System

Abstract

An object of a system according to an embodiment is to efficiently collect and utilize information necessary for optimizing a floor plan and a remodeling plan of a house.SOLUTION: A system includes a learning part, a grasping part, a listening part, a generation part, and a provision part. The learning unit learns a state of life using the camera video. The grasping unit grasps a use frequency and a life guideline of the whole house on the basis of the information learned by the learning unit. The listening part learns listening contents with a house owner and a architect. The generation part generates appropriate room arrangement and reform plan on the basis of the information obtained by the grasping part and the listening part. The providing unit provides the plan generated by the generating 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 efficiently collect and utilize the information needed to optimize home layouts and renovation plans, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and utilize information necessary to optimize the layout and renovation plans of a house. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, an understanding unit, a listening unit, a generation unit, and a providing unit. The learning unit learns the state of daily life using camera footage. The understanding unit understands the frequency of use of the entire house and daily life routes based on the information learned by the learning unit. The listening unit learns the contents of interviews with the homeowner and the architect. The generating unit generates an appropriate floor plan and renovation plan based on the information obtained by the understanding unit and the listening unit. The providing unit provides the plan generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and utilize information necessary to optimize the layout and renovation plans of a home. [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 is a system that learns current lifestyle patterns using camera footage while respecting privacy. By learning lifestyle patterns, the AI ​​grasps the frequency of use of the entire house, including rooms, appliances, lights, door opening / closing, and staircase climbing and descending, as well as daily routines. Furthermore, the system learns from interviews and meetings with the homeowner and architect, as well as budget and building restrictions, to generate and provide optimal floor plans and renovation plans. This system enables homeowners to obtain floor plans and renovation plans that best fit their lifestyles, and architects to make more accurate proposals. Furthermore, the highly accurate learning results can be used to sell home appliances and fixed-line services, providing homes and lifestyles that are highly satisfying. For example, camera footage is used to learn current lifestyle patterns. In this process, privacy is respected, and only necessary information is collected. Next, based on the learned information, the AI ​​grasps the frequency of use of the entire house, including rooms, appliances, lights, door opening / closing, and staircase climbing and descending, as well as daily routines. Furthermore, the system learns from interviews and meetings with the homeowner and architect, as well as budget and building restrictions, to generate optimal floor plans and renovation plans. For example, if a homeowner spends a lot of time in the living room, the system can suggest making the living room larger or optimizing the placement of home appliances, improving the convenience of their home. Furthermore, if the homeowner frequently uses stairs, the system can suggest revising the location or shape of the stairs. This allows the system to provide floor plans and renovation plans that are optimal for the homeowner's lifestyle. For example, if a homeowner spends a lot of time in the living room, the system can suggest making the living room larger. Furthermore, optimizing the placement of home appliances can improve the convenience of their home. Furthermore, if the homeowner frequently uses stairs, the system can suggest revising the location or shape of the stairs. This allows homeowners to obtain floor plans and renovation plans that are optimal for their lifestyle, and allows architects to make more accurate proposals. Furthermore, this highly accurate learning can lead to sales of home appliances, fixed-line services, and other products, providing homes and lifestyles that are satisfying to the homeowner.

[0029] A lifestyle learning system according to an embodiment includes a learning unit, an understanding unit, a listening unit, a generating unit, and a providing unit. The learning unit learns daily life patterns using camera footage. For example, the learning unit collects footage from cameras installed in each room of the house and analyzes daily behavior patterns. The learning unit can also limit the storage period for camera footage and strictly manage access rights. For example, the learning unit can store camera footage for 30 days and automatically delete it thereafter. The learning unit can also allow only specific users to access the camera footage. The learning unit can also estimate a user's emotions and adjust the learning frequency of camera footage based on the estimated user emotions. For example, if a user is feeling stressed, the learning frequency of camera footage can be set low to enhance consideration for privacy. The understanding unit understands the usage frequency and daily flow of the entire house based on the information learned by the learning unit. For example, the understanding unit can analyze the usage frequency of each room and understand which rooms the homeowner frequently uses. The understanding unit can also analyze the usage frequency of home appliances and understand which home appliances are frequently used. Furthermore, the grasping unit can adjust the method of grasping the frequency of use according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the grasping unit can concentrate on grasping the frequency of use in the early morning hours. The listening unit learns the content of conversations with the homeowner and the architect. For example, the listening unit can analyze the conversations with the homeowner and the architect using voice recognition technology and extract important information. The listening unit can also estimate the homeowner's emotions and adjust the method of learning the content of conversations based on the estimated emotions. For example, if the user is feeling stressed, the listening unit can prioritize simple questions to reduce the burden of listening. The generation unit generates optimal floor plans and renovation plans based on the information obtained by the grasping unit and the listening unit. For example, if the homeowner spends a lot of time in the living room, the generation unit can suggest increasing the living room area. The generation unit can also suggest optimizing the placement of home appliances. Furthermore, the generation unit can estimate the homeowner's emotions and adjust the method of generating floor plans and renovation plans based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can suggest a simple and relaxing floor plan.The providing unit provides the plan generated by the generating unit. For example, the providing unit presents the generated plan to the homeowner and receives feedback. The providing unit can also estimate the homeowner's emotions and adjust the method of providing the plan based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible plan. This allows the life learning system according to the embodiment to provide floor plans and renovation plans that are optimal for the homeowner's lifestyle.

[0030] The learning unit limits the storage period for camera footage and strictly manages access rights. The learning unit limits the storage period for camera footage. For example, the learning unit stores camera footage for 30 days and then automatically deletes it. The learning unit also strictly manages access rights. For example, the learning unit allows only specific users to access camera footage. Furthermore, the learning unit can record access logs and track who accessed the camera footage and when. This makes it possible to learn about daily life while protecting privacy.

[0031] The listening unit uses speech recognition technology to learn the content of conversations with the landlord and the architect. The listening unit uses speech recognition technology to learn the content of conversations with the landlord and the architect. For example, the listening unit records conversations with the landlord and the architect and converts them into text data using speech recognition technology. The listening unit can also use speech recognition technology to analyze the content of the conversation and extract important information. Furthermore, the listening unit can estimate the emotions of the landlord and adjust the learning method of the content of the conversation based on the estimated emotions. For example, if the user is feeling stressed, simple questions can be prioritized to reduce the burden of listening. In this way, the use of speech recognition technology improves the accuracy of learning the content of the conversation.

[0032] The generation unit makes a suggestion to increase the living room area if the homeowner spends more than a certain amount of time in the living room. The generation unit makes a suggestion to increase the living room area if the homeowner spends more than a certain amount of time in the living room. For example, the generation unit analyzes the amount of time the homeowner spends in the living room and makes a suggestion to increase the living room area. The generation unit can also make a suggestion to optimize the layout of the living room. Furthermore, the generation unit can estimate the homeowner's emotions and make a suggestion to increase the living room area based on the estimated emotions. For example, if the user is relaxing in the living room, a suggestion to increase the living room area is made. This makes it possible to suggest an optimal floor plan that suits the homeowner's lifestyle.

[0033] The generation unit makes suggestions for optimizing the placement of home appliances. The generation unit makes suggestions for optimizing the placement of home appliances. For example, the generation unit analyzes the frequency of use of home appliances and suggests an optimal placement. The generation unit can also suggest the placement of home appliances using an algorithm that optimizes the placement of home appliances. Furthermore, the generation unit can estimate the emotions of the home owner and suggest the placement of home appliances based on the estimated emotions. For example, if the user is relaxing in the living room, the generation unit makes suggestions for optimizing the placement of home appliances in the living room. This makes it possible to improve the convenience of daily life by optimizing the placement of home appliances.

[0034] The generation unit makes a suggestion to reevaluate the position and shape of the stairs if the number of times the user has ascended and descended the stairs is more than a certain number. The generation unit makes a suggestion to reevaluate the position and shape of the stairs if the number of times the user has ascended and descended the stairs is more than a certain number. For example, the generation unit analyzes the number of times the user has ascended and descended the stairs and makes a suggestion to reevaluate the position and shape of the stairs. The generation unit can also reevaluate the stairs using an algorithm that optimizes the position and shape of the stairs. Furthermore, the generation unit can estimate the owner's emotions and make a suggestion to reevaluate the position and shape of the stairs based on the estimated emotions. For example, if the user finds it inconvenient to ascend and descend the stairs, the generation unit makes a suggestion to reevaluate the position and shape of the stairs. This can improve the convenience of daily life by reevaluating the position and shape of the stairs.

[0035] When learning from camera footage, the learning unit changes the focus of learning based on specific time periods or events. When learning from camera footage, the learning unit changes the focus of learning based on specific time periods or events. For example, the learning unit may focus on learning footage of the living room in the morning when the whole family gathers. The learning unit may also focus on learning how the kitchen is used during dinner time. Furthermore, the learning unit may focus on learning footage of the living room and dining room during times when the family gathers on weekends. This allows for efficient data collection by changing the focus of learning based on specific time periods or events.

[0036] When learning from camera footage, the learning unit optimizes the timing of learning according to the homeowner's lifestyle. When learning from camera footage, the learning unit optimizes the timing of learning according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the learning unit will concentrate learning in the early morning hours. Also, if the homeowner is a nocturnal person, the learning unit can concentrate learning in the evening hours. Furthermore, if the homeowner has an irregular lifestyle, the learning unit can set the optimal learning timing based on past data. In this way, data can be collected efficiently by optimizing the timing of learning according to the homeowner's lifestyle.

[0037] When learning from camera footage, the learning unit improves the accuracy of the learning by referring to the homeowner's past behavioral patterns. When learning from camera footage, the learning unit improves the accuracy of the learning by referring to the homeowner's past behavioral patterns. For example, the learning unit may focus on learning footage of rooms that the homeowner has frequently used in the past. The learning unit may also set learning priorities based on the homeowner's behavioral patterns during specific time periods. Furthermore, the learning unit may analyze the homeowner's past behavioral patterns and create an optimal learning schedule. This allows the accuracy of learning to be improved by referring to the homeowner's past behavioral patterns.

[0038] When learning camera footage, the learning unit prioritizes learning highly relevant footage by taking into account the homeowner's geographical location information. When learning camera footage, the learning unit prioritizes learning highly relevant footage by taking into account the homeowner's geographical location information. For example, if the homeowner lives in an urban area, the learning unit may learn by taking into account external noise and traffic conditions. In addition, if the homeowner lives in the suburbs, the learning unit may also learn by taking into account the garden and external environment. Furthermore, if the homeowner lives in a high-rise apartment building, the learning unit may learn by taking into account the usage of elevators and common spaces. In this way, highly relevant footage can be learned by taking into account the homeowner's geographical location information.

[0039] The learning unit analyzes the homeowner's social media activity and learns related videos when learning camera footage. The learning unit analyzes the homeowner's social media activity and learns related videos when learning camera footage. For example, the learning unit prioritizes learning videos of places where the homeowner frequently posts on social media. The learning unit can also analyze the homeowner's social media activity and learn videos of related rooms and places. Furthermore, the learning unit can learn related videos by referring to the activities of the homeowner's friends on social media. In this way, related videos can be learned by analyzing the homeowner's social media activity.

[0040] When learning from camera footage, the learning unit customizes the learning method by reflecting the landlord's past feedback. When learning from camera footage, the learning unit customizes the learning method by reflecting the landlord's past feedback. For example, the learning unit sets a priority for the footage to learn based on feedback provided by the landlord in the past. The learning unit can also adjust the timing and frequency of learning by reflecting the landlord's past feedback. Furthermore, the learning unit can focus on learning footage of specific rooms or locations based on the landlord's feedback. This makes it possible to customize the learning method by reflecting the landlord's past feedback.

[0041] When grasping the frequency of use, the grasping unit focuses on a specific home appliance or room to improve the accuracy of the grasping. When grasping the frequency of use, the grasping unit focuses on a specific home appliance or room to improve the accuracy of the grasping. For example, the grasping unit may focus on grasping the frequency of use of kitchen appliances to analyze cooking habits. The grasping unit may also focus on grasping the frequency of use of living room appliances to analyze how the family spends their time. Furthermore, the grasping unit may focus on grasping the frequency of use of bathroom appliances to analyze bathing habits. In this way, by focusing on a specific home appliance or room, the accuracy of the grasping of frequency of use can be improved.

[0042] When grasping the frequency of use, the grasping unit optimizes the timing of grasping according to the homeowner's lifestyle. When grasping the frequency of use, the grasping unit optimizes the timing of grasping according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the grasping unit may concentrate grasping of the frequency of use in the early morning hours. Also, if the homeowner is a nocturnal person, the grasping unit may concentrate grasping of the frequency of use in the nighttime hours. Furthermore, if the homeowner has an irregular lifestyle, the grasping unit may set the optimal grasping timing based on past data. In this way, by optimizing the timing of grasping according to the homeowner's lifestyle, data can be collected efficiently.

[0043] The grasping unit improves the accuracy of grasping by referring to the homeowner's past behavioral patterns when grasping the frequency of use. The grasping unit improves the accuracy of grasping by referring to the homeowner's past behavioral patterns when grasping the frequency of use. For example, the grasping unit focuses on grasping the frequency of use of home appliances that the homeowner has used frequently in the past. The grasping unit can also set the focus of grasping based on the homeowner's behavioral patterns during specific time periods. Furthermore, the grasping unit can analyze the homeowner's past behavioral patterns and create an optimal grasping schedule. In this way, the accuracy of grasping can be improved by referring to the homeowner's past behavioral patterns.

[0044] When grasping the frequency of use, the grasping unit prioritizes grasping highly relevant data by taking into account the geographical location information of the landlord. When grasping the frequency of use, the grasping unit prioritizes grasping highly relevant data by taking into account the geographical location information of the landlord. For example, if the landlord lives in an urban area, the grasping unit grasps the frequency of use by taking into account external noise and traffic conditions. In addition, if the landlord lives in a suburban area, the grasping unit can also grasp the frequency of use by taking into account the garden and external environment. Furthermore, if the landlord lives in a high-rise apartment building, the grasping unit can also grasp the frequency of use by taking into account the frequency of use of elevators and common spaces. In this way, by taking into account the geographical location information of the landlord, highly relevant data can be grasped preferentially.

[0045] The grasping unit analyzes the landlord's social media activity and grasps related data when grasping the frequency of use. The grasping unit analyzes the landlord's social media activity and grasps related data when grasping the frequency of use. For example, the grasping unit prioritizes grasping the frequency of use of places that the landlord frequently posts about on social media. The grasping unit can also analyze the content of the landlord's social media activity and grasp the frequency of use of related rooms and places. Furthermore, the grasping unit can grasp related data by referring to the activity of the landlord's friends on social media. In this way, it is possible to grasp related data by analyzing the landlord's social media activity.

[0046] The grasping unit customizes the grasping method by reflecting the landlord's past feedback when grasping the frequency of use. The grasping unit customizes the grasping method by reflecting the landlord's past feedback when grasping the frequency of use. For example, the grasping unit sets a priority for grasping the frequency of use based on feedback provided by the landlord in the past. The grasping unit can also adjust the timing and frequency of grasping by reflecting the landlord's past feedback. Furthermore, the grasping unit can also focus on grasping the frequency of use of specific rooms or places based on the landlord's feedback. In this way, the grasping method can be customized by reflecting the landlord's past feedback.

[0047] When learning the listening content, the listening unit focuses on specific questions or topics to improve the accuracy of learning. When learning the listening content, the listening unit focuses on specific questions or topics to improve the accuracy of learning. For example, the listening unit may focus on learning questions about the homeowner's lifestyle. The listening unit may also focus on learning questions about the homeowner's preferences and hobbies. Furthermore, the listening unit may focus on learning questions about the homeowner's family composition and pets. In this way, by focusing on specific questions or topics, the learning accuracy of the listening content can be improved.

[0048] When learning the listening content, the listening unit optimizes the timing of learning according to the homeowner's lifestyle. When learning the listening content, the listening unit optimizes the timing of learning according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the listening unit will conduct the listening in the early morning hours. Also, if the homeowner is a nocturnal person, the listening unit can conduct the listening in the evening hours. Furthermore, if the homeowner has an irregular lifestyle, the listening unit can set the optimal listening timing based on past data. In this way, by optimizing the timing of learning according to the homeowner's lifestyle, data can be collected efficiently.

[0049] When learning the content of the interview, the listening unit refers to the landlord's past answer patterns to improve the accuracy of the learning. When learning the content of the interview, the listening unit refers to the landlord's past answer patterns to improve the accuracy of the learning. For example, the listening unit customizes the content of the interview based on answers provided by the landlord in the past. The listening unit can also analyze the landlord's past answer patterns and set optimal questions. Furthermore, the listening unit can refer to the landlord's past answers to ask questions to collect detailed information. In this way, the accuracy of the learning can be improved by referring to the landlord's past answer patterns.

[0050] When learning the content of the interview, the listening unit takes into consideration the geographical location information of the landlord and prioritizes learning highly relevant content. When learning the content of the interview, the listening unit takes into consideration the geographical location information of the landlord and prioritizes learning highly relevant content. For example, if the landlord lives in an urban area, the listening unit may prioritize questions about issues specific to cities. Also, if the landlord lives in a suburban area, the listening unit may prioritize questions about issues specific to suburban areas. Furthermore, if the landlord lives in a high-rise apartment building, the listening unit may prioritize questions about issues specific to high-rise apartment buildings. In this way, highly relevant content can be prioritized for learning by taking into consideration the geographical location information of the landlord.

[0051] When learning the listening content, the listening unit analyzes the landlord's social media activity and learns related content. When learning the listening content, the listening unit analyzes the landlord's social media activity and learns related content. For example, the listening unit asks questions based on content that the landlord frequently posts on social media. The listening unit can also analyze the landlord's social media activity and ask related questions. Furthermore, the listening unit can also ask related questions based on the activity of the landlord's friends on social media. In this way, related content can be learned by analyzing the landlord's social media activity.

[0052] When learning the listening content, the listening department customizes the learning method by reflecting the homeowner's past feedback. When learning the listening content, the listening department customizes the learning method by reflecting the homeowner's past feedback. For example, the listening department sets priorities for the listening content based on feedback provided by the homeowner in the past. The listening department can also adjust the timing and frequency of the listening by reflecting the homeowner's past feedback. Furthermore, the listening department can focus questions on specific topics based on the homeowner's feedback. In this way, the learning method can be customized by reflecting the homeowner's past feedback.

[0053] When generating floor plans and renovation plans, the generation unit focuses on specific rooms and home appliances to improve the accuracy of generation. When generating floor plans and renovation plans, the generation unit focuses on specific rooms and home appliances to improve the accuracy of generation. For example, the generation unit may focus on optimizing the living room layout to improve comfort for the family. The generation unit may also focus on optimizing the kitchen layout to improve cooking efficiency. Furthermore, the generation unit may focus on optimizing the bathroom layout to provide a relaxing space. In this way, by focusing on specific rooms and home appliances, the accuracy of generation of floor plans and renovation plans can be improved.

[0054] When generating floor plans and renovation plans, the generation unit optimizes the timing of generation according to the homeowner's lifestyle. When generating floor plans and renovation plans, the generation unit optimizes the timing of generation according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the generation unit can suggest a floor plan that is optimal for early morning hours. Also, if the homeowner is a nocturnal person, the generation unit can suggest a floor plan that is optimal for nighttime hours. Furthermore, if the homeowner has an irregular lifestyle, the generation unit can suggest an optimal floor plan based on past data. In this way, by optimizing the timing of generation according to the homeowner's lifestyle, data can be collected efficiently.

[0055] When generating floor plans and renovation plans, the generation unit improves the accuracy of generation by referring to the homeowner's past behavioral patterns. When generating floor plans and renovation plans, the generation unit improves the accuracy of generation by referring to the homeowner's past behavioral patterns. For example, the generation unit may focus on optimizing the floor plans of rooms that the homeowner has frequently used in the past. The generation unit may also set the floor plan based on the homeowner's behavioral patterns during specific time periods. Furthermore, the generation unit may analyze the homeowner's past behavioral patterns and propose the optimal floor plan. In this way, the accuracy of generation can be improved by referring to the homeowner's past behavioral patterns.

[0056] When generating floor plans and renovation plans, the generation unit takes into consideration the geographical location information of the homeowner and prioritizes generating highly relevant plans. When generating floor plans and renovation plans, the generation unit takes into consideration the geographical location information of the homeowner and prioritizes generating highly relevant plans. For example, if the homeowner lives in an urban area, the generation unit can propose a floor plan that addresses issues specific to cities. Also, if the homeowner lives in a suburban area, the generation unit can propose a floor plan that addresses issues specific to suburban areas. Furthermore, if the homeowner lives in a high-rise apartment building, the generation unit can propose a floor plan that addresses issues specific to high-rise apartment buildings. In this way, by taking into consideration the geographical location information of the homeowner, it is possible to prioritize generating highly relevant plans.

[0057] The generation unit analyzes the homeowner's social media activity when generating floor plans and renovation plans, and generates related plans. The generation unit analyzes the homeowner's social media activity when generating floor plans and renovation plans, and generates related plans. For example, the generation unit suggests floor plans based on content frequently posted by the homeowner on social media. The generation unit can also analyze the homeowner's social media activity and suggest related renovation plans. Furthermore, the generation unit can suggest related plans by referring to the activity of the homeowner's friends on social media. In this way, related plans can be generated by analyzing the homeowner's social media activity.

[0058] The generation unit customizes the generation method by reflecting the landlord's past feedback when generating floor plans and renovation plans. The generation unit customizes the generation method by reflecting the landlord's past feedback when generating floor plans and renovation plans. For example, the generation unit sets priorities for floor plans based on feedback provided by the landlord in the past. The generation unit can also adjust the timing and frequency of generation by reflecting the landlord's past feedback. Furthermore, the generation unit can also focus on optimizing floor plans for specific rooms or locations based on the landlord's feedback. This makes it possible to customize the generation method by reflecting the landlord's past feedback.

[0059] When providing a plan, the providing unit focuses on specific rooms and home appliances to improve the accuracy of the plan provided. When providing a plan, the providing unit focuses on specific rooms and home appliances to improve the accuracy of the plan provided. For example, the providing unit may focus on providing living room plans to improve comfort for the family. The providing unit may also focus on providing kitchen plans to improve cooking efficiency. Furthermore, the providing unit may focus on providing bathroom plans to provide a relaxing space. In this way, by focusing on specific rooms and home appliances, the accuracy of the plan provided can be improved.

[0060] When providing a plan, the provision unit optimizes the timing of provision according to the homeowner's lifestyle. When providing a plan, the provision unit optimizes the timing of provision according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the provision unit may provide a plan for early morning hours. Also, if the homeowner is a nocturnal person, the provision unit may provide a plan for nighttime hours. Furthermore, if the homeowner has an irregular lifestyle, the provision unit may set the optimal provision timing based on past data. This allows for efficient data collection by optimizing the timing of provision according to the homeowner's lifestyle.

[0061] When providing a plan, the provision unit refers to the landlord's past behavioral patterns to improve the accuracy of the plan. When providing a plan, the provision unit refers to the landlord's past behavioral patterns to improve the accuracy of the plan. For example, the provision unit customizes the method of providing the plan based on feedback provided by the landlord in the past. The provision unit can also analyze the landlord's past behavioral patterns to provide an optimal plan. Furthermore, the provision unit can also refer to the landlord's past behavioral patterns to provide a plan that includes detailed information. In this way, by referring to the landlord's past behavioral patterns, the accuracy of the plan can be improved.

[0062] When providing a plan, the provision unit takes into consideration the geographical location information of the landlord and provides a highly relevant plan preferentially. When providing a plan, the provision unit takes into consideration the geographical location information of the landlord and provides a highly relevant plan preferentially. For example, if the landlord lives in an urban area, the provision unit may preferentially provide a plan that addresses issues specific to the city. Also, if the landlord lives in a suburban area, the provision unit may preferentially provide a plan that addresses issues specific to the suburban area. Furthermore, if the landlord lives in a high-rise apartment building, the provision unit may preferentially provide a plan that addresses issues specific to high-rise apartment buildings. In this way, by taking into consideration the geographical location information of the landlord, it is possible to preferentially provide a highly relevant plan.

[0063] When providing a plan, the provision unit analyzes the social media activity of the landlord and provides a relevant plan. When providing a plan, the provision unit analyzes the social media activity of the landlord and provides a relevant plan. For example, the provision unit provides a plan based on the content that the landlord frequently posts on social media. The provision unit can also analyze the content of the landlord's social media activity and provide a relevant plan. Furthermore, the provision unit can provide a relevant plan by taking into account the activity of the landlord's friends on social media. In this way, it is possible to provide a relevant plan by analyzing the landlord's social media activity.

[0064] When providing a plan, the provision unit customizes the provision method by reflecting the landlord's past feedback. When providing a plan, the provision unit customizes the provision method by reflecting the landlord's past feedback. For example, the provision unit customizes the plan provision method based on feedback provided by the landlord in the past. The provision unit can also adjust the timing and frequency of provision by reflecting the landlord's past feedback. Furthermore, the provision unit can also provide plans for specific rooms or locations with a focus based on the landlord's feedback. In this way, the provision method can be customized by reflecting the landlord's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The lifestyle learning system can further include an energy consumption unit. The energy consumption unit monitors the energy consumption of the entire house and suggests efficient energy usage. For example, the energy consumption unit analyzes the energy consumption of each home appliance and recommends the use of energy-efficient appliances. The energy consumption unit can also identify peak energy consumption times based on the homeowner's lifestyle and suggest peak shifting. Furthermore, the energy consumption unit can suggest the introduction of a solar power generation system to promote the use of renewable energy. This can optimize energy consumption and contribute to reducing environmental impact.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The learning unit uses camera footage to learn about daily life. For example, it can collect footage from cameras installed in each room of the house and analyze daily behavioral patterns. It can also limit the storage period for camera footage and strictly manage access rights. It can also estimate the user's emotions and adjust the frequency of learning from camera footage based on the estimated emotions. Step 2: The grasping unit grasps the usage frequency and daily life patterns of the entire house based on the information learned by the learning unit. For example, it analyzes the usage frequency of each room and the usage frequency of home appliances to grasp which rooms and home appliances the homeowner uses most frequently. It can also adjust the method of grasping usage frequency according to the homeowner's lifestyle. Step 3: The listener learns from the conversations with the homeowner and architect. For example, it can use voice recognition technology to analyze the conversation and extract important information. It can also estimate the homeowner's emotions and adjust the way it learns from the conversations based on those emotions. Step 4: The generation unit generates an appropriate floor plan and renovation plan based on the information obtained by the understanding unit and the listening unit. For example, if the homeowner spends a lot of time in the living room, the generation unit will suggest increasing the living room's area. It can also suggest optimizing the placement of home appliances. Furthermore, it can estimate the homeowner's emotions and adjust the generation method of the floor plan and renovation plan based on the estimated emotions. Step 5: The providing unit provides the plan generated by the generating unit. For example, the providing unit presents the generated plan to the landlord and receives feedback. The providing unit can also estimate the landlord's emotions and adjust the way the plan is provided based on the estimated emotions.

[0069] (Example 2) A system according to an embodiment of the present invention is a system that learns current lifestyle patterns using camera footage while respecting privacy. By learning lifestyle patterns, the AI ​​grasps the frequency of use of the entire house, including rooms, appliances, lights, door opening / closing, and staircase climbing and descending, as well as daily routines. Furthermore, the system learns from interviews and meetings with the homeowner and architect, as well as budget and building restrictions, to generate and provide optimal floor plans and renovation plans. This system enables homeowners to obtain floor plans and renovation plans that best fit their lifestyles, and architects to make more accurate proposals. Furthermore, the highly accurate learning results can be used to sell home appliances and fixed-line services, providing homes and lifestyles that are highly satisfying. For example, camera footage is used to learn current lifestyle patterns. In this process, privacy is respected, and only necessary information is collected. Next, based on the learned information, the AI ​​grasps the frequency of use of the entire house, including rooms, appliances, lights, door opening / closing, and staircase climbing and descending, as well as daily routines. Furthermore, the system learns from interviews and meetings with the homeowner and architect, as well as budget and building restrictions, to generate optimal floor plans and renovation plans. For example, if a homeowner spends a lot of time in the living room, the system can suggest making the living room larger or optimizing the placement of home appliances, improving the convenience of their home. Furthermore, if the homeowner frequently uses stairs, the system can suggest revising the location or shape of the stairs. This allows the system to provide floor plans and renovation plans that are optimal for the homeowner's lifestyle. For example, if a homeowner spends a lot of time in the living room, the system can suggest making the living room larger. Furthermore, optimizing the placement of home appliances can improve the convenience of their home. Furthermore, if the homeowner frequently uses stairs, the system can suggest revising the location or shape of the stairs. This allows homeowners to obtain floor plans and renovation plans that are optimal for their lifestyle, and allows architects to make more accurate proposals. Furthermore, this highly accurate learning can lead to sales of home appliances, fixed-line services, and other products, providing homes and lifestyles that are satisfying to the homeowner.

[0070] A lifestyle learning system according to an embodiment includes a learning unit, an understanding unit, a listening unit, a generating unit, and a providing unit. The learning unit learns daily life patterns using camera footage. For example, the learning unit collects footage from cameras installed in each room of the house and analyzes daily behavior patterns. The learning unit can also limit the storage period for camera footage and strictly manage access rights. For example, the learning unit can store camera footage for 30 days and automatically delete it thereafter. The learning unit can also allow only specific users to access the camera footage. The learning unit can also estimate a user's emotions and adjust the learning frequency of camera footage based on the estimated user emotions. For example, if a user is feeling stressed, the learning frequency of camera footage can be set low to enhance consideration for privacy. The understanding unit understands the usage frequency and daily flow of the entire house based on the information learned by the learning unit. For example, the understanding unit can analyze the usage frequency of each room and understand which rooms the homeowner frequently uses. The understanding unit can also analyze the usage frequency of home appliances and understand which home appliances are frequently used. Furthermore, the grasping unit can adjust the method of grasping the frequency of use according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the grasping unit can concentrate on grasping the frequency of use in the early morning hours. The listening unit learns the content of conversations with the homeowner and the architect. For example, the listening unit can analyze the conversations with the homeowner and the architect using voice recognition technology and extract important information. The listening unit can also estimate the homeowner's emotions and adjust the method of learning the content of conversations based on the estimated emotions. For example, if the user is feeling stressed, the listening unit can prioritize simple questions to reduce the burden of listening. The generation unit generates optimal floor plans and renovation plans based on the information obtained by the grasping unit and the listening unit. For example, if the homeowner spends a lot of time in the living room, the generation unit can suggest increasing the living room area. The generation unit can also suggest optimizing the placement of home appliances. Furthermore, the generation unit can estimate the homeowner's emotions and adjust the method of generating floor plans and renovation plans based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can suggest a simple and relaxing floor plan.The providing unit provides the plan generated by the generating unit. For example, the providing unit presents the generated plan to the homeowner and receives feedback. The providing unit can also estimate the homeowner's emotions and adjust the method of providing the plan based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible plan. This allows the life learning system according to the embodiment to provide floor plans and renovation plans that are optimal for the homeowner's lifestyle.

[0071] The learning unit limits the storage period for camera footage and strictly manages access rights. The learning unit limits the storage period for camera footage. For example, the learning unit stores camera footage for 30 days and then automatically deletes it. The learning unit also strictly manages access rights. For example, the learning unit allows only specific users to access camera footage. Furthermore, the learning unit can record access logs and track who accessed the camera footage and when. This makes it possible to learn about daily life while protecting privacy.

[0072] The listening unit uses speech recognition technology to learn the content of conversations with the landlord and the architect. The listening unit uses speech recognition technology to learn the content of conversations with the landlord and the architect. For example, the listening unit records conversations with the landlord and the architect and converts them into text data using speech recognition technology. The listening unit can also use speech recognition technology to analyze the content of the conversation and extract important information. Furthermore, the listening unit can estimate the emotions of the landlord and adjust the learning method of the content of the conversation based on the estimated emotions. For example, if the user is feeling stressed, simple questions can be prioritized to reduce the burden of listening. In this way, the use of speech recognition technology improves the accuracy of learning the content of the conversation.

[0073] The generation unit makes a suggestion to increase the living room area if the homeowner spends more than a certain amount of time in the living room. The generation unit makes a suggestion to increase the living room area if the homeowner spends more than a certain amount of time in the living room. For example, the generation unit analyzes the amount of time the homeowner spends in the living room and makes a suggestion to increase the living room area. The generation unit can also make a suggestion to optimize the layout of the living room. Furthermore, the generation unit can estimate the homeowner's emotions and make a suggestion to increase the living room area based on the estimated emotions. For example, if the user is relaxing in the living room, a suggestion to increase the living room area is made. This makes it possible to suggest an optimal floor plan that suits the homeowner's lifestyle.

[0074] The generation unit makes suggestions for optimizing the placement of home appliances. The generation unit makes suggestions for optimizing the placement of home appliances. For example, the generation unit analyzes the frequency of use of home appliances and suggests an optimal placement. The generation unit can also suggest the placement of home appliances using an algorithm that optimizes the placement of home appliances. Furthermore, the generation unit can estimate the emotions of the home owner and suggest the placement of home appliances based on the estimated emotions. For example, if the user is relaxing in the living room, the generation unit makes suggestions for optimizing the placement of home appliances in the living room. This makes it possible to improve the convenience of daily life by optimizing the placement of home appliances.

[0075] The generation unit makes a suggestion to reevaluate the position and shape of the stairs if the number of times the user has ascended and descended the stairs is more than a certain number. The generation unit makes a suggestion to reevaluate the position and shape of the stairs if the number of times the user has ascended and descended the stairs is more than a certain number. For example, the generation unit analyzes the number of times the user has ascended and descended the stairs and makes a suggestion to reevaluate the position and shape of the stairs. The generation unit can also reevaluate the stairs using an algorithm that optimizes the position and shape of the stairs. Furthermore, the generation unit can estimate the owner's emotions and make a suggestion to reevaluate the position and shape of the stairs based on the estimated emotions. For example, if the user finds it inconvenient to ascend and descend the stairs, the generation unit makes a suggestion to reevaluate the position and shape of the stairs. This can improve the convenience of daily life by reevaluating the position and shape of the stairs.

[0076] The learning unit estimates the user's emotions and adjusts the learning frequency of the camera footage based on the estimated user emotions. The learning unit estimates the user's emotions and adjusts the learning frequency of the camera footage based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit may set the learning frequency of the camera footage low to enhance consideration for privacy. Furthermore, if the user is relaxed, the learning unit may set the learning frequency of the camera footage high to collect more detailed data. Furthermore, if the user is busy, the learning unit may concentrate the learning frequency on a specific time period to efficiently collect data. This allows for enhanced consideration for privacy by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] When learning from camera footage, the learning unit changes the focus of learning based on specific time periods or events. When learning from camera footage, the learning unit changes the focus of learning based on specific time periods or events. For example, the learning unit may focus on learning footage of the living room in the morning when the whole family gathers. The learning unit may also focus on learning how the kitchen is used during dinner time. Furthermore, the learning unit may focus on learning footage of the living room and dining room during times when the family gathers on weekends. This allows for efficient data collection by changing the focus of learning based on specific time periods or events.

[0078] When learning from camera footage, the learning unit optimizes the timing of learning according to the homeowner's lifestyle. When learning from camera footage, the learning unit optimizes the timing of learning according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the learning unit will concentrate learning in the early morning hours. Also, if the homeowner is a nocturnal person, the learning unit can concentrate learning in the evening hours. Furthermore, if the homeowner has an irregular lifestyle, the learning unit can set the optimal learning timing based on past data. In this way, data can be collected efficiently by optimizing the timing of learning according to the homeowner's lifestyle.

[0079] When learning from camera footage, the learning unit improves the accuracy of the learning by referring to the homeowner's past behavioral patterns. When learning from camera footage, the learning unit improves the accuracy of the learning by referring to the homeowner's past behavioral patterns. For example, the learning unit may focus on learning footage of rooms that the homeowner has frequently used in the past. The learning unit may also set learning priorities based on the homeowner's behavioral patterns during specific time periods. Furthermore, the learning unit may analyze the homeowner's past behavioral patterns and create an optimal learning schedule. This allows the accuracy of learning to be improved by referring to the homeowner's past behavioral patterns.

[0080] The learning unit estimates the user's emotions and determines the priority of videos to be learned based on the estimated user emotions. The learning unit estimates the user's emotions and determines the priority of videos to be learned based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit may prioritize learning videos of the living room or bedroom, taking privacy into consideration. Alternatively, if the user is relaxed, the learning unit may prioritize learning videos of the kitchen or bathroom. Furthermore, if the user is busy, the learning unit may prioritize learning videos of the entrance or hallway. This allows for enhanced consideration of privacy by determining the priority of videos to be learned based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] When learning camera footage, the learning unit prioritizes learning highly relevant footage by taking into account the homeowner's geographical location information. When learning camera footage, the learning unit prioritizes learning highly relevant footage by taking into account the homeowner's geographical location information. For example, if the homeowner lives in an urban area, the learning unit may learn by taking into account external noise and traffic conditions. In addition, if the homeowner lives in the suburbs, the learning unit may also learn by taking into account the garden and external environment. Furthermore, if the homeowner lives in a high-rise apartment building, the learning unit may learn by taking into account the usage of elevators and common spaces. In this way, highly relevant footage can be learned by taking into account the homeowner's geographical location information.

[0082] The learning unit analyzes the homeowner's social media activity and learns related videos when learning camera footage. The learning unit analyzes the homeowner's social media activity and learns related videos when learning camera footage. For example, the learning unit prioritizes learning videos of places where the homeowner frequently posts on social media. The learning unit can also analyze the homeowner's social media activity and learn videos of related rooms and places. Furthermore, the learning unit can learn related videos by referring to the activities of the homeowner's friends on social media. In this way, related videos can be learned by analyzing the homeowner's social media activity.

[0083] When learning from camera footage, the learning unit customizes the learning method by reflecting the landlord's past feedback. When learning from camera footage, the learning unit customizes the learning method by reflecting the landlord's past feedback. For example, the learning unit sets a priority for the footage to learn based on feedback provided by the landlord in the past. The learning unit can also adjust the timing and frequency of learning by reflecting the landlord's past feedback. Furthermore, the learning unit can focus on learning footage of specific rooms or locations based on the landlord's feedback. This makes it possible to customize the learning method by reflecting the landlord's past feedback.

[0084] The determination unit estimates the user's emotions and adjusts the method of determining the frequency of use based on the estimated user emotions. The determination unit estimates the user's emotions and adjusts the method of determining the frequency of use based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit may set the frequency of use to be determined low, thereby enhancing consideration for privacy. Furthermore, if the user is relaxed, the determination unit may set the frequency of use to be determined high, thereby collecting detailed data. Furthermore, if the user is busy, the determination unit may concentrate the determination of the frequency of use during a specific time period, thereby efficiently collecting data. Thus, by adjusting the method of determining the frequency of use based on the user's emotions, consideration for privacy can be enhanced. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When grasping the frequency of use, the grasping unit focuses on a specific home appliance or room to improve the accuracy of the grasping. When grasping the frequency of use, the grasping unit focuses on a specific home appliance or room to improve the accuracy of the grasping. For example, the grasping unit may focus on grasping the frequency of use of kitchen appliances to analyze cooking habits. The grasping unit may also focus on grasping the frequency of use of living room appliances to analyze how the family spends their time. Furthermore, the grasping unit may focus on grasping the frequency of use of bathroom appliances to analyze bathing habits. In this way, by focusing on a specific home appliance or room, the accuracy of the grasping of frequency of use can be improved.

[0086] When grasping the frequency of use, the grasping unit optimizes the timing of grasping according to the homeowner's lifestyle. When grasping the frequency of use, the grasping unit optimizes the timing of grasping according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the grasping unit may concentrate grasping of the frequency of use in the early morning hours. Also, if the homeowner is a nocturnal person, the grasping unit may concentrate grasping of the frequency of use in the nighttime hours. Furthermore, if the homeowner has an irregular lifestyle, the grasping unit may set the optimal grasping timing based on past data. In this way, by optimizing the timing of grasping according to the homeowner's lifestyle, data can be collected efficiently.

[0087] The grasping unit improves the accuracy of grasping by referring to the homeowner's past behavioral patterns when grasping the frequency of use. The grasping unit improves the accuracy of grasping by referring to the homeowner's past behavioral patterns when grasping the frequency of use. For example, the grasping unit focuses on grasping the frequency of use of home appliances that the homeowner has used frequently in the past. The grasping unit can also set the focus of grasping based on the homeowner's behavioral patterns during specific time periods. Furthermore, the grasping unit can analyze the homeowner's past behavioral patterns and create an optimal grasping schedule. In this way, the accuracy of grasping can be improved by referring to the homeowner's past behavioral patterns.

[0088] The determination unit estimates the user's emotions and determines the priority of usage frequency based on the estimated user emotions. The determination unit estimates the user's emotions and determines the priority of usage frequency based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit may prioritize the usage frequency of the living room and bedroom in consideration of privacy. The determination unit may also prioritize the usage frequency of the kitchen and bathroom in consideration of privacy when the user is relaxed. Furthermore, the determination unit may prioritize the usage frequency of the entrance and hallway in consideration of privacy when the user is busy. In this way, by determining the priority of usage frequency based on the user's emotions, consideration of privacy can be enhanced. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When grasping the frequency of use, the grasping unit prioritizes grasping highly relevant data by taking into account the geographical location information of the landlord. When grasping the frequency of use, the grasping unit prioritizes grasping highly relevant data by taking into account the geographical location information of the landlord. For example, if the landlord lives in an urban area, the grasping unit grasps the frequency of use by taking into account external noise and traffic conditions. In addition, if the landlord lives in a suburban area, the grasping unit can also grasp the frequency of use by taking into account the garden and external environment. Furthermore, if the landlord lives in a high-rise apartment building, the grasping unit can also grasp the frequency of use by taking into account the frequency of use of elevators and common spaces. In this way, by taking into account the geographical location information of the landlord, highly relevant data can be grasped preferentially.

[0090] The grasping unit analyzes the landlord's social media activity and grasps related data when grasping the frequency of use. The grasping unit analyzes the landlord's social media activity and grasps related data when grasping the frequency of use. For example, the grasping unit prioritizes grasping the frequency of use of places that the landlord frequently posts about on social media. The grasping unit can also analyze the content of the landlord's social media activity and grasp the frequency of use of related rooms and places. Furthermore, the grasping unit can grasp related data by referring to the activity of the landlord's friends on social media. In this way, it is possible to grasp related data by analyzing the landlord's social media activity.

[0091] The grasping unit customizes the grasping method by reflecting the landlord's past feedback when grasping the frequency of use. The grasping unit customizes the grasping method by reflecting the landlord's past feedback when grasping the frequency of use. For example, the grasping unit sets a priority for grasping the frequency of use based on feedback provided by the landlord in the past. The grasping unit can also adjust the timing and frequency of grasping by reflecting the landlord's past feedback. Furthermore, the grasping unit can also focus on grasping the frequency of use of specific rooms or places based on the landlord's feedback. In this way, the grasping method can be customized by reflecting the landlord's past feedback.

[0092] The listening unit estimates the user's emotions and adjusts the learning method of the listening content based on the estimated user emotions. The listening unit estimates the user's emotions and adjusts the learning method of the listening content based on the estimated user emotions. For example, if the user is feeling stressed, the listening unit prioritizes simple questions to reduce the burden of listening. Also, if the user is relaxed, the listening unit can ask detailed questions to collect more information. Furthermore, if the user is busy, the listening unit can perform listening that focuses on the main points in a short amount of time. This reduces the burden of listening by adjusting the learning method of the listening content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] When learning the listening content, the listening unit focuses on specific questions or topics to improve the accuracy of learning. When learning the listening content, the listening unit focuses on specific questions or topics to improve the accuracy of learning. For example, the listening unit may focus on learning questions about the homeowner's lifestyle. The listening unit may also focus on learning questions about the homeowner's preferences and hobbies. Furthermore, the listening unit may focus on learning questions about the homeowner's family composition and pets. In this way, by focusing on specific questions or topics, the learning accuracy of the listening content can be improved.

[0094] When learning the listening content, the listening unit optimizes the timing of learning according to the homeowner's lifestyle. When learning the listening content, the listening unit optimizes the timing of learning according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the listening unit will conduct the listening in the early morning hours. Also, if the homeowner is a nocturnal person, the listening unit can conduct the listening in the evening hours. Furthermore, if the homeowner has an irregular lifestyle, the listening unit can set the optimal listening timing based on past data. In this way, by optimizing the timing of learning according to the homeowner's lifestyle, data can be collected efficiently.

[0095] When learning the content of the interview, the listening unit refers to the landlord's past answer patterns to improve the accuracy of the learning. When learning the content of the interview, the listening unit refers to the landlord's past answer patterns to improve the accuracy of the learning. For example, the listening unit customizes the content of the interview based on answers provided by the landlord in the past. The listening unit can also analyze the landlord's past answer patterns and set optimal questions. Furthermore, the listening unit can refer to the landlord's past answers to ask questions to collect detailed information. In this way, the accuracy of the learning can be improved by referring to the landlord's past answer patterns.

[0096] The listening unit estimates the user's emotions and prioritizes the listening content based on the estimated user emotions. The listening unit estimates the user's emotions and prioritizes the listening content based on the estimated user emotions. For example, if the user is feeling stressed, the listening unit may prioritize important questions in consideration of privacy. Also, if the user is relaxed, the listening unit may prioritize detailed questions. Furthermore, if the user is busy, the listening unit may prioritize questions that get to the point in a short amount of time. In this way, by prioritizing the listening content based on the user's emotions, consideration for privacy can be enhanced. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] When learning the content of the interview, the listening unit takes into consideration the geographical location information of the landlord and prioritizes learning highly relevant content. When learning the content of the interview, the listening unit takes into consideration the geographical location information of the landlord and prioritizes learning highly relevant content. For example, if the landlord lives in an urban area, the listening unit may prioritize questions about issues specific to cities. Also, if the landlord lives in a suburban area, the listening unit may prioritize questions about issues specific to suburban areas. Furthermore, if the landlord lives in a high-rise apartment building, the listening unit may prioritize questions about issues specific to high-rise apartment buildings. In this way, highly relevant content can be prioritized for learning by taking into consideration the geographical location information of the landlord.

[0098] When learning the listening content, the listening unit analyzes the landlord's social media activity and learns related content. When learning the listening content, the listening unit analyzes the landlord's social media activity and learns related content. For example, the listening unit asks questions based on content that the landlord frequently posts on social media. The listening unit can also analyze the landlord's social media activity and ask related questions. Furthermore, the listening unit can also ask related questions based on the activity of the landlord's friends on social media. In this way, related content can be learned by analyzing the landlord's social media activity.

[0099] When learning the listening content, the listening department customizes the learning method by reflecting the homeowner's past feedback. When learning the listening content, the listening department customizes the learning method by reflecting the homeowner's past feedback. For example, the listening department sets priorities for the listening content based on feedback provided by the homeowner in the past. The listening department can also adjust the timing and frequency of the listening by reflecting the homeowner's past feedback. Furthermore, the listening department can focus questions on specific topics based on the homeowner's feedback. In this way, the learning method can be customized by reflecting the homeowner's past feedback.

[0100] The generation unit estimates the user's emotions and adjusts the generation method of floor plans and renovation plans based on the estimated user emotions. The generation unit estimates the user's emotions and adjusts the generation method of floor plans and renovation plans based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit proposes a simple and relaxing floor plan. If the user is relaxed, the generation unit can also propose a detailed and customizable renovation plan. Furthermore, if the user is busy, the generation unit can also propose an efficient and functional floor plan. This allows for adjustment of the generation method of floor plans and renovation plans based on the user's emotions, making it possible to make proposals that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] When generating floor plans and renovation plans, the generation unit focuses on specific rooms and home appliances to improve the accuracy of generation. When generating floor plans and renovation plans, the generation unit focuses on specific rooms and home appliances to improve the accuracy of generation. For example, the generation unit may focus on optimizing the living room layout to improve comfort for the family. The generation unit may also focus on optimizing the kitchen layout to improve cooking efficiency. Furthermore, the generation unit may focus on optimizing the bathroom layout to provide a relaxing space. In this way, by focusing on specific rooms and home appliances, the accuracy of generation of floor plans and renovation plans can be improved.

[0102] When generating floor plans and renovation plans, the generation unit optimizes the timing of generation according to the homeowner's lifestyle. When generating floor plans and renovation plans, the generation unit optimizes the timing of generation according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the generation unit can suggest a floor plan that is optimal for early morning hours. Also, if the homeowner is a nocturnal person, the generation unit can suggest a floor plan that is optimal for nighttime hours. Furthermore, if the homeowner has an irregular lifestyle, the generation unit can suggest an optimal floor plan based on past data. In this way, by optimizing the timing of generation according to the homeowner's lifestyle, data can be collected efficiently.

[0103] When generating floor plans and renovation plans, the generation unit improves the accuracy of generation by referring to the homeowner's past behavioral patterns. When generating floor plans and renovation plans, the generation unit improves the accuracy of generation by referring to the homeowner's past behavioral patterns. For example, the generation unit may focus on optimizing the floor plans of rooms that the homeowner has frequently used in the past. The generation unit may also set the floor plan based on the homeowner's behavioral patterns during specific time periods. Furthermore, the generation unit may analyze the homeowner's past behavioral patterns and propose the optimal floor plan. In this way, the accuracy of generation can be improved by referring to the homeowner's past behavioral patterns.

[0104] The generation unit estimates the user's emotions and prioritizes floor plans and renovation plans based on the estimated user emotions. The generation unit estimates the user's emotions and prioritizes floor plans and renovation plans based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit may prioritize suggesting relaxing floor plans. Furthermore, if the user is relaxed, the generation unit may prioritize suggesting detailed and customizable renovation plans. Furthermore, if the user is busy, the generation unit may prioritize suggesting efficient and functional floor plans. Thus, by prioritizing floor plans and renovation plans based on the user's emotions, it becomes possible to make proposals that meet the user's needs. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] When generating floor plans and renovation plans, the generation unit takes into consideration the geographical location information of the homeowner and prioritizes generating highly relevant plans. When generating floor plans and renovation plans, the generation unit takes into consideration the geographical location information of the homeowner and prioritizes generating highly relevant plans. For example, if the homeowner lives in an urban area, the generation unit can propose a floor plan that addresses issues specific to cities. Also, if the homeowner lives in a suburban area, the generation unit can propose a floor plan that addresses issues specific to suburban areas. Furthermore, if the homeowner lives in a high-rise apartment building, the generation unit can propose a floor plan that addresses issues specific to high-rise apartment buildings. In this way, by taking into consideration the geographical location information of the homeowner, it is possible to prioritize generating highly relevant plans.

[0106] The generation unit analyzes the homeowner's social media activity when generating floor plans and renovation plans, and generates related plans. The generation unit analyzes the homeowner's social media activity when generating floor plans and renovation plans, and generates related plans. For example, the generation unit suggests floor plans based on content frequently posted by the homeowner on social media. The generation unit can also analyze the homeowner's social media activity and suggest related renovation plans. Furthermore, the generation unit can suggest related plans by referring to the activity of the homeowner's friends on social media. In this way, related plans can be generated by analyzing the homeowner's social media activity.

[0107] The generation unit customizes the generation method by reflecting the landlord's past feedback when generating floor plans and renovation plans. The generation unit customizes the generation method by reflecting the landlord's past feedback when generating floor plans and renovation plans. For example, the generation unit sets priorities for floor plans based on feedback provided by the landlord in the past. The generation unit can also adjust the timing and frequency of generation by reflecting the landlord's past feedback. Furthermore, the generation unit can also focus on optimizing floor plans for specific rooms or locations based on the landlord's feedback. This makes it possible to customize the generation method by reflecting the landlord's past feedback.

[0108] The provision unit estimates the user's emotions and adjusts the method of providing the plan based on the estimated user emotions. The provision unit estimates the user's emotions and adjusts the method of providing the plan based on the estimated user emotions. For example, if the user is feeling stressed, the provision unit provides a simple, highly visible plan. If the user is relaxed, the provision unit can also provide a plan that includes detailed information. Furthermore, if the user is busy, the provision unit can also provide a plan that focuses on the main points. This allows for adjustment of the method of providing the plan based on the user's emotions, making it possible to make suggestions that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] When providing a plan, the providing unit focuses on specific rooms and home appliances to improve the accuracy of the plan provided. When providing a plan, the providing unit focuses on specific rooms and home appliances to improve the accuracy of the plan provided. For example, the providing unit may focus on providing living room plans to improve comfort for the family. The providing unit may also focus on providing kitchen plans to improve cooking efficiency. Furthermore, the providing unit may focus on providing bathroom plans to provide a relaxing space. In this way, by focusing on specific rooms and home appliances, the accuracy of the plan provided can be improved.

[0110] When providing a plan, the provision unit optimizes the timing of provision according to the homeowner's lifestyle. When providing a plan, the provision unit optimizes the timing of provision according to the homeowner's lifestyle. For example, if the homeowner gets up early in the morning, the provision unit may provide a plan for early morning hours. Also, if the homeowner is a nocturnal person, the provision unit may provide a plan for nighttime hours. Furthermore, if the homeowner has an irregular lifestyle, the provision unit may set the optimal provision timing based on past data. This allows for efficient data collection by optimizing the timing of provision according to the homeowner's lifestyle.

[0111] When providing a plan, the provision unit refers to the landlord's past behavioral patterns to improve the accuracy of the plan. When providing a plan, the provision unit refers to the landlord's past behavioral patterns to improve the accuracy of the plan. For example, the provision unit customizes the method of providing the plan based on feedback provided by the landlord in the past. The provision unit can also analyze the landlord's past behavioral patterns to provide an optimal plan. Furthermore, the provision unit can also refer to the landlord's past behavioral patterns to provide a plan that includes detailed information. In this way, by referring to the landlord's past behavioral patterns, the accuracy of the plan can be improved.

[0112] The providing unit estimates the user's emotions and determines the priority of plans based on the estimated user emotions. The providing unit estimates the user's emotions and determines the priority of plans based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit may prioritize plans that allow the user to relax. Furthermore, if the user is relaxed, the providing unit may prioritize plans that are detailed and customizable. Furthermore, if the user is busy, the providing unit may prioritize plans that are efficient and functional. This enables proposals that meet the user's needs by prioritizing plans based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] When providing a plan, the provision unit takes into consideration the geographical location information of the landlord and provides a highly relevant plan preferentially. When providing a plan, the provision unit takes into consideration the geographical location information of the landlord and provides a highly relevant plan preferentially. For example, if the landlord lives in an urban area, the provision unit may preferentially provide a plan that addresses issues specific to the city. Also, if the landlord lives in a suburban area, the provision unit may preferentially provide a plan that addresses issues specific to the suburban area. Furthermore, if the landlord lives in a high-rise apartment building, the provision unit may preferentially provide a plan that addresses issues specific to high-rise apartment buildings. In this way, by taking into consideration the geographical location information of the landlord, it is possible to preferentially provide a highly relevant plan.

[0114] When providing a plan, the provision unit analyzes the social media activity of the landlord and provides a relevant plan. When providing a plan, the provision unit analyzes the social media activity of the landlord and provides a relevant plan. For example, the provision unit provides a plan based on the content that the landlord frequently posts on social media. The provision unit can also analyze the content of the landlord's social media activity and provide a relevant plan. Furthermore, the provision unit can provide a relevant plan by taking into account the activity of the landlord's friends on social media. In this way, it is possible to provide a relevant plan by analyzing the landlord's social media activity.

[0115] When providing a plan, the provision unit customizes the provision method by reflecting the landlord's past feedback. When providing a plan, the provision unit customizes the provision method by reflecting the landlord's past feedback. For example, the provision unit customizes the plan provision method based on feedback provided by the landlord in the past. The provision unit can also adjust the timing and frequency of provision by reflecting the landlord's past feedback. Furthermore, the provision unit can also provide plans for specific rooms or locations with a focus based on the landlord's feedback. In this way, the provision method can be customized by reflecting the landlord's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the learning unit, grasping unit, listening unit, generation 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 learning unit learns daily life using the camera 42 of the smart device 14. For example, the grasping unit grasps the frequency of use and daily flow of the entire house using the specific processing unit 290 of the data processing device 12. For example, the listening unit learns the content of interviews with the homeowner and architect using the microphone 38B of the smart device 14. For example, the generation unit generates an optimal floor plan and renovation plan using the specific processing unit 290 of the data processing device 12. For example, the provision unit provides the generated plan using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the learning unit, grasping unit, listening unit, generation 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 learning unit learns the state of daily life using the camera 42 of the smart glasses 214. For example, the grasping unit grasps the frequency of use and daily flow of the entire house using the specific processing unit 290 of the data processing device 12. For example, the listening unit learns the content of interviews with the homeowner and architect using the microphone 238 of the smart glasses 214. For example, the generation unit generates an optimal floor plan and renovation plan using the specific processing unit 290 of the data processing device 12. For example, the provision unit provides the generated plan using the display 40A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the learning unit, grasping unit, listening unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the learning unit learns the state of daily life using the camera 42 of the headset type terminal 314. For example, the grasping unit grasps the frequency of use of the entire house and daily life routes using the specific processing unit 290 of the data processing device 12. For example, the listening unit learns the content of interviews with the homeowner and architect using the microphone 238 of the headset type terminal 314. For example, the generation unit generates optimal floor plans and renovation plans using the specific processing unit 290 of the data processing device 12. For example, the provision unit provides the generated plans using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the learning unit, grasping unit, listening unit, generation 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 learning unit learns the state of daily life using the camera 42 of the robot 414. For example, the grasping unit grasps the frequency of use of the entire house and daily life routes using the specific processing unit 290 of the data processing device 12. For example, the listening unit learns the content of interviews with the homeowner and architect using the microphone 238 of the robot 414. For example, the generation unit generates optimal floor plans and renovation plans using the specific processing unit 290 of the data processing device 12. For example, the provision unit provides the generated plans using the display 40A of the robot 414.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The lifestyle learning system can further include an energy consumption unit. The energy consumption unit monitors the energy consumption of the entire house and suggests efficient energy usage. For example, the energy consumption unit analyzes the energy consumption of each home appliance and recommends the use of energy-efficient appliances. The energy consumption unit can also identify peak energy consumption times based on the homeowner's lifestyle and suggest peak shifting. Furthermore, the energy consumption unit can suggest the introduction of a solar power generation system to promote the use of renewable energy. This can optimize energy consumption and contribute to reducing environmental impact.

[0118] The learning unit can further be equipped with a function to analyze voice data. The voice data analysis function analyzes the homeowner's conversations and environmental sounds to help understand their lifestyle patterns. For example, the voice data analysis function can understand what activities the homeowner is engaged in at specific times of the day. The voice data analysis function can also estimate the homeowner's stress level from their voice and suggest a relaxing environment. Furthermore, the voice data analysis function can estimate the homeowner's health condition from their voice and suggest health management measures. This makes it possible to use voice data to understand their lifestyle patterns in more detail.

[0119] The listening unit can further be equipped with a text message analysis function. The text message analysis function analyzes the content of messages between the homeowner and the architect and extracts important information. For example, the text message analysis function can ascertain the details of the renovations desired by the homeowner. The text message analysis function can also analyze the content of proposals from the architect and provide appropriate feedback to the homeowner. Furthermore, the text message analysis function can infer the homeowner's emotions from the text and make suggestions based on those emotions. This makes it possible to use text messages to collect information with greater accuracy.

[0120] The generation unit can further include a 3D modeling function. The 3D modeling function visualizes the generated floor plan or renovation plan as a 3D model. For example, the 3D modeling function allows the homeowner to intuitively understand the size of the living room and the furniture arrangement. The 3D modeling function can also be used to check the details of the renovation plan proposed by the architect. Furthermore, the 3D modeling function can estimate the homeowner's emotions and customize the 3D model based on their emotions. This makes it possible to make proposals that are visually easy to understand, increasing the homeowner's satisfaction.

[0121] The generation unit can further make proposals that take environmental data into consideration. The environmental data is collected based on the local climate and natural environment. For example, the generation unit can propose a floor plan with high insulation performance to a homeowner living in a cold region. The generation unit can also propose a renovation plan that takes measures against mold to a homeowner living in a humid region. Furthermore, the generation unit can estimate the homeowner's emotions and make proposals that take into consideration environmental data based on the emotions. This makes it possible to provide floor plans and renovation plans that are suitable for the local environment.

[0122] The learning unit can also collect the homeowner's health data and make health management suggestions. Health data is collected from wearable devices and smartphones. For example, the learning unit can analyze the homeowner's sleep patterns and suggest a comfortable sleeping environment. The learning unit can also analyze the homeowner's exercise volume and make suggestions to address the lack of exercise. Furthermore, the learning unit can estimate the homeowner's emotions and make health management suggestions based on those emotions. This makes it possible to improve the homeowner's living environment according to their health condition.

[0123] The grasping unit can further grasp the homeowner's hobbies and preferences and optimize the lifestyle flow. The hobbies and preferences are collected from the homeowner's behavioral data and interviews. For example, if the homeowner's hobby is cooking, the grasping unit can make suggestions to increase the frequency of kitchen use. Also, if the homeowner's hobby is reading, the grasping unit can optimize the layout of the living room and study. Furthermore, the grasping unit can estimate the homeowner's emotions and grasp the hobbies and preferences based on the emotions. This makes it possible to optimize the lifestyle flow according to the homeowner's hobbies and preferences.

[0124] The provision unit can also collect feedback from the landlord in real time and instantly revise the proposal. Feedback is collected from the landlord's voice or text messages. For example, if the landlord expresses dissatisfaction with the proposal, the provision unit can immediately present an alternative. Also, if the landlord gives positive feedback on the proposal, the provision unit can make a proposal to strengthen the proposal. Furthermore, the provision unit can estimate the landlord's emotions, collect feedback based on the emotions, and revise the proposal. This can increase the landlord's satisfaction.

[0125] The provision unit can further customize the proposal content based on the homeowner's life events. Life events refer to important events such as the homeowner's marriage, childbirth, or moving. For example, if the homeowner is planning to get married, the provision unit can propose a floor plan suitable for newlywed life. Also, if the homeowner is planning to give birth, the provision unit can propose a renovation plan suitable for raising children. Furthermore, the provision unit can estimate the homeowner's emotions and make proposals according to life events based on the emotions. This makes it possible to make optimal proposals according to the homeowner's life events.

[0126] The proposal department can further adjust the proposal content based on the homeowner's budget. The budget is calculated based on the homeowner's income, expenses, and savings. For example, the proposal department can propose an optimal renovation plan within the homeowner's budget. The proposal department can also make cost-cutting proposals to prevent the budget from going over budget. Furthermore, the proposal department can estimate the homeowner's emotions and adjust the budget based on those emotions. This makes it possible to make realistic proposals that fit the homeowner's budget.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The learning unit uses camera footage to learn about daily life. For example, it can collect footage from cameras installed in each room of the house and analyze daily behavioral patterns. It can also limit the storage period for camera footage and strictly manage access rights. It can also estimate the user's emotions and adjust the frequency of learning from camera footage based on the estimated emotions. Step 2: The grasping unit grasps the usage frequency and daily life patterns of the entire house based on the information learned by the learning unit. For example, it analyzes the usage frequency of each room and the usage frequency of home appliances to grasp which rooms and home appliances the homeowner uses most frequently. It can also adjust the method of grasping usage frequency according to the homeowner's lifestyle. Step 3: The listener learns from the conversations with the homeowner and architect. For example, it can use voice recognition technology to analyze the conversation and extract important information. It can also estimate the homeowner's emotions and adjust the way it learns from the conversations based on those emotions. Step 4: The generation unit generates an appropriate floor plan and renovation plan based on the information obtained by the understanding unit and the listening unit. For example, if the homeowner spends a lot of time in the living room, the generation unit will suggest increasing the living room's area. It can also suggest optimizing the placement of home appliances. Furthermore, it can estimate the homeowner's emotions and adjust the generation method of the floor plan and renovation plan based on the estimated emotions. Step 5: The providing unit provides the plan generated by the generating unit. For example, the providing unit presents the generated plan to the landlord and receives feedback. The providing unit can also estimate the landlord's emotions and adjust the way the plan is provided based on the estimated emotions.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 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.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the 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.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the 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.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] [Explanation of symbols]

[0201] 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 learning department that uses camera footage to learn about daily life, a grasping unit that grasps the frequency of use and daily life routes of the entire house based on the information learned by the learning unit; A listening section where students learn about interviews with landlords and architects, a generation unit that generates an appropriate floor plan and renovation plan based on the information obtained by the grasping unit and the listening unit; a providing unit that provides the plan generated by the generating unit. A system characterized by:

2. The learning unit Limit the storage period for camera footage and strictly manage access rights 2. The system of claim 1.

3. The listening unit Using speech recognition technology to learn what is being heard from homeowners and architects 2. The system of claim 1.

4. The generation unit If the homeowner spends more than a certain amount of time in the living room, we will propose increasing the living room area.

2. The system of claim 1.

5. The generation unit Propose appropriate placement of home appliances 2. The system of claim 1.

6. The generation unit If the number of times the stairs are climbed or descended exceeds a certain number, a suggestion is made to reevaluate the location and shape of the stairs.

2. The system of claim 1.

7. The learning unit Estimate the user's emotions and adjust the learning frequency of camera footage based on the estimated user emotions.

2. The system of claim 1.

8. The learning unit When studying camera footage, change the focus of the study based on specific times or events 2. The system of claim 1.

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