System

Abstract

An object of a system according to an embodiment is to optimize the arrangement and interior design of a room based on the Feng Shui principle.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information on a drawing and arrangement of a room from a user. The analysis unit analyzes the information collected by the collection unit. The providing unit provides advice based on an analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately optimize room layout and interior design based on Feng Shui principles, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the layout and interior design of a room based on the principles of Feng Shui. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information on room drawings and layouts from a user. The analysis unit analyzes the information collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the layout and interior design of a room based on the principles of Feng Shui. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention optimizes room layout and interior design based on the principles of Feng Shui, providing individual users with a harmonious space. The system analyzes the room's layout and layout provided by the user and provides optimal advice based on the user's lifestyle and preferences. This allows residents to create a comfortable living environment and bring healing and relaxation to their daily lives. For example, the user provides the system with a room's layout and layout, such as the living room layout and furniture arrangement. This information is analyzed in detail by the system. The system then optimizes the room's layout and interior design based on the principles of Feng Shui. For example, by rearranging the furniture or using specific colors and materials, the flow of energy in the room can be improved, thereby creating a comfortable living environment for the user. Furthermore, the system provides personalized advice based on the user's lifestyle and preferences. For example, if a user is looking for a relaxing space, the system suggests optimal layout and design based on the principles of Feng Shui. This allows the user to create healing and relaxation in their daily lives. The system can thereby balance the user's mind and body through personalized consideration of interior design and lifestyle that incorporates the principles of Feng Shui. For example, a specific layout and design can reduce stress and enhance relaxation. Additionally, placement and design based on Feng Shui principles can be expected to improve users' health and well-being.

[0029] An interior design optimization system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information on room plans and layouts from a user. For example, the collection unit may collect information such as the layout of a living room and furniture arrangements provided by the user. The collection unit may also collect information using various means, such as voice, text, and images. For example, the collection unit may collect photos of a room taken by the user with a smartphone. The collection unit may also scan the room plans provided by the user and collect them as digital data. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit may optimize the room layout and interior design based on the principles of feng shui. The analysis unit may improve the flow of energy in a room by, for example, rearranging furniture and using specific colors and materials. The analysis unit may also perform analysis to provide personalized advice based on the user's lifestyle and preferences. For example, if the user is looking for a relaxing space, the analysis unit may suggest an optimal layout and design based on the principles of feng shui. The provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the provision unit may provide advice to the user online. The providing unit, for example, proposes specific layouts and designs to create a space where the user can relax. The providing unit can also provide advice to reduce the user's stress and enhance the relaxation effect. In this way, the interior design optimization system according to the embodiment can optimize the layout and interior design of the user's room and provide a harmonious space.

[0030] The analysis unit can adjust the room layout and interior design based on the principles of Feng Shui. The principles of Feng Shui include, for example, basic rules for improving the flow of energy in a room. For example, the analysis unit can improve the energy flow in a room by changing the furniture layout and using specific colors and materials. For example, the analysis unit can smooth the flow of energy by arranging furniture along the walls and reserving space in the center of the room. The analysis unit can also improve the atmosphere of the room by using specific colors and materials. For example, the analysis unit can use soft colors and natural materials to enhance the relaxing effect. Furthermore, the analysis unit can provide specific advice for optimizing the room layout and interior design based on the principles of Feng Shui. For example, the analysis unit can suggest the optimal layout and design based on the user's lifestyle and preferences. As a result, a harmonious space can be provided by optimizing the room layout and interior design based on the principles of Feng Shui.

[0031] The providing unit can provide advice based on the user's lifestyle and preferences. For example, the providing unit can suggest an optimal interior design based on the user's lifestyle and preferences. For example, if the user is looking for a relaxing space, the providing unit can suggest an optimal layout and design based on the principles of feng shui. The providing unit can also suggest using specific colors and materials according to the user's preferences. For example, the providing unit can improve the atmosphere of the room by using the colors and materials that the user prefers. Furthermore, the providing unit can provide specific advice tailored to the user's lifestyle. For example, if the user is looking for an environment where they can concentrate on work or study, the providing unit can suggest a layout and design that will increase concentration based on the principles of feng shui. This makes it possible to achieve a more appropriate interior design by providing individual advice based on the user's lifestyle and preferences.

[0032] The providing unit may provide advice online. For example, the providing unit may provide advice to a user through an online platform. For example, the providing unit may provide advice to a user in real time through a video call or chat. The providing unit may also receive room drawings and layout information provided by a user online and provide advice based on the received information. For example, the providing unit may analyze photos and drawings of a room uploaded by a user and suggest optimal layouts and designs. Furthermore, the providing unit may record the advice provided online so that the user can refer to it later. For example, the providing unit may save the advice provided to the user in text or video format so that the user can access it at any time. In this way, by providing advice online, the user can receive advice from anywhere.

[0033] The analysis unit can improve the energy flow of a room by changing the furniture arrangement and using colors and materials. For example, the analysis unit can improve the energy flow of a room by changing the furniture arrangement. For example, the analysis unit can smooth the energy flow by arranging furniture along the walls and reserving space in the center of the room. The analysis unit can also improve the atmosphere of a room by using specific colors and materials. For example, the analysis unit can use soft colors and natural materials to enhance the relaxing effect. Furthermore, the analysis unit can provide specific advice for optimizing the room's arrangement and interior design based on the principles of feng shui. For example, the analysis unit can suggest optimal arrangement and design based on the user's lifestyle and preferences. This can improve the energy flow of a room and provide a harmonious space by adjusting the furniture arrangement and using specific colors and materials.

[0034] The providing unit can provide advice on which layout and design will reduce the user's stress and enhance the relaxation effect. For example, the providing unit can provide advice on which specific layout and design will reduce the user's stress and enhance the relaxation effect. For example, if the user is looking for a relaxing space, the providing unit can suggest an optimal layout and design based on the principles of feng shui. The providing unit can also suggest using specific colors and materials according to the user's preferences. For example, the providing unit can improve the atmosphere of the room by using the user's preferred colors and materials. Furthermore, the providing unit can provide specific advice tailored to the user's lifestyle. For example, if the user is looking for an environment where they can concentrate on work or study, the providing unit can suggest a layout and design that will enhance concentration based on the principles of feng shui. This can provide a comfortable space by reducing the user's stress and enhancing the relaxation effect with a specific layout and design.

[0035] The collection unit can analyze the user's past room layout information and select a collection method. The collection unit can, for example, select the most efficient collection method based on the room layout information provided by the user in the past. For example, the collection unit can extract a specific pattern from the user's past layout information and customize the collection method based on that pattern. The collection unit can also analyze the user's past layout information and provide feedback to optimize the collection method. For example, the collection unit selects the most efficient collection method based on the room layout information provided by the user in the past. The collection unit can extract a specific pattern from the user's past layout information and customize the collection method based on that pattern. The collection unit can also analyze the user's past layout information and provide feedback to optimize the collection method. In this way, information can be efficiently collected by selecting the optimal collection method based on the past layout information.

[0036] When collecting information about a room's layout or layout, the collection unit can select information based on the user's current living situation and areas of interest. The collection unit can, for example, collect only necessary information according to the user's current living situation. For example, the collection unit collects only necessary information according to the user's current living situation. The collection unit can preferentially collect related information based on the user's areas of interest. For example, the collection unit preferentially collects related information based on the user's areas of interest. The collection unit can also customize the information to be collected taking into account the user's living situation and areas of interest. For example, the collection unit customizes the information to be collected taking into account the user's living situation and areas of interest. This enables efficient information collection by collecting only necessary information according to the user's living situation and areas of interest.

[0037] When collecting information about a room's layout and layout, the collection unit can select a collection means according to the user's input method. For example, if the user prefers voice input, the collection unit can collect information about the room's layout and layout by voice. For example, if the user prefers voice input, the collection unit collects information about the room's layout and layout by voice. For example, if the user prefers text input, the collection unit can collect information about the room's layout and layout by text. For example, if the user prefers text input, the collection unit can collect information about the room's layout and layout by text. For example, if the user prefers image input, the collection unit can also collect information about the room's layout and layout by images. For example, if the user prefers image input, the collection unit collects information about the room's layout and layout by images. In this way, by selecting the optimal collection means according to the user's input method, information can be provided in a manner that is most user-friendly for the user.

[0038] When collecting room diagrams and layout information, the collection unit can prioritize collecting information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting layout information specific to a region based on the user's geographical location information. For example, the collection unit prioritizes collecting layout information specific to a region based on the user's geographical location information. The collection unit can collect information based on feng shui principles of a region by taking into account the user's geographical location information. For example, the collection unit collects information based on feng shui principles of a region by taking into account the user's geographical location information. The collection unit can also collect layout information suitable for the climate and environment of a region by referring to the user's geographical location information. For example, the collection unit collects layout information suitable for the climate and environment of a region by referring to the user's geographical location information. In this way, it is possible to collect information specialized for a region by taking into account the user's geographical location information.

[0039] The collection unit can analyze the user's social media activities and collect information when collecting room drawings and layout information. The collection unit can, for example, analyze the content of the user's posts on social media and collect related layout information. For example, the collection unit analyzes the content of the user's posts on social media and collects related layout information. The collection unit can collect related layout information by referring to the activities of the user's friends on social media. For example, the collection unit collects related layout information by referring to the activities of the user's friends on social media. The collection unit can also collect related layout information based on the user's check-in information on social media. For example, the collection unit collects related layout information based on the user's check-in information on social media. In this way, related information can be efficiently collected by analyzing the user's social media activities.

[0040] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting information about room drawings and layouts. The collection unit can, for example, improve the collection method based on the user's past feedback. For example, the collection unit improves the collection method based on the user's past feedback. The collection unit can adjust the range of information to be collected by reflecting the user's past feedback. For example, the collection unit adjusts the range of information to be collected by reflecting the user's past feedback. The collection unit can also customize the collection method by referring to the user's past feedback. For example, the collection unit customizes the collection method by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the scope of the analysis based on the importance of the room. The analysis unit can, for example, perform a detailed analysis on an important room such as a living room. For example, the analysis unit performs a detailed analysis on an important room such as a living room. The analysis unit can perform a brief analysis on a less important room such as a closet. For example, the analysis unit performs a brief analysis on a less important room such as a closet. The analysis unit can also adjust the level of detail of the analysis according to the importance of the room. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the room. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the room.

[0042] The analysis unit can apply an analysis algorithm according to the room category during analysis. For example, the analysis unit can apply an analysis algorithm for enhancing a relaxing effect to a living room. For example, the analysis unit applies an analysis algorithm for enhancing a relaxing effect to a living room. The analysis unit can apply an analysis algorithm for ensuring an efficient traffic flow to a kitchen. For example, the analysis unit applies an analysis algorithm for ensuring an efficient traffic flow to a kitchen. The analysis unit can also apply an analysis algorithm for providing a comfortable sleeping environment to a bedroom. For example, the analysis unit applies an analysis algorithm for providing a comfortable sleeping environment to a bedroom. In this way, by applying different analysis algorithms according to the room category, more appropriate analysis results can be provided.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, improve the analysis algorithm based on the user's past analysis results. For example, the analysis unit improves the analysis algorithm based on the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also optimize the analysis method by reflecting the user's past analysis results. For example, the analysis unit optimizes the analysis method by reflecting the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the order of analysis based on the time when the rooms were arranged. The analysis unit can, for example, prioritize analyzing rooms that were recently arranged. For example, the analysis unit prioritizes analyzing rooms that were recently arranged. The analysis unit can also prioritize analyzing rooms that have not been changed for a long period of time. For example, the analysis unit prioritizes analyzing rooms that have not been changed for a long period of time. The analysis unit can also determine the priority of analysis based on the time when the rooms were arranged. For example, the analysis unit determines the priority of analysis based on the time when the rooms were arranged. This enables efficient analysis by determining the priority of analysis based on the time when the rooms were arranged.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the rooms. For example, the analysis unit can consecutively analyze rooms with high relevance, such as the living room and the dining room. For example, the analysis unit consecutively analyzes rooms with high relevance, such as the living room and the dining room. The analysis unit can also separately analyze rooms with low relevance, such as a closet and a bedroom. For example, the analysis unit separately analyzes rooms with low relevance, such as a closet and a bedroom. The analysis unit can also adjust the order of analysis based on the relevance of the rooms. For example, the analysis unit adjusts the order of analysis based on the relevance of the rooms. This enables efficient analysis by adjusting the order of analysis based on the relevance of the rooms.

[0046] During analysis, the analysis unit can change the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. If the user does not have technical expertise, the analysis unit can use concise and easy-to-understand terminology. For example, if the user does not have technical expertise, the analysis unit can use concise and easy-to-understand terminology. The analysis unit can also adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0047] When providing advice, the providing unit can adjust the scope of the advice based on the importance of the room. The providing unit can provide detailed advice for an important room such as a living room, for example. For example, the providing unit provides detailed advice for an important room such as a living room. The providing unit can provide concise advice for a room of low importance such as a closet. For example, the providing unit provides concise advice for a room of low importance such as a closet. The providing unit can also adjust the level of detail of the advice according to the importance of the room. For example, the providing unit adjusts the level of detail of the advice according to the importance of the room. This enables efficient advice by adjusting the level of detail of the advice according to the importance of the room.

[0048] The providing unit can apply an advice algorithm according to the room category when providing advice. For example, the providing unit can apply an advice algorithm for enhancing a relaxing effect to a living room. For example, the providing unit applies an advice algorithm for enhancing a relaxing effect to a living room. The providing unit can apply an advice algorithm for ensuring an efficient traffic line to a kitchen. For example, the providing unit applies an advice algorithm for ensuring an efficient traffic line to a kitchen. The providing unit can also apply an advice algorithm for providing a comfortable sleeping environment to a bedroom. For example, the providing unit applies an advice algorithm for providing a comfortable sleeping environment to a bedroom. In this way, by applying different advice algorithms according to the room category, more appropriate advice can be provided.

[0049] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit can, for example, improve the advice algorithm based on the user's past advice results. For example, the providing unit improves the advice algorithm based on the user's past advice results. The providing unit can improve the accuracy of the advice by referring to the user's past advice results. For example, the providing unit improves the accuracy of the advice by referring to the user's past advice results. The providing unit can also optimize the advice method by reflecting the user's past advice results. For example, the providing unit optimizes the advice method by reflecting the user's past advice results. In this way, the accuracy of the advice can be improved by referring to the user's past advice results.

[0050] When providing advice, the providing unit can determine the order of advice based on the time when the rooms were arranged. The providing unit can, for example, give priority to advice about rooms that were recently arranged. For example, the providing unit gives priority to advice about rooms that were recently arranged. The providing unit can also give priority to advice about rooms that have not been changed for a long period of time. For example, the providing unit gives priority to advice about rooms that have not been changed for a long period of time. The providing unit can also determine the priority of advice based on the time when the rooms were arranged. For example, the providing unit determines the priority of advice based on the time when the rooms were arranged. This enables efficient advice by determining the priority of advice based on the time when the rooms were arranged.

[0051] The providing unit can adjust the order of advice based on the relevance of the rooms when providing advice. The providing unit can, for example, consecutively provide advice for rooms with high relevance, such as the living room and the dining room. For example, the providing unit consecutively provides advice for rooms with high relevance, such as the living room and the dining room. The providing unit can also separately provide advice for rooms with low relevance, such as the closet and the bedroom. For example, the providing unit separately provides advice for rooms with low relevance, such as the closet and the bedroom. The providing unit can also adjust the order of advice based on the relevance of the rooms. For example, the providing unit adjusts the order of advice based on the relevance of the rooms. This enables efficient advice by adjusting the order of advice based on the relevance of the rooms.

[0052] When providing advice, the providing unit can change the use of technical terminology in the advice depending on the user's level of expertise. For example, if the user has technical expertise, the providing unit can use detailed technical terminology. For example, if the user has technical expertise, the providing unit uses detailed technical terminology. If the user does not have technical expertise, the providing unit can use concise and easy-to-understand terminology. For example, if the user does not have technical expertise, the providing unit uses concise and easy-to-understand terminology. The providing unit can also adjust the use of technical terminology in the advice depending on the user's level of expertise. For example, the providing unit adjusts the use of technical terminology in the advice depending on the user's level of expertise. In this way, by adjusting the use of technical terminology depending on the user's level of expertise, it is possible to provide advice that is easier to understand.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can analyze the user's past interior design preferences and customize the type of information to be collected. For example, the collection unit can analyze the patterns of colors and materials selected by the user in the past and make new suggestions based on that. The collection unit can also optimize the information to be collected by referring to the layouts and arrangements that the user has preferred in the past. Furthermore, the collection unit can determine the priority of the information to be collected based on the user's past preferences. This makes it possible to collect information that reflects the user's past preferences.

[0055] The providing unit can provide advice taking into consideration the user's current health condition. For example, if the user is tired, the providing unit can suggest an arrangement or design that will enhance relaxation. Also, if the user wants to maintain their health, the providing unit can suggest an interior design that uses colors and materials that are considered to be good for health. Furthermore, if the user has a specific health problem, the providing unit can suggest an arrangement or design that addresses that problem. In this way, advice can be provided that is tailored to the user's health condition.

[0056] The providing unit can provide advice based on the user's life events. For example, if the user starts a new job, the providing unit can suggest layouts and designs that will help improve concentration. Also, if the user gets married, the providing unit can suggest interior designs that suit the couple's preferences. Furthermore, if the user is expecting a child, the providing unit can provide advice on how to provide a safe and comfortable environment for the child. In this way, advice can be provided that is tailored to the user's life events.

[0057] The collection unit can collect information tailored to local culture and customs based on the user's geographical location information. For example, the collection unit can collect information based on traditional interior designs in the area where the user lives. The collection unit can also collect information with priority on layout and design information suited to the local climate and environment. Furthermore, the collection unit can collect information based on local feng shui principles. This makes it possible to collect information taking the user's geographical location information into consideration.

[0058] The analysis unit can improve the analysis algorithm based on the user's past feedback. For example, it can improve the accuracy of the analysis by referring to feedback provided by the user in the past. It can also optimize the analysis method by reflecting the user's past feedback. Furthermore, it can adjust the way the analysis results are presented based on the user's past feedback. This makes it possible to perform an analysis that reflects the user's past feedback.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects information on room drawings and layouts from the user. For example, the collection unit may collect information such as the layout of a living room and furniture arrangement provided by the user. The collection unit may also collect information using various means such as voice, text, and images. For example, the collection unit may collect photos of the room taken by the user with a smartphone. The collection unit may also scan the room drawings provided by the user and collect them as digital data. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit optimizes the room layout and interior design based on the principles of Feng Shui. For example, the analysis unit can improve the energy flow in the room by rearranging furniture and using specific colors and materials. The analysis unit can also perform analysis to provide personalized advice based on the user's lifestyle and preferences. For example, if the user is looking for a relaxing space, the analysis unit can suggest the optimal layout and design based on the principles of Feng Shui. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. For example, the providing unit can provide advice to the user online. For example, the providing unit can suggest specific layouts and designs to create a space where the user can relax. The providing unit can also provide advice to reduce the user's stress and enhance the relaxation effect.

[0061] (Example 2) A system according to an embodiment of the present invention optimizes room layout and interior design based on the principles of Feng Shui, providing individual users with a harmonious space. The system analyzes the room's layout and layout provided by the user and provides optimal advice based on the user's lifestyle and preferences. This allows residents to create a comfortable living environment and bring healing and relaxation to their daily lives. For example, the user provides the system with a room's layout and layout, such as the living room layout and furniture arrangement. This information is analyzed in detail by the system. The system then optimizes the room's layout and interior design based on the principles of Feng Shui. For example, by rearranging the furniture or using specific colors and materials, the flow of energy in the room can be improved, thereby creating a comfortable living environment for the user. Furthermore, the system provides personalized advice based on the user's lifestyle and preferences. For example, if a user is looking for a relaxing space, the system suggests optimal layout and design based on the principles of Feng Shui. This allows the user to create healing and relaxation in their daily lives. The system can thereby balance the user's mind and body through personalized consideration of interior design and lifestyle that incorporates the principles of Feng Shui. For example, a specific layout and design can reduce stress and enhance relaxation. Additionally, placement and design based on Feng Shui principles can be expected to improve users' health and well-being.

[0062] An interior design optimization system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information on room plans and layouts from a user. For example, the collection unit may collect information such as the layout of a living room and furniture arrangements provided by the user. The collection unit may also collect information using various means, such as voice, text, and images. For example, the collection unit may collect photos of a room taken by the user with a smartphone. The collection unit may also scan the room plans provided by the user and collect them as digital data. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit may optimize the room layout and interior design based on the principles of feng shui. The analysis unit may improve the flow of energy in a room by, for example, rearranging furniture and using specific colors and materials. The analysis unit may also perform analysis to provide personalized advice based on the user's lifestyle and preferences. For example, if the user is looking for a relaxing space, the analysis unit may suggest an optimal layout and design based on the principles of feng shui. The provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the provision unit may provide advice to the user online. The providing unit, for example, proposes specific layouts and designs to create a space where the user can relax. The providing unit can also provide advice to reduce the user's stress and enhance the relaxation effect. In this way, the interior design optimization system according to the embodiment can optimize the layout and interior design of the user's room and provide a harmonious space.

[0063] The analysis unit can adjust the room layout and interior design based on the principles of Feng Shui. The principles of Feng Shui include, for example, basic rules for improving the flow of energy in a room. For example, the analysis unit can improve the energy flow in a room by changing the furniture layout and using specific colors and materials. For example, the analysis unit can smooth the flow of energy by arranging furniture along the walls and reserving space in the center of the room. The analysis unit can also improve the atmosphere of the room by using specific colors and materials. For example, the analysis unit can use soft colors and natural materials to enhance the relaxing effect. Furthermore, the analysis unit can provide specific advice for optimizing the room layout and interior design based on the principles of Feng Shui. For example, the analysis unit can suggest the optimal layout and design based on the user's lifestyle and preferences. As a result, a harmonious space can be provided by optimizing the room layout and interior design based on the principles of Feng Shui.

[0064] The providing unit can provide advice based on the user's lifestyle and preferences. For example, the providing unit can suggest an optimal interior design based on the user's lifestyle and preferences. For example, if the user is looking for a relaxing space, the providing unit can suggest an optimal layout and design based on the principles of feng shui. The providing unit can also suggest using specific colors and materials according to the user's preferences. For example, the providing unit can improve the atmosphere of the room by using the colors and materials that the user prefers. Furthermore, the providing unit can provide specific advice tailored to the user's lifestyle. For example, if the user is looking for an environment where they can concentrate on work or study, the providing unit can suggest a layout and design that will increase concentration based on the principles of feng shui. This makes it possible to achieve a more appropriate interior design by providing individual advice based on the user's lifestyle and preferences.

[0065] The providing unit may provide advice online. For example, the providing unit may provide advice to a user through an online platform. For example, the providing unit may provide advice to a user in real time through a video call or chat. The providing unit may also receive room drawings and layout information provided by a user online and provide advice based on the received information. For example, the providing unit may analyze photos and drawings of a room uploaded by a user and suggest optimal layouts and designs. Furthermore, the providing unit may record the advice provided online so that the user can refer to it later. For example, the providing unit may save the advice provided to the user in text or video format so that the user can access it at any time. In this way, by providing advice online, the user can receive advice from anywhere.

[0066] The analysis unit can improve the energy flow of a room by changing the furniture arrangement and using colors and materials. For example, the analysis unit can improve the energy flow of a room by changing the furniture arrangement. For example, the analysis unit can smooth the energy flow by arranging furniture along the walls and reserving space in the center of the room. The analysis unit can also improve the atmosphere of a room by using specific colors and materials. For example, the analysis unit can use soft colors and natural materials to enhance the relaxing effect. Furthermore, the analysis unit can provide specific advice for optimizing the room's arrangement and interior design based on the principles of feng shui. For example, the analysis unit can suggest optimal arrangement and design based on the user's lifestyle and preferences. This can improve the energy flow of a room and provide a harmonious space by adjusting the furniture arrangement and using specific colors and materials.

[0067] The providing unit can provide advice on which layout and design will reduce the user's stress and enhance the relaxation effect. For example, the providing unit can provide advice on which specific layout and design will reduce the user's stress and enhance the relaxation effect. For example, if the user is looking for a relaxing space, the providing unit can suggest an optimal layout and design based on the principles of feng shui. The providing unit can also suggest using specific colors and materials according to the user's preferences. For example, the providing unit can improve the atmosphere of the room by using the user's preferred colors and materials. Furthermore, the providing unit can provide specific advice tailored to the user's lifestyle. For example, if the user is looking for an environment where they can concentrate on work or study, the providing unit can suggest a layout and design that will enhance concentration based on the principles of feng shui. This can provide a comfortable space by reducing the user's stress and enhancing the relaxation effect with a specific layout and design.

[0068] The collection unit can estimate the user's emotions and adjust the timing of collecting information about the room's layout and layout based on the user's emotions. The collection unit can, for example, use an emotion recognition algorithm to estimate the user's emotions. For example, the collection unit can analyze the user's facial expressions and voice to estimate the emotions. If the user is relaxed, the collection unit can flexibly set the collection timing and select a time when the user can provide information most comfortably. Furthermore, if the user is feeling stressed, the collection unit can shorten the collection timing and collect information quickly, thereby reducing the burden on the user. Furthermore, if the user is busy, the collection unit can adjust the collection timing to suit the user's schedule and collect information efficiently. As a result, by adjusting the collection timing according to the user's emotions, the user can provide information most comfortably.

[0069] The collection unit can analyze the user's past room layout information and select a collection method. The collection unit can, for example, select the most efficient collection method based on the room layout information provided by the user in the past. For example, the collection unit can extract a specific pattern from the user's past layout information and customize the collection method based on that pattern. The collection unit can also analyze the user's past layout information and provide feedback to optimize the collection method. For example, the collection unit selects the most efficient collection method based on the room layout information provided by the user in the past. The collection unit can extract a specific pattern from the user's past layout information and customize the collection method based on that pattern. The collection unit can also analyze the user's past layout information and provide feedback to optimize the collection method. In this way, information can be efficiently collected by selecting the optimal collection method based on the past layout information.

[0070] When collecting information about a room's layout or layout, the collection unit can select information based on the user's current living situation and areas of interest. The collection unit can, for example, collect only necessary information according to the user's current living situation. For example, the collection unit collects only necessary information according to the user's current living situation. The collection unit can preferentially collect related information based on the user's areas of interest. For example, the collection unit preferentially collects related information based on the user's areas of interest. The collection unit can also customize the information to be collected taking into account the user's living situation and areas of interest. For example, the collection unit customizes the information to be collected taking into account the user's living situation and areas of interest. This enables efficient information collection by collecting only necessary information according to the user's living situation and areas of interest.

[0071] When collecting information about a room's layout and layout, the collection unit can select a collection means according to the user's input method. For example, if the user prefers voice input, the collection unit can collect information about the room's layout and layout by voice. For example, if the user prefers voice input, the collection unit collects information about the room's layout and layout by voice. For example, if the user prefers text input, the collection unit can collect information about the room's layout and layout by text. For example, if the user prefers text input, the collection unit can collect information about the room's layout and layout by text. For example, if the user prefers image input, the collection unit can also collect information about the room's layout and layout by images. For example, if the user prefers image input, the collection unit collects information about the room's layout and layout by images. In this way, by selecting the optimal collection means according to the user's input method, information can be provided in a manner that is most user-friendly for the user.

[0072] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the user's emotions. The collection unit can, for example, use an emotion recognition algorithm to estimate the user's emotions. For example, the collection unit can analyze the user's facial expressions and voice to estimate the emotions. The collection unit can prioritize collecting detailed information when the user is relaxed. For example, the collection unit prioritizes collecting detailed information when the user is relaxed. The collection unit can prioritize collecting basic information when the user is stressed. For example, the collection unit prioritizes collecting basic information when the user is stressed. The collection unit can also prioritize collecting important information when the user is busy. For example, the collection unit prioritizes collecting important information when the user is busy. In this way, more appropriate information can be collected by prioritizing information according to the user's emotions.

[0073] When collecting room diagrams and layout information, the collection unit can prioritize collecting information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting layout information specific to a region based on the user's geographical location information. For example, the collection unit prioritizes collecting layout information specific to a region based on the user's geographical location information. The collection unit can collect information based on feng shui principles of a region by taking into account the user's geographical location information. For example, the collection unit collects information based on feng shui principles of a region by taking into account the user's geographical location information. The collection unit can also collect layout information suitable for the climate and environment of a region by referring to the user's geographical location information. For example, the collection unit collects layout information suitable for the climate and environment of a region by referring to the user's geographical location information. In this way, it is possible to collect information specialized for a region by taking into account the user's geographical location information.

[0074] The collection unit can analyze the user's social media activities and collect information when collecting room drawings and layout information. The collection unit can, for example, analyze the content of the user's posts on social media and collect related layout information. For example, the collection unit analyzes the content of the user's posts on social media and collects related layout information. The collection unit can collect related layout information by referring to the activities of the user's friends on social media. For example, the collection unit collects related layout information by referring to the activities of the user's friends on social media. The collection unit can also collect related layout information based on the user's check-in information on social media. For example, the collection unit collects related layout information based on the user's check-in information on social media. In this way, related information can be efficiently collected by analyzing the user's social media activities.

[0075] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting information about room drawings and layouts. The collection unit can, for example, improve the collection method based on the user's past feedback. For example, the collection unit improves the collection method based on the user's past feedback. The collection unit can adjust the range of information to be collected by reflecting the user's past feedback. For example, the collection unit adjusts the range of information to be collected by reflecting the user's past feedback. The collection unit can also customize the collection method by referring to the user's past feedback. For example, the collection unit customizes the collection method by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the user's emotions. The analysis unit can, for example, use an emotion recognition algorithm to estimate the user's emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate the emotions. The analysis unit can provide detailed analysis results when the user is relaxed. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can provide concise analysis results when the user is stressed. For example, the analysis unit can provide concise analysis results when the user is stressed. The analysis unit can also provide analysis results that are concise when the user is busy. For example, the analysis unit can provide analysis results that are concise when the user is busy. In this way, by adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided.

[0077] During analysis, the analysis unit can adjust the scope of the analysis based on the importance of the room. The analysis unit can, for example, perform a detailed analysis on an important room such as a living room. For example, the analysis unit performs a detailed analysis on an important room such as a living room. The analysis unit can perform a brief analysis on a less important room such as a closet. For example, the analysis unit performs a brief analysis on a less important room such as a closet. The analysis unit can also adjust the level of detail of the analysis according to the importance of the room. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the room. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the room.

[0078] The analysis unit can apply an analysis algorithm according to the room category during analysis. For example, the analysis unit can apply an analysis algorithm for enhancing a relaxing effect to a living room. For example, the analysis unit applies an analysis algorithm for enhancing a relaxing effect to a living room. The analysis unit can apply an analysis algorithm for ensuring an efficient traffic flow to a kitchen. For example, the analysis unit applies an analysis algorithm for ensuring an efficient traffic flow to a kitchen. The analysis unit can also apply an analysis algorithm for providing a comfortable sleeping environment to a bedroom. For example, the analysis unit applies an analysis algorithm for providing a comfortable sleeping environment to a bedroom. In this way, by applying different analysis algorithms according to the room category, more appropriate analysis results can be provided.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, improve the analysis algorithm based on the user's past analysis results. For example, the analysis unit improves the analysis algorithm based on the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also optimize the analysis method by reflecting the user's past analysis results. For example, the analysis unit optimizes the analysis method by reflecting the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the user's emotions. The analysis unit can, for example, use an emotion recognition algorithm to estimate the user's emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate the emotions. The analysis unit can provide a detailed analysis when the user is relaxed. For example, the analysis unit can provide a detailed analysis when the user is relaxed. The analysis unit can provide a concise analysis when the user is stressed. For example, the analysis unit can provide a concise analysis when the user is stressed. The analysis unit can also provide a concise analysis when the user is busy. For example, the analysis unit can provide a concise analysis when the user is busy. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0081] During analysis, the analysis unit can determine the order of analysis based on the time when the rooms were arranged. The analysis unit can, for example, prioritize analyzing rooms that were recently arranged. For example, the analysis unit prioritizes analyzing rooms that were recently arranged. The analysis unit can also prioritize analyzing rooms that have not been changed for a long period of time. For example, the analysis unit prioritizes analyzing rooms that have not been changed for a long period of time. The analysis unit can also determine the priority of analysis based on the time when the rooms were arranged. For example, the analysis unit determines the priority of analysis based on the time when the rooms were arranged. This enables efficient analysis by determining the priority of analysis based on the time when the rooms were arranged.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the rooms. For example, the analysis unit can consecutively analyze rooms with high relevance, such as the living room and the dining room. For example, the analysis unit consecutively analyzes rooms with high relevance, such as the living room and the dining room. The analysis unit can also separately analyze rooms with low relevance, such as a closet and a bedroom. For example, the analysis unit separately analyzes rooms with low relevance, such as a closet and a bedroom. The analysis unit can also adjust the order of analysis based on the relevance of the rooms. For example, the analysis unit adjusts the order of analysis based on the relevance of the rooms. This enables efficient analysis by adjusting the order of analysis based on the relevance of the rooms.

[0083] During analysis, the analysis unit can change the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. If the user does not have technical expertise, the analysis unit can use concise and easy-to-understand terminology. For example, if the user does not have technical expertise, the analysis unit can use concise and easy-to-understand terminology. The analysis unit can also adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0084] The providing unit can estimate the user's emotion and adjust the way in which advice is expressed based on the user's emotion. The providing unit can, for example, use an emotion recognition algorithm to estimate the user's emotion. For example, the providing unit can analyze the user's facial expressions and voice to estimate the emotion. The providing unit can provide detailed advice when the user is relaxed. For example, the providing unit provides detailed advice when the user is relaxed. The providing unit can provide concise advice when the user is stressed. For example, the providing unit provides concise advice when the user is stressed. The providing unit can also provide advice that focuses on the main points when the user is busy. For example, the providing unit provides advice that focuses on the main points when the user is busy. In this way, more appropriate advice can be provided by adjusting the way in which advice is expressed according to the user's emotion.

[0085] When providing advice, the providing unit can adjust the scope of the advice based on the importance of the room. The providing unit can provide detailed advice for an important room such as a living room, for example. For example, the providing unit provides detailed advice for an important room such as a living room. The providing unit can provide concise advice for a room of low importance such as a closet. For example, the providing unit provides concise advice for a room of low importance such as a closet. The providing unit can also adjust the level of detail of the advice according to the importance of the room. For example, the providing unit adjusts the level of detail of the advice according to the importance of the room. This enables efficient advice by adjusting the level of detail of the advice according to the importance of the room.

[0086] The providing unit can apply an advice algorithm according to the room category when providing advice. For example, the providing unit can apply an advice algorithm for enhancing a relaxing effect to a living room. For example, the providing unit applies an advice algorithm for enhancing a relaxing effect to a living room. The providing unit can apply an advice algorithm for ensuring an efficient traffic line to a kitchen. For example, the providing unit applies an advice algorithm for ensuring an efficient traffic line to a kitchen. The providing unit can also apply an advice algorithm for providing a comfortable sleeping environment to a bedroom. For example, the providing unit applies an advice algorithm for providing a comfortable sleeping environment to a bedroom. In this way, by applying different advice algorithms according to the room category, more appropriate advice can be provided.

[0087] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit can, for example, improve the advice algorithm based on the user's past advice results. For example, the providing unit improves the advice algorithm based on the user's past advice results. The providing unit can improve the accuracy of the advice by referring to the user's past advice results. For example, the providing unit improves the accuracy of the advice by referring to the user's past advice results. The providing unit can also optimize the advice method by reflecting the user's past advice results. For example, the providing unit optimizes the advice method by reflecting the user's past advice results. In this way, the accuracy of the advice can be improved by referring to the user's past advice results.

[0088] The providing unit can estimate the user's emotion and adjust the length of the advice based on the user's emotion. The providing unit can, for example, use an emotion recognition algorithm to estimate the user's emotion. For example, the providing unit can analyze the user's facial expressions and voice to estimate the emotion. The providing unit can provide detailed advice when the user is relaxed. For example, the providing unit provides detailed advice when the user is relaxed. The providing unit can provide concise advice when the user is stressed. For example, the providing unit provides concise advice when the user is stressed. The providing unit can also provide advice that hits the key points when the user is busy. For example, the providing unit provides advice that hits the key points when the user is busy. In this way, more appropriate advice can be provided by adjusting the length of the advice according to the user's emotion.

[0089] When providing advice, the providing unit can determine the order of advice based on the time when the rooms were arranged. The providing unit can, for example, give priority to advice about rooms that were recently arranged. For example, the providing unit gives priority to advice about rooms that were recently arranged. The providing unit can also give priority to advice about rooms that have not been changed for a long period of time. For example, the providing unit gives priority to advice about rooms that have not been changed for a long period of time. The providing unit can also determine the priority of advice based on the time when the rooms were arranged. For example, the providing unit determines the priority of advice based on the time when the rooms were arranged. This enables efficient advice by determining the priority of advice based on the time when the rooms were arranged.

[0090] The providing unit can adjust the order of advice based on the relevance of the rooms when providing advice. The providing unit can, for example, consecutively provide advice for rooms with high relevance, such as the living room and the dining room. For example, the providing unit consecutively provides advice for rooms with high relevance, such as the living room and the dining room. The providing unit can also separately provide advice for rooms with low relevance, such as the closet and the bedroom. For example, the providing unit separately provides advice for rooms with low relevance, such as the closet and the bedroom. The providing unit can also adjust the order of advice based on the relevance of the rooms. For example, the providing unit adjusts the order of advice based on the relevance of the rooms. This enables efficient advice by adjusting the order of advice based on the relevance of the rooms.

[0091] When providing advice, the providing unit can change the use of technical terminology in the advice depending on the user's level of expertise. For example, if the user has technical expertise, the providing unit can use detailed technical terminology. For example, if the user has technical expertise, the providing unit uses detailed technical terminology. If the user does not have technical expertise, the providing unit can use concise and easy-to-understand terminology. For example, if the user does not have technical expertise, the providing unit uses concise and easy-to-understand terminology. The providing unit can also adjust the use of technical terminology in the advice depending on the user's level of expertise. For example, the providing unit adjusts the use of technical terminology in the advice depending on the user's level of expertise. In this way, by adjusting the use of technical terminology depending on the user's level of expertise, it is possible to provide advice that is easier to understand. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14 and estimate the user's emotions using the control unit 46A. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and analyze the collected information based on the principles of feng shui to propose an optimal interior design. The provision unit can be realized, for example, by the control unit 46A of the smart device 14, and provide online advice to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214 and estimate the user's emotions using the control unit 46A. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and analyze the collected information based on the principles of feng shui to propose an optimal interior design. The provision unit can be realized, for example, by the control unit 46A of the smart glasses 214, and provide online advice to the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect the user's facial expressions and voice using the camera 42 and microphone 238 of the headset terminal 314 and estimate the user's emotions using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information based on the principles of feng shui to propose an optimal interior design. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314, and can provide online advice to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414 and estimate the user's emotions using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information based on the principles of feng shui to propose an optimal interior design. The provision unit is realized, for example, by the control unit 46A of the robot 414, and can provide online advice to the user.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The providing unit can estimate the user's emotions and determine the priority of advice based on the user's emotions. For example, if the user is feeling stressed, the providing unit can provide priority advice to enhance relaxation. Also, if the user is relaxed, the providing unit can provide more detailed interior design advice. Furthermore, if the user is busy, the providing unit can provide concise advice that focuses on the main points. In this way, by adjusting the priority of advice according to the user's emotions, more appropriate advice can be provided.

[0094] The collection unit can analyze the user's past interior design preferences and customize the type of information to be collected. For example, the collection unit can analyze the patterns of colors and materials selected by the user in the past and make new suggestions based on that. The collection unit can also optimize the information to be collected by referring to the layouts and arrangements that the user has preferred in the past. Furthermore, the collection unit can determine the priority of the information to be collected based on the user's past preferences. This makes it possible to collect information that reflects the user's past preferences.

[0095] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is stressed, concise analysis results can be provided. Furthermore, if the user is busy, analysis results that focus on the main points can be provided. In this way, by adjusting the level of detail of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0096] The providing unit can provide advice taking into consideration the user's current health condition. For example, if the user is tired, the providing unit can suggest an arrangement or design that will enhance relaxation. Also, if the user wants to maintain their health, the providing unit can suggest an interior design that uses colors and materials that are considered to be good for health. Furthermore, if the user has a specific health problem, the providing unit can suggest an arrangement or design that addresses that problem. In this way, advice can be provided that is tailored to the user's health condition.

[0097] The collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the user's emotions. For example, if the user is relaxed, detailed information can be collected. Also, if the user is feeling stressed, basic information can be collected with priority. Furthermore, if the user is busy, important information can be collected with priority. In this way, by adjusting the level of detail of information according to the user's emotions, more appropriate information can be collected.

[0098] The providing unit can provide advice based on the user's life events. For example, if the user starts a new job, the providing unit can suggest layouts and designs that will help improve concentration. Also, if the user gets married, the providing unit can suggest interior designs that suit the couple's preferences. Furthermore, if the user is expecting a child, the providing unit can provide advice on how to provide a safe and comfortable environment for the child. In this way, advice can be provided that is tailored to the user's life events.

[0099] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the user's emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is stressed, a concise analysis can be performed. Furthermore, if the user is busy, an analysis that focuses on the main points can be performed. In this way, by adjusting the timing of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0100] The collection unit can collect information tailored to local culture and customs based on the user's geographical location information. For example, the collection unit can collect information based on traditional interior designs in the area where the user lives. The collection unit can also collect information with priority on layout and design information suited to the local climate and environment. Furthermore, the collection unit can collect information based on local feng shui principles. This makes it possible to collect information taking the user's geographical location information into consideration.

[0101] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the user's emotions. For example, if the user is relaxed, detailed advice can be provided. If the user is stressed, concise advice can be provided. Furthermore, if the user is busy, advice that focuses on the main points can be provided. In this way, by adjusting the way in which advice is expressed depending on the user's emotions, more appropriate advice can be provided.

[0102] The analysis unit can improve the analysis algorithm based on the user's past feedback. For example, it can improve the accuracy of the analysis by referring to feedback provided by the user in the past. It can also optimize the analysis method by reflecting the user's past feedback. Furthermore, it can adjust the way the analysis results are presented based on the user's past feedback. This makes it possible to perform an analysis that reflects the user's past feedback.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects information on room drawings and layouts from the user. For example, the collection unit may collect information such as the layout of a living room and furniture arrangement provided by the user. The collection unit may also collect information using various means such as voice, text, and images. For example, the collection unit may collect photos of the room taken by the user with a smartphone. The collection unit may also scan the room drawings provided by the user and collect them as digital data. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit optimizes the room layout and interior design based on the principles of Feng Shui. For example, the analysis unit can improve the energy flow in the room by rearranging furniture and using specific colors and materials. The analysis unit can also perform analysis to provide personalized advice based on the user's lifestyle and preferences. For example, if the user is looking for a relaxing space, the analysis unit can suggest the optimal layout and design based on the principles of Feng Shui. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. For example, the providing unit can provide advice to the user online. For example, the providing unit can suggest specific layouts and designs to create a space where the user can relax. The providing unit can also provide advice to reduce the user's stress and enhance the relaxation effect.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

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

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information on room drawings and layouts from users; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides advice based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Adapting room layout and interior design based on Feng Shui principles 2. The system of claim 1.

3. The providing unit Providing advice based on the user's lifestyle and preferences 2. The system of claim 1.

4. The providing unit Providing advice online 2. The system of claim 1.

5. The analysis unit Improve the energy flow in a room by rearranging furniture and using colors and materials 2. The system of claim 1.

6. The providing unit The layout and design of the site provide advice that reduces stress and promotes relaxation.

2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of collecting information on room layouts and layouts based on the user's emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past room layout information and select the collection method 2. The system of claim 1.

Images