system

Abstract

The system according to this embodiment aims to recommend optimal furniture and decorations based on the user's preferences and the characteristics of the room. [Solution] The system according to the embodiment comprises an input unit, an analysis unit, a recommendation unit, and an AR unit. The input unit receives the user's preferences and the characteristics of the room. The analysis unit analyzes the information input by the input unit. The recommendation unit recommends furniture and decorations based on the information analyzed by the analysis unit. The AR unit places the furniture and decorations recommended by the recommendation unit using AR technology.

Description

Technical Field

[0006] , , ,

[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, the method including 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, recommendations for furniture and decorations based on user preferences and room characteristics have not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend optimal furniture and decorations based on user preferences and room characteristics.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, an analysis unit, a recommendation unit, and an AR unit. The input unit receives the user's preferences and room characteristics as input. The analysis unit analyzes the information input by the input unit. The recommendation unit recommends furniture and decorations based on the information analyzed by the analysis unit. The AR unit places the furniture and decorations recommended by the recommendation unit using augmented reality (AR) technology. [Effects of the Invention]

[0007] The system according to this embodiment can recommend optimal furniture and decorations based on the user's preferences and the characteristics of the room. [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 labeled 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 applicable to the communication I / F include wireless communication standards such as 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) The AI ​​assistant interior coordination advisory system according to an embodiment of the present invention is a system that recommends furniture and decorations that take into account the user's preferences, room size, and shape. In this system, the user inputs their preferences and room characteristics, and the analysis unit analyzes the information collected by the input unit. Based on the analyzed information, the recommendation unit recommends furniture or decorations, and the user can virtually place them in the actual room using AR technology, allowing them to view a virtual interior. Furthermore, it also includes a suggestion unit that provides coordination suggestions based on trends and design styles. For example, it can suggest the optimal coordination for the user based on the latest interior trends or specific design styles. The recommendation unit can also recommend furniture and decorations based on the user's lifestyle and areas of interest. For example, if the user has pets, it can recommend pet-friendly furniture. Additionally, the input unit has a function to estimate the user's emotions and determine the priority of input items based on the estimated emotions. For example, if the user is feeling stressed, it will prioritize recommending relaxing interiors. It also has a function to estimate preferences based on the user's social media activity and automatically input them. This allows the user to find an interior that suits their preferences without any hassle. Finally, the recommendation system also has a function to estimate the user's emotions and recommend furniture and decorations based on those emotions. For example, if the user is happy, it can recommend furniture in bright colors. In this way, it can provide interior coordination that matches the user's emotions. As a result, the AI ​​assistant interior coordination advisory system recommends furniture and decorations based on the user's preferences and the characteristics of the room, and allows the user to virtually place them using AR technology to check the interior.

[0029] The AI ​​assistant interior coordination advisory system according to this embodiment comprises an input unit, an analysis unit, a recommendation unit, and an AR unit. The input unit receives input from the user's preferences and the characteristics of the room. User preferences include, for example, color, style, and function, but are not limited to such examples. Room characteristics include, for example, size, shape, and use, but are not limited to such examples. The input unit provides, for example, an interface for the user to input preferences and room characteristics. The analysis unit analyzes the information input by the input unit. The analysis is performed, for example, using data analysis methods and algorithms, but is not limited to such examples. The analysis unit performs, for example, an analysis to select the optimal furniture and decorations based on the user's preferences and the characteristics of the room. The recommendation unit recommends furniture or decorations based on the information analyzed by the analysis unit. The recommendation is performed, for example, based on recommendation algorithms and evaluation criteria, but is not limited to such examples. The recommendation unit recommends the optimal furniture and decorations based, for example, the user's preferences and the characteristics of the room. The AR unit places the furniture or decorations recommended by the recommendation unit using AR technology. AR technology includes, but is not limited to, the devices and software used. For example, the AR unit virtually places furniture and decorations in the user's room, allowing the user to view the interior. Thus, the AI ​​assistant interior coordination advisory system according to the embodiment recommends furniture and decorations based on the user's preferences and the characteristics of the room, and virtually places them using AR technology, allowing the user to view the interior. Some or all of the above-described processes in the input unit, analysis unit, recommendation unit, and AR unit may be performed using AI, for example, or without AI. For example, the input unit provides an interface for inputting the user's preferences and the characteristics of the room, the analysis unit analyzes the information input by the input unit, the recommendation unit recommends furniture or decorations based on the information analyzed by the analysis unit, and the AR unit places the furniture or decorations recommended by the recommendation unit using AR technology.

[0030] The input section allows users to input their preferences and room characteristics. User preferences include, but are not limited to, color, style, and functionality. Specifically, users can select their preferred color palette and interior style (e.g., modern, classic, minimalist) through the interface. They can also input their requirements regarding furniture functionality (e.g., ample storage space, a comfortable sofa). Room characteristics include, but are not limited to, size, shape, and purpose. Specifically, users can input the room's area, ceiling height, window location and number, and room purpose (living room, bedroom, office, etc.). The input section provides, for example, an interface for users to input their preferences and room characteristics. This interface could be provided as a touchscreen, voice input, or even a smartphone app or web application. This allows users to intuitively input information, and the system can then proceed to the next step based on that information. Furthermore, the input section also has a function to save and reuse data previously entered by the user. This saves users the trouble of entering the same information repeatedly.

[0031] The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, data analysis methods and algorithms, but is not limited to these examples. Specifically, it performs analysis to select the optimal furniture and decorations based on the user's preferences and the characteristics of the room. For example, the analysis unit uses a clustering algorithm to classify the user's preferences into several categories and proposes the most suitable interior style for each category. It also performs analysis to optimize furniture placement and size based on the characteristics of the room. For example, if the room is small, it can recommend compact and multi-functional furniture. Furthermore, the analysis unit can use AI to learn from the user's input data and perform more accurate analysis. For example, it can predict the user's preferences based on past user selection history and feedback, and provide more appropriate suggestions. Based on these analysis results, the analysis unit provides information to the subsequent recommendation unit.

[0032] The recommendation unit recommends furniture or decorations based on information analyzed by the analysis unit. Recommendations are made based on, for example, recommendation algorithms and evaluation criteria, but are not limited to these examples. Specifically, it recommends optimal furniture and decorations based on the user's preferences and the characteristics of the room. For example, if the user prefers a modern style, the recommendation unit will suggest furniture and decorations with a modern design. It can also recommend furniture of appropriate size and placement depending on the size and shape of the room. The recommendation unit can use AI to learn the user's preferences and room characteristics, enabling more accurate recommendations. For example, it prioritizes recommending furniture and decorations that the user is likely to like based on their past selection history and feedback. The recommendation unit also has a function to provide feedback to the user on the recommended furniture and decorations. This allows the system to further learn the user's preferences and improve the accuracy of future recommendations.

[0033] The AR section uses augmented reality (AR) technology to place furniture or decorations recommended by the recommendation section. AR technology includes, but is not limited to, the devices and software used. Specifically, it virtually places furniture and decorations in the user's room, allowing the user to check the interior design. For example, virtual furniture and decorations can be overlaid onto the real room using the camera of a smartphone or tablet. This allows users to visually check how furniture will be placed in their room before actually purchasing it. The AR section also provides the ability for users to freely move and resize the virtually placed furniture and decorations. This allows users to find the optimal placement through trial and error. Furthermore, the AR section has a function to place multiple pieces of furniture and decorations simultaneously and check the overall balance. This allows users to check the coordination of the entire room at once. In addition, the AR section provides a function to save information on the virtual furniture and decorations placed by the user and review it later. This allows users to review placements they have tried and use this information to help them make a final decision.

[0034] The proposal unit can make suggestions based on trends and design styles. For example, the proposal unit can suggest the best coordination for the user based on the latest interior trends. For example, the proposal unit can suggest the best interior for the user, taking into account the latest designs and trendy colors. The proposal unit can also suggest the best coordination for the user based on a specific design style. For example, the proposal unit can suggest the best interior for the user based on design styles such as modern, classic, or minimalist. This makes it possible to make coordination suggestions based on trends and design styles. 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 suggest the best coordination for the user based on the latest interior trends and a specific design style.

[0035] The recommendation system can recommend furniture and decorations based on the user's lifestyle and areas of interest at the time of input. For example, if the user has pets, the recommendation system can recommend pet-friendly furniture. For example, the recommendation system can recommend furniture made from pet-friendly materials and designs. Also, if the user has children, the recommendation system can recommend child-safe furniture. For example, the recommendation system can recommend furniture made from child-safe materials and designs. Furthermore, if the user enjoys outdoor activities, the recommendation system can recommend furniture made from natural materials. For example, the recommendation system can recommend furniture made from natural materials and designs. This allows for the recommendation of more appropriate furniture and decorations based on the user's lifestyle and areas of interest. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system recommends the most suitable furniture and decorations based on the user's lifestyle and areas of interest.

[0036] The input unit can analyze the user's past interior selection history and select an appropriate input method. For example, the input unit can automatically suggest relevant input items based on the interior style the user has previously selected. The input unit can also prioritize displaying input methods (voice, text, etc.) that the user has previously used. Furthermore, the input unit can predict and suggest interior styles to be used during specific time periods based on the user's past selection history. This allows the input unit to provide the optimal input method based on the user's past selection history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit analyzes the user's past interior selection history and selects an appropriate input method.

[0037] The input section can customize input items based on the user's current lifestyle and areas of interest during input. For example, if the user owns a pet, the input section will prioritize displaying input items related to pet-friendly interiors. For example, the input section will prioritize displaying input items related to interiors using pet-friendly materials and designs. Furthermore, if the user has children, the input section can prioritize displaying input items related to child-safe interiors. For example, the input section will prioritize displaying input items related to interiors using child-safe materials and designs. Additionally, if the user enjoys outdoor activities, the input section can prioritize displaying input items related to interiors using natural materials. For example, the input section will prioritize displaying input items related to interiors using natural materials and designs. This allows the input items to be customized based on the user's lifestyle and areas of interest. Some or all of the above processing in the input section may be performed using AI, for example, or without AI. For example, the input section customizes input items based on the user's current lifestyle and areas of interest.

[0038] The input section can prioritize displaying input items that are highly relevant based on the user's geographical location information during input. For example, if the user lives in a cold region, the input section will prioritize displaying input items related to interiors using warm materials. For example, the input section will prioritize displaying input items related to interiors using warm materials for users living in cold regions. The input section can also prioritize displaying input items related to compact interiors if the user lives in an urban area. For example, the input section will prioritize displaying input items related to compact interiors for users living in urban areas. Furthermore, if the user lives by the sea, the input section can prioritize displaying input items related to sea-themed interiors. For example, the input section will prioritize displaying input items related to sea-themed interiors for users living by the sea. This allows for the prioritization of highly relevant input items based on the user's geographical location information. Some or all of the above processing in the input section may be performed using AI, for example, or without AI. For example, the input section prioritizes displaying highly relevant input items based on the user's geographical location information.

[0039] The input unit can automatically suggest relevant input items based on the user's social media activity during input. For example, the input unit can suggest relevant input items based on the interior styles the user has "liked" on social media. For example, the input unit can suggest input items related to the interior styles the user has "liked". The input unit can also suggest relevant input items based on the styles of interior designers the user follows. For example, the input unit can suggest input items related to the styles of interior designers the user follows. Furthermore, the input unit can also suggest relevant input items based on interior images the user has shared. For example, the input unit can suggest input items related to interior images the user has shared. This allows the system to automatically suggest relevant input items based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit automatically suggests relevant input items based on the user's social media activity.

[0040] The analysis unit can improve the accuracy of the analysis based on the user's past interior selection history. For example, the analysis unit can customize the analysis results based on the interior style the user has previously selected. The analysis unit can also analyze the user's preferences from their past selection history and reflect them in the analysis results. For example, the analysis unit can analyze the user's preferences from their past selection history and reflect them in the analysis results. Furthermore, the analysis unit can adjust the analysis results by taking into account the interior style the user has previously avoided. For example, the analysis unit can adjust the analysis results by taking into account the interior style the user has previously avoided. This allows the accuracy of the analysis to be improved based on the user's past selection history. 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 improve the accuracy of the analysis based on the user's past interior selection history.

[0041] The analysis unit can customize its analysis algorithm based on the user's lifestyle and areas of interest during analysis. For example, if the user owns a pet, the analysis unit will prioritize analyzing pet-friendly interiors. For example, the analysis unit will prioritize analyzing interiors made of pet-friendly materials and designs. Also, if the user has children, the analysis unit can prioritize analyzing interiors made of child-safe materials and designs. Furthermore, if the user enjoys outdoor activities, the analysis unit can prioritize analyzing interiors made with natural materials. For example, the analysis unit will prioritize analyzing interiors made with natural materials and designs. This allows the analysis algorithm to be customized based on the user's lifestyle and areas of interest. 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 customizes its analysis algorithm based on the user's lifestyle and areas of interest.

[0042] The analysis unit can improve the accuracy of its analysis based on the user's geographical location information. For example, if the user lives in a cold region, the analysis unit will prioritize analyzing interiors made with warm materials. For example, the analysis unit will prioritize analyzing interiors made with warm materials for users living in cold regions. The analysis unit can also prioritize analyzing compact interiors for users living in urban areas. For example, the analysis unit will prioritize analyzing compact interiors for users living in urban areas. Furthermore, if the user lives by the sea, the analysis unit can prioritize analyzing interiors with a sea theme. For example, the analysis unit will prioritize analyzing interiors with a sea theme for users living by the sea. This allows the analysis accuracy to be improved based on the user's geographical location information. 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 improves the accuracy of its analysis based on the user's geographical location information.

[0043] The analysis unit can improve the accuracy of its analysis based on the user's social media activity. For example, the analysis unit can customize the analysis results based on the interior styles that the user has "liked" on social media. The analysis unit can also customize the analysis results based on the styles of interior designers that the user follows. Furthermore, the analysis unit can also customize the analysis results based on interior images that the user has shared. This allows the accuracy of the analysis to be improved based on the user's social media activity. 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 improves the accuracy of the analysis based on the user's social media activity.

[0044] The recommendation system can improve the accuracy of recommendations based on the user's past interior design selection history. For example, the recommendation system can recommend furniture related to the interior design style the user has previously selected. The recommendation system can also analyze the user's preferences from their past selection history and reflect this in the recommendation results. Furthermore, the recommendation system can adjust the recommendation results by considering the interior design style the user has previously avoided. This allows for improved recommendation accuracy based on the user's past selection history. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can improve the accuracy of recommendations based on the user's past interior design selection history.

[0045] The recommendation system can customize its recommendation algorithm based on the user's lifestyle and areas of interest. For example, if the user has pets, the recommendation system will prioritize recommending pet-friendly furniture. For example, the recommendation system will prioritize recommending furniture made from pet-friendly materials and designs. Similarly, if the user has children, the recommendation system can prioritize recommending furniture made from child-safe materials and designs. Furthermore, if the user enjoys outdoor activities, the recommendation system can prioritize recommending furniture made from natural materials. For example, the recommendation system will prioritize recommending furniture made from natural materials and designs. This allows the recommendation algorithm to be customized based on the user's lifestyle and areas of interest. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system customizes its recommendation algorithm based on the user's lifestyle and areas of interest.

[0046] The recommendation system can improve the accuracy of recommendations based on the user's geographical location. For example, if a user lives in a cold climate, the recommendation system will prioritize recommending furniture made from warm materials. The recommendation system can also prioritize recommending compact furniture if the user lives in an urban area. Furthermore, if a user lives by the sea, the recommendation system can prioritize recommending ocean-themed furniture. This allows for improved recommendation accuracy based on the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system improves the accuracy of recommendations based on the user's geographical location.

[0047] The recommendation system can improve the accuracy of recommendations based on the user's social media activity. For example, the recommendation system can recommend furniture related to interior styles that the user has "liked" on social media. The recommendation system can also recommend furniture related to the styles of interior designers that the user follows. Furthermore, the recommendation system can recommend furniture related to interior images that the user has shared. This allows for improved recommendation accuracy based on the user's social media activity. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can improve the accuracy of recommendations based on the user's social media activity.

[0048] The AR unit can improve the accuracy of its AR display based on the user's past interior selection history. For example, the AR unit provides relevant AR displays based on the interior styles the user has previously selected. The AR unit can also analyze the user's preferences from their past selection history and reflect them in the AR display. For example, the AR unit analyzes the user's preferences from their past selection history and reflects them in the AR display. Furthermore, the AR unit can adjust the AR display considering interior styles the user has previously avoided. For example, the AR unit adjusts the AR display considering interior styles the user has previously avoided. This improves the accuracy of the AR display based on the user's past selection history. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit improves the accuracy of the display based on the user's past interior selection history.

[0049] The AR unit can customize the display algorithm based on the user's lifestyle and areas of interest when displaying AR content. For example, if the user has pets, the AR unit can prioritize displaying pet-friendly interiors in AR. For example, the AR unit can prioritize displaying interiors made of pet-friendly materials and designs in AR. Also, if the user has children, the AR unit can prioritize displaying child-safe interiors in AR. For example, the AR unit can prioritize displaying interiors made of child-safe materials and designs in AR. Furthermore, if the user enjoys outdoor activities, the AR unit can prioritize displaying interiors made with natural materials in AR. For example, the AR unit can prioritize displaying interiors made with natural materials and designs in AR. This allows the AR display algorithm to be customized based on the user's lifestyle and areas of interest. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit customizes the display algorithm based on the user's lifestyle and areas of interest.

[0050] The AR unit can improve the accuracy of the display based on the user's geographical location information when displaying AR content. For example, if the user lives in a cold region, the AR unit can prioritize displaying interiors made with warm materials. For example, the AR unit can prioritize displaying interiors made with warm materials for users living in cold regions. The AR unit can also prioritize displaying compact interiors for users living in urban areas. For example, the AR unit can prioritize displaying compact interiors for users living in urban areas. Furthermore, if the user lives by the sea, the AR unit can prioritize displaying interiors with a sea theme. For example, the AR unit can prioritize displaying interiors with a sea theme for users living by the sea. This improves the accuracy of the AR display based on the user's geographical location information. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit improves the accuracy of the display based on the user's geographical location information.

[0051] The AR unit can improve the accuracy of AR displays based on the user's social media activity. For example, the AR unit can provide relevant AR displays based on interior styles that the user has "liked" on social media. For example, the AR unit can provide AR displays related to interior styles that the user has "liked". The AR unit can also provide relevant AR displays based on the styles of interior designers that the user follows. For example, the AR unit can provide AR displays related to the styles of interior designers that the user follows. Furthermore, the AR unit can provide relevant AR displays based on interior images that the user has shared. For example, the AR unit can provide AR displays related to interior images that the user has shared. This allows for improved accuracy of AR displays based on the user's social media activity. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit improves the accuracy of displays based on the user's social media activity.

[0052] The proposal function can adjust the level of detail of its proposals based on trends and design styles. For example, the proposal function can provide detailed proposals based on the latest interior trends. For example, the proposal function can provide detailed proposals based on the latest designs and trendy colors. The proposal function can also provide detailed proposals based on specific design styles. For example, the proposal function can provide detailed proposals based on design styles such as modern, classic, and minimalist. Furthermore, the proposal function can also provide detailed proposals based on user preferences. For example, the proposal function can provide detailed proposals based on user preferences. This allows for adjustment of the level of detail of proposals based on trends and design styles. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function adjusts the level of detail of proposals based on trends and design styles.

[0053] The suggestion unit can customize its suggestion algorithm based on the user's lifestyle and areas of interest when making suggestions. For example, if the user has pets, the suggestion unit will prioritize suggesting pet-friendly interiors. For example, the suggestion unit will prioritize suggesting interiors made with pet-friendly materials and designs. Also, if the user has children, the suggestion unit can prioritize suggesting child-safe interiors. For example, the suggestion unit will prioritize suggesting interiors made with child-safe materials and designs. Furthermore, if the user enjoys outdoor activities, the suggestion unit can prioritize suggesting interiors made with natural materials. For example, the suggestion unit will prioritize suggesting interiors made with natural materials and designs. This allows the suggestion algorithm to be customized based on the user's lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit customizes its suggestion algorithm based on the user's lifestyle and areas of interest.

[0054] The proposal department can determine the priority of proposals based on trends and changes in design style when making proposals. For example, the proposal department may prioritize proposals based on the latest interior trends. For example, the proposal department may prioritize proposals based on the latest designs and trendy colors. The proposal department can also prioritize proposals based on specific design styles. For example, the proposal department may prioritize proposals based on design styles such as modern, classic, or minimalist. Furthermore, the proposal department may also prioritize proposals based on user preferences. For example, the proposal department may prioritize proposals based on user preferences. This allows the proposal department to determine the priority of proposals based on trends and changes in design style. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department determines the priority of proposals based on trends and changes in design style.

[0055] The suggestion function can improve the accuracy of its suggestions based on the user's social media activity. For example, the suggestion function can provide relevant suggestions based on the interior styles that the user has "liked" on social media. For example, the suggestion function can provide relevant suggestions based on the interior styles that the user has "liked". For example, the suggestion function can provide relevant suggestions based on the interior styles that the user follows. For example, the suggestion function can provide relevant suggestions based on the interior styles that the user follows. Furthermore, the suggestion function can provide relevant suggestions based on interior images that the user has shared. For example, the suggestion function can provide relevant suggestions based on the user's social media activity. This allows the accuracy of suggestions to be improved based on the user's social media activity. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can improve the accuracy of its suggestions based on the user's social media activity.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The AI ​​assistant advisory system for interior design can also provide recommendations that take the user's health condition into account. For example, if a user has allergies, it can recommend furniture and decorations made from allergen-free materials. If a user suffers from back pain, it can recommend chairs and beds with back-friendly designs. Furthermore, if a user has a visual impairment, it can recommend furniture with colors and designs that are easily recognizable. This allows for the provision of more appropriate interior design based on the user's health condition.

[0058] The AI ​​assistant advisory system for interior design can further enhance recommendations by taking into account the user's environmental awareness. For example, if a user is interested in environmental protection, it can recommend furniture and decorations made from renewable materials. It can also recommend energy-efficient lighting and appliances. Furthermore, if a user supports local products, it can recommend furniture and decorations made by local artisans. This allows for the provision of more sustainable interiors based on the user's environmental consciousness.

[0059] The AI ​​assistant advisory system for interior design can further enhance recommendations by taking into account the user's hobbies and skills. For example, if the user enjoys music, it can recommend room layouts with good acoustics and furniture for storing musical instruments. If the user enjoys painting, it can recommend optimal locations and lighting for displaying artwork. Furthermore, if the user enjoys reading, it can recommend lighting and bookshelf placement suitable for reading. This allows for the provision of more personalized interiors based on the user's hobbies and skills.

[0060] The AI ​​assistant advisory system for interior design can further enhance recommendations by taking into account the user's family structure. For example, if the user lives with elderly people, it can recommend elderly-friendly furniture and decorations. If the user has many children, it can recommend furniture arrangements that ensure safe play space for children. Furthermore, if the user has pets, it can recommend furniture made from pet-friendly materials and designs. This allows for the provision of more appropriate interiors based on the user's family structure.

[0061] The AI ​​assistant advisory system for interior design can further enhance recommendations by taking into account the user's travel history. For example, if a user has visited a particular country or region, it can recommend interiors that incorporate the culture and design of that region. Similarly, if a user prefers beach resorts, it can recommend ocean-themed interiors. Furthermore, if a user prefers mountainous regions, it can recommend interiors using natural materials. This allows for the provision of more personalized interiors based on the user's travel history.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The input section allows the user to input their preferences and room characteristics. User preferences include, but are not limited to, color, style, and function. Room characteristics include, but are not limited to, size, shape, and purpose. The input section provides, for example, an interface for the user to input their preferences and room characteristics. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, data analysis methods and algorithms, but is not limited to such examples. For example, the analysis unit performs an analysis to select the optimal furniture and decorations based on the user's preferences and the characteristics of the room. Step 3: The recommendation unit recommends furniture or decorations based on the information analyzed by the analysis unit. Recommendations are made based on, for example, recommendation algorithms or evaluation criteria. The recommendation unit may also recommend optimal furniture or decorations based on, for example, the user's preferences or the characteristics of the room. Step 4: The AR unit places the furniture or decorations recommended by the recommendation unit using AR technology. AR technology includes, but is not limited to, the devices and software used. For example, the AR unit virtually places furniture and decorations in the user's room, allowing the user to view the interior.

[0064] (Example of form 2) The AI ​​assistant interior coordination advisory system according to an embodiment of the present invention is a system that recommends furniture and decorations that take into account the user's preferences, room size, and shape. In this system, the user inputs their preferences and room characteristics, and the analysis unit analyzes the information collected by the input unit. Based on the analyzed information, the recommendation unit recommends furniture or decorations, and the user can virtually place them in the actual room using AR technology, allowing them to view a virtual interior. Furthermore, it also includes a suggestion unit that provides coordination suggestions based on trends and design styles. For example, it can suggest the optimal coordination for the user based on the latest interior trends or specific design styles. The recommendation unit can also recommend furniture and decorations based on the user's lifestyle and areas of interest. For example, if the user has pets, it can recommend pet-friendly furniture. Additionally, the input unit has a function to estimate the user's emotions and determine the priority of input items based on the estimated emotions. For example, if the user is feeling stressed, it will prioritize recommending relaxing interiors. It also has a function to estimate preferences based on the user's social media activity and automatically input them. This allows the user to find an interior that suits their preferences without any hassle. Finally, the recommendation system also has a function to estimate the user's emotions and recommend furniture and decorations based on those emotions. For example, if the user is happy, it can recommend furniture in bright colors. In this way, it can provide interior coordination that matches the user's emotions. As a result, the AI ​​assistant interior coordination advisory system recommends furniture and decorations based on the user's preferences and the characteristics of the room, and allows the user to virtually place them using AR technology to check the interior.

[0065] The AI ​​assistant interior coordination advisory system according to this embodiment comprises an input unit, an analysis unit, a recommendation unit, and an AR unit. The input unit receives input from the user's preferences and the characteristics of the room. User preferences include, for example, color, style, and function, but are not limited to such examples. Room characteristics include, for example, size, shape, and use, but are not limited to such examples. The input unit provides, for example, an interface for the user to input preferences and room characteristics. The analysis unit analyzes the information input by the input unit. The analysis is performed, for example, using data analysis methods and algorithms, but is not limited to such examples. The analysis unit performs, for example, an analysis to select the optimal furniture and decorations based on the user's preferences and the characteristics of the room. The recommendation unit recommends furniture or decorations based on the information analyzed by the analysis unit. The recommendation is performed, for example, based on recommendation algorithms and evaluation criteria, but is not limited to such examples. The recommendation unit recommends the optimal furniture and decorations based, for example, the user's preferences and the characteristics of the room. The AR unit places the furniture or decorations recommended by the recommendation unit using AR technology. AR technology includes, but is not limited to, the devices and software used. For example, the AR unit virtually places furniture and decorations in the user's room, allowing the user to view the interior. Thus, the AI ​​assistant interior coordination advisory system according to the embodiment recommends furniture and decorations based on the user's preferences and the characteristics of the room, and virtually places them using AR technology, allowing the user to view the interior. Some or all of the above-described processes in the input unit, analysis unit, recommendation unit, and AR unit may be performed using AI, for example, or without AI. For example, the input unit provides an interface for inputting the user's preferences and the characteristics of the room, the analysis unit analyzes the information input by the input unit, the recommendation unit recommends furniture or decorations based on the information analyzed by the analysis unit, and the AR unit places the furniture or decorations recommended by the recommendation unit using AR technology.

[0066] The input section allows users to input their preferences and room characteristics. User preferences include, but are not limited to, color, style, and functionality. Specifically, users can select their preferred color palette and interior style (e.g., modern, classic, minimalist) through the interface. They can also input their requirements regarding furniture functionality (e.g., ample storage space, a comfortable sofa). Room characteristics include, but are not limited to, size, shape, and purpose. Specifically, users can input the room's area, ceiling height, window location and number, and room purpose (living room, bedroom, office, etc.). The input section provides, for example, an interface for users to input their preferences and room characteristics. This interface could be provided as a touchscreen, voice input, or even a smartphone app or web application. This allows users to intuitively input information, and the system can then proceed to the next step based on that information. Furthermore, the input section also has a function to save and reuse data previously entered by the user. This saves users the trouble of entering the same information repeatedly.

[0067] The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, data analysis methods and algorithms, but is not limited to these examples. Specifically, it performs analysis to select the optimal furniture and decorations based on the user's preferences and the characteristics of the room. For example, the analysis unit uses a clustering algorithm to classify the user's preferences into several categories and proposes the most suitable interior style for each category. It also performs analysis to optimize furniture placement and size based on the characteristics of the room. For example, if the room is small, it can recommend compact and multi-functional furniture. Furthermore, the analysis unit can use AI to learn from the user's input data and perform more accurate analysis. For example, it can predict the user's preferences based on past user selection history and feedback, and provide more appropriate suggestions. Based on these analysis results, the analysis unit provides information to the subsequent recommendation unit.

[0068] The recommendation unit recommends furniture or decorations based on information analyzed by the analysis unit. Recommendations are made based on, for example, recommendation algorithms and evaluation criteria, but are not limited to these examples. Specifically, it recommends optimal furniture and decorations based on the user's preferences and the characteristics of the room. For example, if the user prefers a modern style, the recommendation unit will suggest furniture and decorations with a modern design. It can also recommend furniture of appropriate size and placement depending on the size and shape of the room. The recommendation unit can use AI to learn the user's preferences and room characteristics, enabling more accurate recommendations. For example, it prioritizes recommending furniture and decorations that the user is likely to like based on their past selection history and feedback. The recommendation unit also has a function to provide feedback to the user on the recommended furniture and decorations. This allows the system to further learn the user's preferences and improve the accuracy of future recommendations.

[0069] The AR section uses augmented reality (AR) technology to place furniture or decorations recommended by the recommendation section. AR technology includes, but is not limited to, the devices and software used. Specifically, it virtually places furniture and decorations in the user's room, allowing the user to check the interior design. For example, virtual furniture and decorations can be overlaid onto the real room using the camera of a smartphone or tablet. This allows users to visually check how furniture will be placed in their room before actually purchasing it. The AR section also provides the ability for users to freely move and resize the virtually placed furniture and decorations. This allows users to find the optimal placement through trial and error. Furthermore, the AR section has a function to place multiple pieces of furniture and decorations simultaneously and check the overall balance. This allows users to check the coordination of the entire room at once. In addition, the AR section provides a function to save information on the virtual furniture and decorations placed by the user and review it later. This allows users to review placements they have tried and use this information to help them make a final decision.

[0070] The proposal unit can make suggestions based on trends and design styles. For example, the proposal unit can suggest the best coordination for the user based on the latest interior trends. For example, the proposal unit can suggest the best interior for the user, taking into account the latest designs and trendy colors. The proposal unit can also suggest the best coordination for the user based on a specific design style. For example, the proposal unit can suggest the best interior for the user based on design styles such as modern, classic, or minimalist. This makes it possible to make coordination suggestions based on trends and design styles. 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 suggest the best coordination for the user based on the latest interior trends and a specific design style.

[0071] The recommendation system can recommend furniture and decorations based on the user's lifestyle and areas of interest at the time of input. For example, if the user has pets, the recommendation system can recommend pet-friendly furniture. For example, the recommendation system can recommend furniture made from pet-friendly materials and designs. Also, if the user has children, the recommendation system can recommend child-safe furniture. For example, the recommendation system can recommend furniture made from child-safe materials and designs. Furthermore, if the user enjoys outdoor activities, the recommendation system can recommend furniture made from natural materials. For example, the recommendation system can recommend furniture made from natural materials and designs. This allows for the recommendation of more appropriate furniture and decorations based on the user's lifestyle and areas of interest. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system recommends the most suitable furniture and decorations based on the user's lifestyle and areas of interest.

[0072] The input unit can estimate the user's emotions and prioritize input items based on the estimated emotions. For example, if the user is stressed, the input unit will prioritize displaying input items related to relaxing interiors. For example, it will prioritize displaying input items related to interiors with relaxing colors and designs. Also, if the user is excited, the input unit can prioritize displaying input items related to colorful and lively interiors. For example, it will prioritize displaying input items related to colorful and lively interiors. Furthermore, if the user is tired, the input unit can prioritize displaying input items related to simple and calming interiors. For example, it will prioritize displaying input items related to simple and calming interiors. In this way, by prioritizing input items based on the user's emotions, a more appropriate interior can be recommended. Emotion estimation is achieved using an emotion estimation function, for example, 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 input unit may be performed using AI, for example, or without AI. For example, the input unit estimates the user's emotions and determines the priority of input items based on the estimated user emotions.

[0073] The input unit can analyze the user's past interior selection history and select an appropriate input method. For example, the input unit can automatically suggest relevant input items based on the interior style the user has previously selected. The input unit can also prioritize displaying input methods (voice, text, etc.) that the user has previously used. Furthermore, the input unit can predict and suggest interior styles to be used during specific time periods based on the user's past selection history. This allows the input unit to provide the optimal input method based on the user's past selection history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit analyzes the user's past interior selection history and selects an appropriate input method.

[0074] The input section can customize input items based on the user's current lifestyle and areas of interest during input. For example, if the user owns a pet, the input section will prioritize displaying input items related to pet-friendly interiors. For example, the input section will prioritize displaying input items related to interiors using pet-friendly materials and designs. Furthermore, if the user has children, the input section can prioritize displaying input items related to child-safe interiors. For example, the input section will prioritize displaying input items related to interiors using child-safe materials and designs. Additionally, if the user enjoys outdoor activities, the input section can prioritize displaying input items related to interiors using natural materials. For example, the input section will prioritize displaying input items related to interiors using natural materials and designs. This allows the input items to be customized based on the user's lifestyle and areas of interest. Some or all of the above processing in the input section may be performed using AI, for example, or without AI. For example, the input section customizes input items based on the user's current lifestyle and areas of interest.

[0075] The input unit can estimate the user's emotions and adjust the display order of input items based on the estimated emotions. For example, if the user is relaxed, the input unit can display detailed input options at the top. For example, if the user is relaxed, the input unit can display detailed input options at the top. For example, if the user is in a hurry, the input unit can display simple input options at the top. For example, if the user is in a hurry, the input unit can display simple input options at the top. Furthermore, if the user is having fun, the input unit can display customizable input options at the top. For example, if the user is having fun, the input unit can display customizable input options at the top. This allows for the recommendation of more appropriate interiors by adjusting the display order of input items based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 input unit may be performed using AI, for example, or without AI. For example, the input section estimates the user's emotions and adjusts the display order of the input items based on the estimated emotions.

[0076] The input section can prioritize displaying input items that are highly relevant based on the user's geographical location information during input. For example, if the user lives in a cold region, the input section will prioritize displaying input items related to interiors using warm materials. For example, the input section will prioritize displaying input items related to interiors using warm materials for users living in cold regions. The input section can also prioritize displaying input items related to compact interiors if the user lives in an urban area. For example, the input section will prioritize displaying input items related to compact interiors for users living in urban areas. Furthermore, if the user lives by the sea, the input section can prioritize displaying input items related to sea-themed interiors. For example, the input section will prioritize displaying input items related to sea-themed interiors for users living by the sea. This allows for the prioritization of highly relevant input items based on the user's geographical location information. Some or all of the above processing in the input section may be performed using AI, for example, or without AI. For example, the input section prioritizes displaying highly relevant input items based on the user's geographical location information.

[0077] The input unit can automatically suggest relevant input items based on the user's social media activity during input. For example, the input unit can suggest relevant input items based on the interior styles the user has "liked" on social media. For example, the input unit can suggest input items related to the interior styles the user has "liked". The input unit can also suggest relevant input items based on the styles of interior designers the user follows. For example, the input unit can suggest input items related to the styles of interior designers the user follows. Furthermore, the input unit can also suggest relevant input items based on interior images the user has shared. For example, the input unit can suggest input items related to interior images the user has shared. This allows the system to automatically suggest relevant input items based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit automatically suggests relevant input items based on the user's social media activity.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide multiple options. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide multiple options. If the user is in a hurry, the analysis unit can perform a concise analysis and provide only one optimal option. For example, if the user is in a hurry, the analysis unit can perform a concise analysis and provide only one optimal option. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. For example, if the analysis unit is excited, the analysis unit can provide visually appealing analysis results. This allows for more appropriate analysis results to be provided by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated user emotions.

[0079] The analysis unit can improve the accuracy of the analysis based on the user's past interior selection history. For example, the analysis unit can customize the analysis results based on the interior style the user has previously selected. The analysis unit can also analyze the user's preferences from their past selection history and reflect them in the analysis results. For example, the analysis unit can analyze the user's preferences from their past selection history and reflect them in the analysis results. Furthermore, the analysis unit can adjust the analysis results by taking into account the interior style the user has previously avoided. For example, the analysis unit can adjust the analysis results by taking into account the interior style the user has previously avoided. This allows the accuracy of the analysis to be improved based on the user's past selection history. 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 improve the accuracy of the analysis based on the user's past interior selection history.

[0080] The analysis unit can customize its analysis algorithm based on the user's lifestyle and areas of interest during analysis. For example, if the user owns a pet, the analysis unit will prioritize analyzing pet-friendly interiors. For example, the analysis unit will prioritize analyzing interiors made of pet-friendly materials and designs. Also, if the user has children, the analysis unit can prioritize analyzing interiors made of child-safe materials and designs. Furthermore, if the user enjoys outdoor activities, the analysis unit can prioritize analyzing interiors made with natural materials. For example, the analysis unit will prioritize analyzing interiors made with natural materials and designs. This allows the analysis algorithm to be customized based on the user's lifestyle and areas of interest. 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 customizes its analysis algorithm based on the user's lifestyle and areas of interest.

[0081] 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 nervous, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can provide a simple and highly visible display method for nervous users. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit can provide a display method that includes detailed information for relaxed users. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, the analysis unit can provide a display method that gets straight to the point for users in a hurry. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. 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 estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated user emotions.

[0082] The analysis unit can improve the accuracy of its analysis based on the user's geographical location information. For example, if the user lives in a cold region, the analysis unit will prioritize analyzing interiors made with warm materials. For example, the analysis unit will prioritize analyzing interiors made with warm materials for users living in cold regions. The analysis unit can also prioritize analyzing compact interiors for users living in urban areas. For example, the analysis unit will prioritize analyzing compact interiors for users living in urban areas. Furthermore, if the user lives by the sea, the analysis unit can prioritize analyzing interiors with a sea theme. For example, the analysis unit will prioritize analyzing interiors with a sea theme for users living by the sea. This allows the analysis accuracy to be improved based on the user's geographical location information. 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 improves the accuracy of its analysis based on the user's geographical location information.

[0083] The analysis unit can improve the accuracy of its analysis based on the user's social media activity. For example, the analysis unit can customize the analysis results based on the interior styles that the user has "liked" on social media. The analysis unit can also customize the analysis results based on the styles of interior designers that the user follows. Furthermore, the analysis unit can also customize the analysis results based on interior images that the user has shared. This allows the accuracy of the analysis to be improved based on the user's social media activity. 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 improves the accuracy of the analysis based on the user's social media activity.

[0084] The recommendation system can estimate the user's emotions and determine the priority of furniture and decorations to recommend based on the estimated emotions. For example, if the user is relaxed, the recommendation system will prioritize recommending furniture in calming colors. For example, if the user is relaxed, the recommendation system will prioritize recommending furniture in calming colors. For example, if the user is excited, the recommendation system will prioritize recommending colorful and lively furniture. For example, if the user is excited, the recommendation system will prioritize recommending colorful and lively furniture. Furthermore, if the user is tired, the recommendation system will prioritize recommending simple and calming furniture. For example, if the user is tired, the recommendation system will prioritize recommending simple and calming furniture. In this way, the priority of furniture and decorations to recommend can be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system estimates the user's emotions and determines the priority of furniture and decorations to recommend based on those estimated emotions.

[0085] The recommendation system can improve the accuracy of recommendations based on the user's past interior design selection history. For example, the recommendation system can recommend furniture related to the interior design style the user has previously selected. The recommendation system can also analyze the user's preferences from their past selection history and reflect this in the recommendation results. Furthermore, the recommendation system can adjust the recommendation results by considering the interior design style the user has previously avoided. This allows for improved recommendation accuracy based on the user's past selection history. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can improve the accuracy of recommendations based on the user's past interior design selection history.

[0086] The recommendation system can customize its recommendation algorithm based on the user's lifestyle and areas of interest. For example, if the user has pets, the recommendation system will prioritize recommending pet-friendly furniture. For example, the recommendation system will prioritize recommending furniture made from pet-friendly materials and designs. Similarly, if the user has children, the recommendation system can prioritize recommending furniture made from child-safe materials and designs. Furthermore, if the user enjoys outdoor activities, the recommendation system can prioritize recommending furniture made from natural materials. For example, the recommendation system will prioritize recommending furniture made from natural materials and designs. This allows the recommendation algorithm to be customized based on the user's lifestyle and areas of interest. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system customizes its recommendation algorithm based on the user's lifestyle and areas of interest.

[0087] The recommendation section can estimate the user's emotions and adjust how recommended furniture and decorations are displayed based on the estimated emotions. For example, if the user is relaxed, the recommendation section can provide a display method that includes detailed information. For example, if the user is relaxed, the recommendation section can provide a display method that includes detailed information. For example, if the user is in a hurry, the recommendation section can provide a display method that includes concise information. For example, if the user is in a hurry, the recommendation section can provide a display method that includes concise information. Furthermore, if the user is having fun, the recommendation section can provide a customizable display method. For example, if the user is having fun, the recommendation section can provide a customizable display method. This allows the display method of recommended furniture and decorations to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 recommendation section may be performed using AI, for example, or without AI. For example, the recommendation system estimates the user's emotions and adjusts how recommended furniture and decorations are displayed based on those estimated emotions.

[0088] The recommendation system can improve the accuracy of recommendations based on the user's geographical location. For example, if a user lives in a cold climate, the recommendation system will prioritize recommending furniture made from warm materials. The recommendation system can also prioritize recommending compact furniture if the user lives in an urban area. Furthermore, if a user lives by the sea, the recommendation system can prioritize recommending ocean-themed furniture. This allows for improved recommendation accuracy based on the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system improves the accuracy of recommendations based on the user's geographical location.

[0089] The recommendation system can improve the accuracy of recommendations based on the user's social media activity. For example, the recommendation system can recommend furniture related to interior styles that the user has "liked" on social media. The recommendation system can also recommend furniture related to the styles of interior designers that the user follows. Furthermore, the recommendation system can recommend furniture related to interior images that the user has shared. This allows for improved recommendation accuracy based on the user's social media activity. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can improve the accuracy of recommendations based on the user's social media activity.

[0090] The AR unit can estimate the user's emotions and adjust the AR display method based on the estimated user emotions. For example, if the user is relaxed, the AR unit can provide an AR display that progresses at a leisurely pace. For example, the AR unit can provide an AR display that progresses at a leisurely pace for a relaxed user. The AR unit can also provide a concise and rapid AR display if the user is in a hurry. For example, the AR unit can provide a concise and rapid AR display for a user in a hurry. Furthermore, if the user is excited, the AR unit can provide an AR display with visually stimulating effects. For example, the AR unit can provide an AR display with visually stimulating effects for an excited user. This allows the AR display method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 AR unit may be performed using AI, for example, or without AI. For example, the AR unit estimates the user's emotions and adjusts the AR display method based on the estimated user emotions.

[0091] The AR unit can improve the accuracy of its AR display based on the user's past interior selection history. For example, the AR unit provides relevant AR displays based on the interior styles the user has previously selected. The AR unit can also analyze the user's preferences from their past selection history and reflect them in the AR display. For example, the AR unit analyzes the user's preferences from their past selection history and reflects them in the AR display. Furthermore, the AR unit can adjust the AR display considering interior styles the user has previously avoided. For example, the AR unit adjusts the AR display considering interior styles the user has previously avoided. This improves the accuracy of the AR display based on the user's past selection history. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit improves the accuracy of the display based on the user's past interior selection history.

[0092] The AR unit can customize the display algorithm based on the user's lifestyle and areas of interest when displaying AR content. For example, if the user has pets, the AR unit can prioritize displaying pet-friendly interiors in AR. For example, the AR unit can prioritize displaying interiors made of pet-friendly materials and designs in AR. Also, if the user has children, the AR unit can prioritize displaying child-safe interiors in AR. For example, the AR unit can prioritize displaying interiors made of child-safe materials and designs in AR. Furthermore, if the user enjoys outdoor activities, the AR unit can prioritize displaying interiors made with natural materials in AR. For example, the AR unit can prioritize displaying interiors made with natural materials and designs in AR. This allows the AR display algorithm to be customized based on the user's lifestyle and areas of interest. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit customizes the display algorithm based on the user's lifestyle and areas of interest.

[0093] The AR unit can estimate the user's emotions and adjust the order of AR displays based on the estimated emotions. For example, if the user is relaxed, the AR unit can display AR displays containing detailed information at the top. For example, if the user is relaxed, the AR unit can display AR displays containing detailed information at the top. For example, if the user is in a hurry, the AR unit can display AR displays containing concise information at the top. For example, if the user is in a hurry, the AR unit can display AR displays containing concise information at the top. Furthermore, if the user is having fun, the AR unit can display customizable AR displays at the top. For example, if the user is having fun, the AR unit can display customizable AR displays at the top. In this way, the order of AR displays can be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit estimates the user's emotions and adjusts the order of AR displays based on the estimated emotions.

[0094] The AR unit can improve the accuracy of the display based on the user's geographical location information when displaying AR content. For example, if the user lives in a cold region, the AR unit can prioritize displaying interiors made with warm materials. For example, the AR unit can prioritize displaying interiors made with warm materials for users living in cold regions. The AR unit can also prioritize displaying compact interiors for users living in urban areas. For example, the AR unit can prioritize displaying compact interiors for users living in urban areas. Furthermore, if the user lives by the sea, the AR unit can prioritize displaying interiors with a sea theme. For example, the AR unit can prioritize displaying interiors with a sea theme for users living by the sea. This improves the accuracy of the AR display based on the user's geographical location information. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit improves the accuracy of the display based on the user's geographical location information.

[0095] The AR unit can improve the accuracy of AR displays based on the user's social media activity. For example, the AR unit can provide relevant AR displays based on interior styles that the user has "liked" on social media. For example, the AR unit can provide AR displays related to interior styles that the user has "liked". The AR unit can also provide relevant AR displays based on the styles of interior designers that the user follows. For example, the AR unit can provide AR displays related to the styles of interior designers that the user follows. Furthermore, the AR unit can provide relevant AR displays based on interior images that the user has shared. For example, the AR unit can provide AR displays related to interior images that the user has shared. This allows for improved accuracy of AR displays based on the user's social media activity. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit improves the accuracy of displays based on the user's social media activity.

[0096] 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. For example, the suggestion unit can provide detailed suggestions to a relaxed user. The suggestion unit can also provide concise suggestions to a user in a hurry. For example, the suggestion unit can provide concise suggestions to a user in a hurry. Furthermore, the suggestion unit can provide customizable suggestions to a user who is enjoying themselves. For example, the suggestion unit can provide customizable suggestions to a user who is enjoying themselves. This allows the way it presents its suggestions to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. For example, the suggestion unit estimates the user's emotions and adjusts the way it presents its suggestions based on those estimated emotions.

[0097] The proposal function can adjust the level of detail of its proposals based on trends and design styles. For example, the proposal function can provide detailed proposals based on the latest interior trends. For example, the proposal function can provide detailed proposals based on the latest designs and trendy colors. The proposal function can also provide detailed proposals based on specific design styles. For example, the proposal function can provide detailed proposals based on design styles such as modern, classic, and minimalist. Furthermore, the proposal function can also provide detailed proposals based on user preferences. For example, the proposal function can provide detailed proposals based on user preferences. This allows for adjustment of the level of detail of proposals based on trends and design styles. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function adjusts the level of detail of proposals based on trends and design styles.

[0098] The suggestion unit can customize its suggestion algorithm based on the user's lifestyle and areas of interest when making suggestions. For example, if the user has pets, the suggestion unit will prioritize suggesting pet-friendly interiors. For example, the suggestion unit will prioritize suggesting interiors made with pet-friendly materials and designs. Also, if the user has children, the suggestion unit can prioritize suggesting child-safe interiors. For example, the suggestion unit will prioritize suggesting interiors made with child-safe materials and designs. Furthermore, if the user enjoys outdoor activities, the suggestion unit can prioritize suggesting interiors made with natural materials. For example, the suggestion unit will prioritize suggesting interiors made with natural materials and designs. This allows the suggestion algorithm to be customized based on the user's lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit customizes its suggestion algorithm based on the user's lifestyle and areas of interest.

[0099] The suggestion section can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion section can provide detailed suggestions. For example, the suggestion section can provide detailed suggestions to a relaxed user. The suggestion section can also provide concise suggestions to a user in a hurry. For example, the suggestion section can provide concise suggestions to a user in a hurry. Furthermore, the suggestion section can provide customizable suggestions to a user who is enjoying themselves. For example, the suggestion section can provide customizable suggestions to a user who is enjoying themselves. This allows the length of the suggestions to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 section may be performed using AI or not using AI. For example, the suggestion section estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions.

[0100] The proposal department can determine the priority of proposals based on trends and changes in design style when making proposals. For example, the proposal department may prioritize proposals based on the latest interior trends. For example, the proposal department may prioritize proposals based on the latest designs and trendy colors. The proposal department can also prioritize proposals based on specific design styles. For example, the proposal department may prioritize proposals based on design styles such as modern, classic, or minimalist. Furthermore, the proposal department may also prioritize proposals based on user preferences. For example, the proposal department may prioritize proposals based on user preferences. This allows the proposal department to determine the priority of proposals based on trends and changes in design style. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department determines the priority of proposals based on trends and changes in design style.

[0101] The suggestion function can improve the accuracy of its suggestions based on the user's social media activity. For example, the suggestion function can provide relevant suggestions based on the interior styles that the user has "liked" on social media. For example, the suggestion function can provide relevant suggestions based on the interior styles that the user has "liked". For example, the suggestion function can provide relevant suggestions based on the interior styles that the user follows. For example, the suggestion function can provide relevant suggestions based on the interior styles that the user follows. Furthermore, the suggestion function can provide relevant suggestions based on interior images that the user has shared. For example, the suggestion function can provide relevant suggestions based on the user's social media activity. This allows the accuracy of suggestions to be improved based on the user's social media activity. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can improve the accuracy of its suggestions based on the user's social media activity.

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

[0103] The AI ​​assistant advisory system for interior design can also provide recommendations that take the user's health condition into account. For example, if a user has allergies, it can recommend furniture and decorations made from allergen-free materials. If a user suffers from back pain, it can recommend chairs and beds with back-friendly designs. Furthermore, if a user has a visual impairment, it can recommend furniture with colors and designs that are easily recognizable. This allows for the provision of more appropriate interior design based on the user's health condition.

[0104] The AI ​​assistant advisory system for interior design can further enhance recommendations by taking into account the user's environmental awareness. For example, if a user is interested in environmental protection, it can recommend furniture and decorations made from renewable materials. It can also recommend energy-efficient lighting and appliances. Furthermore, if a user supports local products, it can recommend furniture and decorations made by local artisans. This allows for the provision of more sustainable interiors based on the user's environmental consciousness.

[0105] The AI ​​assistant advisory system for interior design can further enhance recommendations by taking into account the user's hobbies and skills. For example, if the user enjoys music, it can recommend room layouts with good acoustics and furniture for storing musical instruments. If the user enjoys painting, it can recommend optimal locations and lighting for displaying artwork. Furthermore, if the user enjoys reading, it can recommend lighting and bookshelf placement suitable for reading. This allows for the provision of more personalized interiors based on the user's hobbies and skills.

[0106] The AI ​​assistant advisory system for interior design can further enhance recommendations by taking into account the user's family structure. For example, if the user lives with elderly people, it can recommend elderly-friendly furniture and decorations. If the user has many children, it can recommend furniture arrangements that ensure safe play space for children. Furthermore, if the user has pets, it can recommend furniture made from pet-friendly materials and designs. This allows for the provision of more appropriate interiors based on the user's family structure.

[0107] The AI ​​assistant advisory system for interior design can further enhance recommendations by taking into account the user's travel history. For example, if a user has visited a particular country or region, it can recommend interiors that incorporate the culture and design of that region. Similarly, if a user prefers beach resorts, it can recommend ocean-themed interiors. Furthermore, if a user prefers mountainous regions, it can recommend interiors using natural materials. This allows for the provision of more personalized interiors based on the user's travel history.

[0108] An AI assistant advisory system for interior design can estimate the user's emotions and adjust the interior color scheme based on those emotions. For example, if the user is relaxed, it can recommend interiors with calming colors. If the user is excited, it can recommend interiors with bright colors. Furthermore, if the user is sad, it can recommend interiors with warm colors. This allows the system to provide more appropriate interiors based on the user's emotions.

[0109] An AI assistant advisory system for interior design can estimate the user's emotions and adjust the interior layout based on those emotions. For example, if the user is relaxed, it can recommend a spacious layout. If the user is excited, it can recommend a lively layout. Furthermore, if the user is tired, it can recommend a simple and calming layout. This allows the system to provide a more appropriate interior based on the user's emotions.

[0110] An AI assistant advisory system for interior design can estimate the user's emotions and adjust the interior materials based on those emotions. For example, if the user is relaxed, it can recommend furniture made of soft materials. If the user is excited, it can recommend furniture made of colorful materials. Furthermore, if the user is sad, it can recommend furniture made of warm materials. This allows the system to provide a more appropriate interior based on the user's emotions.

[0111] An AI assistant advisory system for interior design can estimate the user's emotions and adjust the interior lighting based on those emotions. For example, if the user is relaxed, it can recommend soft lighting. If the user is excited, it can recommend bright lighting. Furthermore, if the user is tired, it can recommend warm lighting. This allows the system to provide a more appropriate interior design based on the user's emotions.

[0112] An AI assistant advisory system for interior design can estimate the user's emotions and adjust interior decor based on those emotions. For example, if the user is relaxed, it can recommend nature-themed decor. If the user is excited, it can recommend colorful and vibrant decor. Furthermore, if the user is sad, it can recommend warm and comforting decor. This allows the system to provide more appropriate interior design based on the user's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The input section allows the user to input their preferences and room characteristics. User preferences include, but are not limited to, color, style, and function. Room characteristics include, but are not limited to, size, shape, and purpose. The input section provides, for example, an interface for the user to input their preferences and room characteristics. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, data analysis methods and algorithms, but is not limited to such examples. For example, the analysis unit performs an analysis to select the optimal furniture and decorations based on the user's preferences and the characteristics of the room. Step 3: The recommendation unit recommends furniture or decorations based on the information analyzed by the analysis unit. Recommendations are made based on, for example, recommendation algorithms or evaluation criteria. The recommendation unit may also recommend optimal furniture or decorations based on, for example, the user's preferences or the characteristics of the room. Step 4: The AR unit places the furniture or decorations recommended by the recommendation unit using AR technology. AR technology includes, but is not limited to, the devices and software used. For example, the AR unit virtually places furniture and decorations in the user's room, allowing the user to view the interior.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0116] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] 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.

[0118] For example, the input unit can input user preferences and room characteristics using the reception device 38 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends furniture and decorations based on the analyzed information. The AR unit virtually places the recommended furniture and decorations using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0133] 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.

[0134] For example, the input unit can input the user's preferences and room characteristics using the microphone 238 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is implemented by the identification processing unit 290 of the data processing device 12 and recommends furniture and decorations based on the analyzed information. The AR unit virtually places the recommended furniture and decorations using the display of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the example described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0149] 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.

[0150] For example, the input unit can input user preferences and room characteristics using the microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends furniture and decorations based on the analyzed information. The AR unit virtually places the recommended furniture and decorations using the display 343 of the headset terminal 314. The correspondence between each unit and the device and control unit is not limited to the example described above and can be changed in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0166] 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.

[0167] For example, the input unit can input user preferences and room characteristics using the microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends furniture and decorations based on the analyzed information. The AR unit virtually places the recommended furniture and decorations using the display of the robot 414. The correspondence between each unit and the device and control unit is not limited to the example described above and can be changed in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) An input section for entering the user's preferences and room characteristics, An analysis unit analyzes the information input by the input unit, Based on the information analyzed by the aforementioned analysis unit, a recommendation unit recommends furniture and decorations, The system includes an AR unit that uses AR technology to arrange furniture and decorations recommended by the aforementioned recommendation unit. A system characterized by the following features. (Note 2) The department includes a proposal division that makes suggestions based on trends and design styles. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recommendation department, Based on the user's lifestyle and areas of interest at the time of input, the system recommends furniture and decorations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned input unit is It estimates the user's emotions and prioritizes input fields based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned input unit is Analyze the user's past interior design selection history and select the appropriate input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned input unit is When users enter data, the input fields are customized based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is It estimates the user's emotions and adjusts the display order of input fields based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is When users enter data, the system prioritizes displaying input fields that are more relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is When users enter data, the system automatically suggests relevant input fields based on their social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Estimate the user's emotions, and adjust the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's past interior selection history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the analysis algorithm is customized based on the user's lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, It estimates the user's emotions and determines the priority of recommended furniture and decorations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, When making recommendations, we improve the accuracy of recommendations based on the user's past interior design selection history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When making recommendations, the recommendation algorithm is customized based on the user's lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, Estimate the user's emotions and adjust how furniture and decorations are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, When making recommendations, we improve the accuracy of recommendations based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, we improve the accuracy of recommendations based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned AR section is, It estimates the user's emotions and adjusts the AR display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned AR section is, When displaying AR content, the accuracy of the display is improved based on the user's past interior selection history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned AR section is, When displaying AR content, the display algorithm is customized based on the user's lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned AR section is, It estimates the user's emotions and adjusts the order of AR displays based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned AR section is, When displaying AR, the accuracy of the display is improved based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned AR section is, When displaying AR, the accuracy of the display is improved based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on trends and design styles. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making suggestions, the suggestion algorithm is customized based on the user's lifestyle and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making proposals, prioritize them based on trends and changes in design style. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making suggestions, we improve the accuracy of those suggestions based on the user's social media activity. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0187] 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 input section for entering the user's preferences and room characteristics, An analysis unit analyzes the information input by the input unit, Based on the information analyzed by the aforementioned analysis unit, a recommendation unit recommends furniture and decorations, The system includes an AR section that uses AR technology to arrange furniture and decorations recommended by the aforementioned recommendation section. A system characterized by the following features.

2. The department includes a proposal division that makes suggestions based on trends and design styles. The system according to feature 1.

3. The aforementioned recommendation department, Based on the user's lifestyle and areas of interest at the time of input, the system recommends furniture and decorations. The system according to feature 1.

4. The aforementioned input unit is It estimates the user's emotions and prioritizes input fields based on the estimated user emotions. The system according to feature 1.

5. The aforementioned input unit is Analyze the user's past interior design selection history and select the appropriate input method. The system according to feature 1.

6. The aforementioned input unit is When users enter data, the input fields are customized based on their current lifestyle and areas of interest. The system according to feature 1.

7. The aforementioned input unit is It estimates the user's emotions and adjusts the display order of input fields based on the estimated user emotions. The system according to feature 1.

8. The aforementioned input unit is When users enter data, the system prioritizes displaying input fields that are more relevant to their geographical location. The system according to feature 1.

9. The aforementioned input unit is When users enter data, the system automatically suggests relevant input fields based on their social media activity. The system according to feature 1.

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