand the room layout and the placement of objects. The AI ​​uses image recognition technology to identify furniture and items in the room and understands their position and size. It also uses spatial awareness technology to analyze the dimensions and shape of the room. This allows the overall layout of the room to be understood. Furthermore, the AI ​​proposes the optimal placement of objects based on the user's requests and the characteristics of the room. For example, if a user inputs a request such as "I want to place a new sofa in the living room," the AI ​​considers the room layout and the placement of existing furniture and proposes the optimal place to put the sofa. This proposal is provided to the user in a visually easy-to-understand manner. For example, 3D models and AR (augmented reality) can be used to allow users to visualize the layout as if they were actually in the room. This enables users to efficiently optimize the room layout. For instance, when purchasing new furniture, users can check in advance where it will be placed most effectively. They can also receive suggestions for making the most efficient use of their room space. This improves the usability of the room and enhances the quality of life. As a result, the AI ​​system can efficiently optimize the room layout based on the user's requests.

[0061] The AI ​​system according to this embodiment comprises an acquisition unit, an analysis unit, a reception unit, and a proposal unit. The acquisition unit acquires images or spatial data of a room. The images or spatial data of a room include, but are not limited to, 2D images, 3D scan data, LiDAR data, etc. The acquisition unit can, for example, use a camera to capture an overall view of the room and acquire image data. The acquisition unit can also use sensors to measure the dimensions of the room and acquire spatial data. For example, the acquisition unit can use a LiDAR sensor to acquire 3D scan data of the room. The analysis unit analyzes the data acquired by the acquisition unit to understand the layout of the room and the placement of objects. The analysis unit uses image recognition technology to identify furniture and objects in the room and understand their positions and sizes. For example, the analysis unit uses an object detection algorithm to identify furniture and objects and understand their positions. The analysis unit can also use a deep learning model to understand the sizes of furniture and objects. Furthermore, the analysis unit uses spatial understanding technology to analyze the dimensions and shape of the room. For example, the analysis unit analyzes 3D scan data to understand the dimensions and shape of the room. The reception unit receives user requests based on the layout and arrangement determined by the analysis unit. The reception unit can receive user requests through an interface. For example, the reception unit can receive user requests in text format. The reception unit can also accept voice input and gesture input. The proposal unit proposes the placement of items based on the requests received by the reception unit and the layout and arrangement determined by the analysis unit. The proposal unit proposes the optimal placement of items based on the user's requests. For example, if the user inputs a request such as "I want to place a new sofa in the living room," the proposal unit will consider the room layout and the arrangement of existing furniture and propose the optimal place to put the sofa. The proposal unit provides the proposal in a visually easy-to-understand format. For example, the proposal unit can use 3D models or AR to allow the user to check the placement as if they were actually in the room. As a result, the AI ​​system according to the embodiment can efficiently optimize the room layout based on the user's requests.

[0062] The proposal function can provide users with suggestions in a visually easy-to-understand format using 3D models or AR. For example, the proposal function can use 3D rendering technology to display room layouts and object placements as 3D models. For instance, if a user wants to place a new sofa, the proposal function can generate a 3D model of the room and visually display the sofa's placement. The proposal function can also use AR kits to allow users to check the placement as if they were actually in the room. For example, the proposal function can overlay a 3D model onto a real-world image of the room through the camera of a smartphone or tablet. This allows users to visually check the room layout and decide on the optimal placement of objects. Furthermore, the proposal function has a function to explain the effects of the suggestions to the user. For example, the proposal function visually shows how the suggested arrangement makes effective use of the room space. It also explains how the suggested arrangement improves the usability of the room. In this way, the proposal function can provide suggestions in a user-friendly format and efficiently optimize the room layout.

[0063] The analysis unit can identify furniture and objects in a room using image recognition technology and determine their location and size. For example, the analysis unit can identify furniture and objects in a room using an object detection algorithm. For example, the analysis unit can identify furniture and objects in a room with high accuracy using a deep learning model. Furthermore, the analysis unit can also determine the location of furniture and objects using an object detection algorithm. For example, the analysis unit analyzes image data of the room to identify the location of furniture and objects. In addition, the analysis unit can determine the size of furniture and objects using a deep learning model. For example, the analysis unit analyzes image data of the room to determine the size of furniture and objects. This allows the analysis unit to accurately determine the location and size of furniture and objects in a room. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input image data of the room into a generating AI and have the generating AI perform the identification of furniture and objects.

[0064] The analysis unit can analyze the dimensions and shape of a room using spatial awareness technology. For example, the analysis unit can analyze the dimensions and shape of a room using 3D scanning technology. For example, the analysis unit can acquire 3D scan data of a room using LiDAR technology and analyze the dimensions and shape of the room. The analysis unit can also analyze the dimensions and shape of a room with high accuracy using spatial awareness technology. For example, the analysis unit can analyze 3D scan data of a room to identify the dimensions and shape of the room. This allows the analysis unit to accurately analyze the dimensions and shape of the room. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input 3D scan data of a room into a generating AI and have the generating AI perform the analysis of the dimensions and shape of the room.

[0065] The suggestion unit can propose the placement of items based on the user's requests, the characteristics of the room, and the arrangement of existing furniture. For example, if the user inputs a request such as "I want to place a new sofa in the living room," the suggestion unit will consider the room layout and the arrangement of existing furniture and propose the optimal place to put the sofa. The suggestion unit proposes the optimal placement of items considering the characteristics of the room and the arrangement of existing furniture. For example, the suggestion unit will consider characteristics such as the size, shape, and purpose of the room to propose the optimal placement. The suggestion unit can also propose the placement of new furniture considering the arrangement of existing furniture. For example, the suggestion unit will identify the placement of new furniture based on the arrangement of existing furniture. In this way, the suggestion unit can propose the optimal placement of items based on the user's requests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's requests, the characteristics of the room, and the arrangement of existing furniture into a generating AI and have the generating AI execute a proposal for the optimal placement of items.

[0066] The proposal unit can explain the effects of the proposal to the user. For example, the proposal unit can visually demonstrate how the proposed layout makes effective use of the room space. For example, the proposal unit can explain how the proposed layout improves the usability of the room. The proposal unit can also explain how the proposed layout improves the aesthetics of the room. For example, the proposal unit can visually demonstrate how the proposed layout affects the room's design. In this way, the proposal unit can explain the effects of the proposal to the user and deepen their understanding of the proposal. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the effects of the proposed layout into a generating AI and have the generating AI perform the explanation of the effects.

[0067] The acquisition unit can estimate the user's emotions and adjust the timing of image and spatial data acquisition based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit can periodically acquire image and spatial data and continuously monitor changes in the room. For example, if the user is stressed, the acquisition unit can reduce the acquisition frequency to alleviate the user's burden. Furthermore, if the user is excited, the acquisition unit can acquire image and spatial data in real time and perform immediate analysis. For example, the acquisition unit can estimate the user's emotions using technologies such as facial recognition and voice analysis, and adjust the data acquisition timing based on the results. This allows the acquisition unit to acquire data at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user emotion data into the generative AI and have the generative AI adjust the data acquisition timing.

[0068] The image acquisition unit can select the optimal image acquisition method according to the room's lighting conditions and time of day. For example, during daylight hours when natural light is present, the acquisition unit adjusts the camera's exposure to acquire a clear image. For example, at night, the acquisition unit adjusts the camera's sensitivity to match the room's lighting conditions to acquire an image with less noise. Furthermore, if the room's lighting changes, the acquisition unit can automatically adjust the camera settings in real time to always acquire the optimal image. For example, the acquisition unit automatically adjusts the camera settings according to the room's lighting conditions and time of day to acquire the optimal image. In this way, the acquisition unit can acquire the optimal image according to the room's lighting conditions and time of day. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the room's lighting conditions and time of day into a generating AI and have the generating AI select the optimal image acquisition method.

[0069] The acquisition unit can simultaneously acquire environmental data such as room temperature and humidity during image acquisition and utilize it for analysis. For example, when acquiring images, the acquisition unit can acquire data from a room temperature sensor and reflect temperature changes in the analysis. For example, the acquisition unit can acquire data from a humidity sensor and optimize furniture placement when humidity is high. The acquisition unit can also acquire environmental data in real time and make placement suggestions according to the room conditions. For example, the acquisition unit can acquire data from temperature and humidity sensors in real time and utilize the room's environmental data in the analysis. This allows the acquisition unit to make more accurate placement suggestions by utilizing environmental data in the analysis. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input environmental data into a generating AI and have the generating AI perform data processing for use in analysis.

[0070] The data acquisition unit can estimate the user's emotions and determine the priority of data to acquire based on the estimated emotions. For example, if the user is relaxed, the data acquisition unit will prioritize acquiring images of the entire room. For example, if the user is stressed, the data acquisition unit will prioritize acquiring images of a specific area and perform rapid analysis. Furthermore, if the user is excited, the data acquisition unit can simultaneously acquire data from multiple sensors and perform detailed analysis. For example, the data acquisition unit can estimate the user's emotions using technologies such as facial recognition or voice analysis and determine the priority of data based on the results. This enables efficient data acquisition by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not using AI. For example, the data acquisition unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0071] The data acquisition unit can acquire data at the optimal timing, taking into account the user's lifestyle patterns. For example, the data acquisition unit can acquire images of the room at the time the user wakes up in the morning and analyze the morning layout. For example, the data acquisition unit can acquire images of the room at the time the user returns home and analyze the layout after returning home. The data acquisition unit can also acquire images of the room at the time the user goes to bed and analyze the nighttime layout. For example, the data acquisition unit acquires data at the optimal timing, taking into account the user's lifestyle patterns. As a result, the data acquisition unit can acquire data at the optimal timing according to the user's lifestyle patterns. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's lifestyle pattern data into a generating AI and have the generating AI determine the optimal data acquisition timing.

[0072] The data acquisition unit can also acquire and integrate data from the user's smart home devices during the acquisition process. For example, the acquisition unit can acquire data from smart lights and reflect the room's lighting conditions in the analysis. For example, the acquisition unit can acquire data from smart thermostats and reflect the room's temperature conditions in the analysis. The acquisition unit can also acquire data from smart speakers and reflect the user's voice commands in the analysis. For example, the acquisition unit can acquire data from smart home devices in real time and perform analysis according to the room's conditions. This allows the acquisition unit to perform more detailed analysis by integrating data from smart home devices. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data from smart home devices into a generating AI and have the generating AI perform data integration and analysis.

[0073] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to grasp a highly accurate layout. For example, if the user is stressed, the analysis unit can perform a rapid analysis to grasp a basic layout. Furthermore, if the user is excited, the analysis unit can perform a real-time analysis to immediately grasp the layout. For example, the analysis unit can estimate the user's emotions using technologies such as facial recognition and voice analysis, and adjust the accuracy of the analysis based on the results. In this way, the analysis unit can grasp a more appropriate layout by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the analysis accuracy.

[0074] The analysis unit can track changes in a room by referring to past data during analysis, thereby gaining a more accurate understanding of the layout. For example, the analysis unit can track changes in furniture placement by referring to past image data. For example, the analysis unit can track changes in room dimensions by referring to past spatial data. The analysis unit can also track changes in the state of a room by referring to past environmental data. For example, the analysis unit can identify changes in a room by referring to past layout data. In this way, the analysis unit can track changes in a room by referring to past data and gain a more accurate understanding of the layout. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the tracking of changes in the room.

[0075] The analysis unit can apply different analysis algorithms depending on the intended use of the room during analysis. For example, in the case of a living room, the analysis unit may apply an analysis algorithm that prioritizes a relaxing layout. For example, in the case of an office, the analysis unit may apply an analysis algorithm that prioritizes a layout that prioritizes work efficiency. Furthermore, in the case of a kitchen, the analysis unit may apply an analysis algorithm that prioritizes a layout that prioritizes traffic flow. In short, the analysis unit applies the most appropriate analysis algorithm depending on the intended use of the room. This allows the analysis unit to determine a more appropriate layout by applying the most appropriate analysis algorithm according to the intended use of the room. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input room intended use data into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results in a way that is easy for the user to understand. For example, if the user is stressed, the analysis unit can display concise analysis results to reduce the user's burden. Also, if the user is excited, the analysis unit can provide a visually stimulating display method to attract the user's interest. For example, the analysis unit can estimate the user's emotions using technologies such as facial recognition and voice analysis, and adjust the display method of the analysis results based on the results. In this way, the analysis unit can provide results in a way that is easy for the user to understand by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the analysis results are displayed.

[0077] The analysis unit can optimize furniture placement by considering the acoustic characteristics of the room during analysis. For example, the analysis unit can analyze the acoustic characteristics of the room and propose a placement that minimizes sound reflection. For example, the analysis unit can analyze the acoustic characteristics of the room and propose a placement that optimizes sound diffusion. The analysis unit can also analyze the acoustic characteristics of the room and propose a placement that optimizes sound absorption. For example, the analysis unit optimizes furniture placement by considering the acoustic characteristics of the room. In this way, the analysis unit can propose a placement that optimizes sound reflection and diffusion by considering the acoustic characteristics of the room. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input room acoustic characteristic data into a generating AI and have the generating AI propose the optimal furniture placement.

[0078] The analysis unit can propose a layout that takes into account the energy efficiency of the room during the analysis. For example, the analysis unit can analyze the energy efficiency of the room and propose a layout that makes the most of natural light. For example, the analysis unit can analyze the energy efficiency of the room and propose a layout that optimizes heating and cooling efficiency. The analysis unit can also analyze the energy efficiency of the room and propose a layout that minimizes power consumption. For example, the analysis unit can propose an optimal layout that takes the energy efficiency of the room into consideration. In this way, the analysis unit can propose an energy-efficient layout by taking the energy efficiency of the room into consideration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input room energy efficiency data into a generating AI and have the generating AI propose an optimal layout.

[0079] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, if the user is relaxed, the reception desk may provide detailed input options and suggest a customizable request processing method. For example, if the user is stressed, the reception desk may provide a simple interface and minimize the input steps. The reception desk can also prioritize voice input and process requests quickly if the user is in a hurry. For example, the reception desk may estimate the user's emotions using technologies such as facial recognition or voice analysis and adjust the request processing method based on the results. This allows the reception desk to process requests in the most optimal way for the user by adjusting the processing method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​adjust how requests are received.

[0080] The reception department can make optimal suggestions by referring to the user's past request history when receiving a request. For example, the reception department can make optimal suggestions based on requests previously entered by the user. For example, the reception department can predict and suggest similar requests from the user's past request history. The reception department can also analyze the user's past request history and make the most efficient suggestions. For example, the reception department can make optimal suggestions by referring to the user's past request history. In this way, the reception department can make optimal suggestions by referring to the user's past request history. Some or all of the above processing in the reception department may be performed using AI, for example, or without using AI. For example, the reception department can input the user's past request history data into a generating AI and have the generating AI execute the optimal suggestion.

[0081] The reception desk can ask customized questions based on the user's lifestyle and hobbies when receiving a request. For example, the reception desk can ask questions about the optimal furniture arrangement, taking into account the user's lifestyle. For example, the reception desk can ask questions about furniture arrangement related to the user's hobbies, taking into account the user's hobbies. The reception desk can also ask customized questions based on the user's lifestyle and hobbies and make optimal suggestions. For example, the reception desk can ask customized questions based on the user's lifestyle and hobbies. This allows the reception desk to make more appropriate suggestions by asking customized questions based on the user's lifestyle and hobbies. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's lifestyle and hobby data into a generating AI and have the generating AI generate customized questions.

[0082] The reception desk can estimate the user's emotions and prioritize requests based on those emotions. For example, if the user is relaxed, the reception desk will prioritize detailed requests. For example, if the user is stressed, the reception desk will prioritize basic requests. Also, if the user is in a hurry, the reception desk can prioritize requests that require a quick response. For example, the reception desk can estimate the user's emotions using technologies such as facial recognition or voice analysis and prioritize requests based on the results. This allows the reception desk to efficiently receive requests by prioritizing requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of requests.

[0083] The reception unit can accept user voice input and gesture input when receiving requests. For example, if the user enters a request by voice, the reception unit will accept the request using voice recognition technology. For example, if the user enters a request by gesture, the reception unit will accept the request using gesture recognition technology. Furthermore, if the user enters a request using a combination of voice and gesture, the reception unit can integrate both inputs and accept the request. For example, the reception unit will accept user voice input and gesture input using voice recognition technology and gesture recognition technology. In this way, the reception unit can provide a user-friendly interface by accepting voice input and gesture input. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user voice data and gesture data into a generating AI and have the generating AI perform the request reception.

[0084] The reception desk can make suggestions when receiving requests, taking into account the user's family structure and whether or not they have pets. For example, the reception desk can consider the user's family structure and suggest a layout that allows all family members to live comfortably. For example, if the user has pets, the reception desk can suggest a layout that takes the pet's safety into consideration. The reception desk can also suggest the optimal layout based on the user's family structure and whether or not they have pets. For example, the reception desk can suggest the optimal layout based on the user's family structure and whether or not they have pets. In this way, the reception desk can make more appropriate suggestions by considering the user's family structure and whether or not they have pets. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's family structure and pet status data into a generating AI and have the generating AI execute the optimal suggestion.

[0085] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions in a way that is easy for the user to understand. For example, if the user is stressed, the suggestion unit can provide concise suggestions to reduce the user's burden. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions to attract the user's interest. For example, the suggestion unit can estimate the user's emotions using technologies such as facial recognition or voice analysis, and adjust the way it presents its suggestions based on the results. This allows the suggestion unit to provide suggestions in a way that is easy for the user to understand by adjusting the presentation of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way the proposal is expressed.

[0086] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past feedback. For example, the suggestion unit can improve the accuracy of its suggestions based on feedback previously provided by the user. For example, the suggestion unit can analyze the user's preferences and tendencies from their past feedback and make optimal suggestions. The suggestion unit can also customize the content of its suggestions by referring to the user's past feedback. For example, the suggestion unit can improve the accuracy of its suggestions based on the user's past feedback. In this way, the suggestion unit can improve the accuracy of its suggestions by referring to the user's past feedback. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past feedback data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0087] The suggestion unit can make different suggestions depending on the season and events in the room. For example, during Christmas, the suggestion unit will suggest the placement of a Christmas tree. For example, during birthday season, the suggestion unit will suggest furniture arrangements for a party. The suggestion unit can also suggest the optimal furniture arrangement for each season. For example, the suggestion unit will make different suggestions depending on the season and events in the room. In this way, the suggestion unit can suggest the optimal arrangement for the user by making suggestions according to the season and events in the room. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input seasonal and event data into a generating AI and have the generating AI execute the optimal suggestion.

[0088] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will prioritize detailed suggestions. For example, if the user is stressed, the suggestion unit will prioritize basic suggestions. Also, if the user is in a hurry, the suggestion unit can prioritize suggestions that require a quick response. For example, the suggestion unit can estimate the user's emotions using technologies such as facial recognition or voice analysis and determine the priority of suggestions based on the results. This allows the suggestion unit to make efficient suggestions by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0089] The proposal unit can send notifications to the user's smartphone or tablet when a proposal is submitted. For example, the proposal unit can send a notification to the user's smartphone when the proposal is completed. For example, the proposal unit can display the proposal content on the user's tablet and prompt them to confirm it. The proposal unit can also send notifications to the user's device in real time if there are updates to the proposal. For example, the proposal unit can notify the user when the proposal is completed or updated. This allows the proposal unit to notify the user when the proposal is completed or updated, enabling the user to quickly review the proposal. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input notification data into a generating AI and have the generating AI execute the sending of notifications.

[0090] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can analyze the user's social media posts and suggest furniture arrangements that suit their preferences. For example, the suggestion unit can make suggestions based on the user's social media activity to match events that the user is interested in. The suggestion unit can also make suggestions by referring to the preferences of the user's social media followers and friends. For example, the suggestion unit can analyze the user's social media activity and make relevant suggestions. In this way, the suggestion unit can make suggestions based on the user's preferences and interests by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input social media data into a generating AI and have the generating AI execute relevant suggestions. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, analysis unit, reception unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires images and spatial data of the room using the camera 42 and sensors of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired data to understand the room layout and the placement of objects. The reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's requests. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal placement of objects based on the user's requests and provides it visually through the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, analysis unit, reception unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires images and spatial data of the room using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired data to understand the room layout and the placement of objects. The reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's requests. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal placement of objects based on the user's requests and provides it visually through the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, analysis unit, reception unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires images and spatial data of the room using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired data to understand the room layout and the placement of objects. The reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user requests. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal placement of objects based on the user's requests and provides it visually through the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, analysis unit, reception unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires images and spatial data of the room using the camera 42 and sensors of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data to understand the room layout and the placement of objects. The reception unit is implemented by the control unit 46A of the robot 414 and receives user requests. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal placement of objects based on the user's requests, and provides it visually through the display and speaker of the robot 414.

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

[0092] The analysis unit can analyze the acoustic characteristics of a room and propose an arrangement that minimizes sound reflection. For example, it can consider the materials of the room's walls and ceiling and propose an arrangement that optimizes sound absorption. It can also adjust the placement of furniture to optimize sound diffusion. Furthermore, the analysis unit can use acoustic simulation to evaluate the acoustic characteristics of the room in advance and propose an optimal arrangement. In this way, the analysis unit can optimize the acoustic environment by proposing an arrangement that takes the acoustic characteristics of the room into consideration.

[0093] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions in an easy-to-understand format. If the user is stressed, it can provide concise suggestions to reduce the user's burden. Furthermore, if the user is excited, it can provide visually stimulating suggestions to capture the user's interest. In this way, the suggestion function can provide suggestions in an easy-to-understand format by adjusting the presentation style according to the user's emotions.

[0094] The image acquisition unit can select the optimal image acquisition method according to the room's lighting conditions and time of day. For example, during the daytime when natural light is present, it adjusts the camera's exposure to acquire a clear image. At night, it adjusts the camera's sensitivity to match the room's lighting conditions to acquire an image with less noise. Furthermore, if the room's lighting changes, the acquisition unit can automatically adjust the camera settings in real time to always acquire the optimal image. As a result, the acquisition unit can acquire the optimal image according to the room's lighting conditions and time of day.

[0095] The analysis unit can track changes in a room by referring to past data, enabling a more accurate understanding of the layout. For example, it can track changes in furniture placement by referring to past image data, track changes in room dimensions by referring to past spatial data, and track changes in room conditions by referring to past environmental data. This allows the analysis unit to track changes in a room and understand the layout more accurately by referring to past data.

[0096] The proposal team can improve the accuracy of their proposals by referring to past user feedback. For example, they can improve the accuracy of proposals based on feedback previously provided by users. By analyzing preferences and trends from past user feedback, they can make optimal proposals. They can also customize the content of proposals by referring to past user feedback. In this way, the proposal team can improve the accuracy of their proposals by referring to past user feedback.

[0097] The data acquisition unit can estimate the user's emotions and determine the priority of data to acquire based on the estimated emotions. For example, if the user is relaxed, it will prioritize acquiring images of the entire room. If the user is stressed, it will prioritize acquiring images of a specific area and perform rapid analysis. Furthermore, if the user is excited, it can acquire data from multiple sensors simultaneously and perform detailed analysis. In this way, the data acquisition unit can efficiently acquire data by determining the priority of data according to the user's emotions.

[0098] The analysis unit can propose layouts that take into account the energy efficiency of a room. For example, it can analyze the energy efficiency of a room and propose a layout that maximizes the use of natural light. It can also propose a layout that optimizes heating and cooling efficiency. Furthermore, it can propose a layout that minimizes power consumption. In this way, the analysis unit can propose highly energy-efficient layouts by considering the energy efficiency of the room.

[0099] The reception desk can estimate the user's emotions and adjust the request processing method based on that estimation. For example, if the user is relaxed, it can offer detailed input options and suggest a customizable request processing method. If the user is stressed, it can provide a simple interface and minimize the input steps. Furthermore, if the user is in a hurry, it can prioritize voice input and process the request quickly. This allows the reception desk to process requests in the most optimal way for the user by adjusting the processing method according to their emotions.

[0100] The proposal department can offer different suggestions depending on the season and events in the room. For example, during Christmas, it can suggest the placement of a Christmas tree. During birthday season, it can suggest furniture arrangements for parties. It can also suggest the optimal furniture arrangement for each season. In this way, the proposal department can suggest the best possible arrangement for the user by providing suggestions tailored to the season and events in the room.

[0101] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is relaxed, it can perform a detailed analysis to determine a highly accurate layout. If the user is stressed, it can perform a rapid analysis to determine a basic layout. Furthermore, if the user is excited, it can perform a real-time analysis to immediately determine the layout. In this way, the analysis unit can determine a more appropriate layout by adjusting the accuracy of the analysis according to the user's emotions.

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

[0103] Step 1: The acquisition unit acquires images or spatial data of the room. These include 2D images, 3D scan data, and LiDAR data. The acquisition unit uses a camera to capture an overall view of the room and acquires image data. It can also use sensors to measure the dimensions of the room and acquire spatial data. For example, a LiDAR sensor can be used to acquire 3D scan data of the room. Step 2: The analysis unit analyzes the data acquired by the acquisition unit to understand the room layout and the placement of objects. The analysis unit uses image recognition technology to identify furniture and objects in the room and understand their location and size. For example, it uses object detection algorithms or deep learning models to determine the location and size of furniture and objects. It also uses spatial awareness technology to analyze the dimensions and shape of the room. For example, it analyzes 3D scan data to understand the dimensions and shape of the room. Step 3: The reception desk receives user requests based on the layout and arrangement determined by the analysis desk. The reception desk can receive user requests through an interface. For example, it can receive requests entered by the user in text format, as well as voice input and gesture input. Step 4: The proposal department proposes the placement of items based on the requests received by the reception department and the layout and arrangements identified by the analysis department. The proposal department proposes the optimal placement of items based on the user's requests. For example, if a user inputs a request such as "I want to place a new sofa in the living room," the proposal department will consider the room layout and the arrangement of existing furniture and propose the optimal place to put the sofa. The proposal department provides the proposal in a visually easy-to-understand format. For example, it may use 3D models or AR to allow the user to check the placement as if they were actually in the room.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0175] [Explanation of symbols]

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

Claims

1. An acquisition unit that acquires images or spatial data of a room, An analysis unit analyzes the data acquired by the acquisition unit to understand the room layout and the placement of objects, Based on the layout and arrangement determined by the aforementioned analysis unit, a reception unit receives user requests, The system includes a proposal unit that proposes the placement of items based on requests received by the reception unit and the layout and arrangement understood by the analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, We provide suggestions to users in a visually easy-to-understand format using 3D models or AR. The system according to feature 1.

3. The aforementioned analysis unit, Using image recognition technology, we identify furniture and objects in a room and determine their location and size. The system according to feature 1.

4. The aforementioned analysis unit, We use spatial awareness technology to analyze the dimensions and shape of a room. The system according to feature 1.

5. The aforementioned proposal section is, Based on user requests, we propose the placement of items based on the characteristics of the room and the arrangement of existing furniture. The system according to feature 1.

6. The aforementioned proposal section is, Explain the effects of the proposal to the user. The system according to feature 1.

7. The acquisition unit is, It estimates the user's emotions and adjusts the timing of image and spatial data acquisition based on the estimated user emotions. The system according to feature 1.

8. The acquisition unit is, The optimal image acquisition method is selected according to the room's lighting conditions and time of day. The system according to feature 1.

9. The acquisition unit is, During acquisition, environmental data such as room temperature and humidity are also acquired and used for analysis. The system according to feature 1.

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