System

Abstract

An object of a system according to an embodiment is to efficiently create an ideal house that reflects a user's desire.SOLUTION: A system according to an embodiment includes a request understanding unit, a house creation unit, and a feedback collection unit. The desire understanding unit understands a user's desire. The house creation unit creates a house on the basis of the request understood by the request understanding unit. The feedback collection unit collects feedback of the user on the house created by the house creation 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] With conventional technology, the process of creating an ideal home that reflects the user's needs is complicated, and it takes a long time to achieve satisfactory results.

[0005] The system according to the embodiment aims to efficiently create an ideal home that reflects the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a request understanding unit, a house creation unit, and a feedback collection unit. The request understanding unit understands a user's request. The house creation unit creates a house based on the request understood by the request understanding unit. The feedback collection unit collects user feedback on the house created by the house creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create an ideal home that reflects the user's wishes. [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 combines a conversational AI with Minecraft, allowing users to repeatedly create their dream home until they are satisfied. In this system, the conversational AI understands the user's requests and automatically creates a sample home in the Minecraft game based on those requests. The user provides feedback to the AI ​​through dialogue, and the AI ​​reflects that feedback and improves the home. This allows the system to repeatedly create the user's dream home until they are satisfied.

[0029] A system according to an embodiment includes a request understanding unit, a house creation unit, and a feedback collection unit. The request understanding unit understands a user's request. For example, if the user requests, "I want the living room to be bigger," the request understanding unit analyzes the request. The request understanding unit also receives the user's request in natural language and understands the content of the request. For example, the request understanding unit receives a specific request such as, "I want the windows to be bigger so that more light can come in." The house creation unit creates a house based on the request understood by the request understanding unit. For example, if the user requests, "I want the living room to be bigger," the house creation unit creates a house with a larger living room. The house creation unit also generates a blueprint that reflects the user's request and constructs the house based on the blueprint. The feedback collection unit collects user feedback on the house created by the house creation unit. For example, the user provides specific feedback such as, "The living room is bigger, but I wish the ceiling was a little higher." The feedback collection unit analyzes this feedback and reflects it in the next sample house. In this way, the system according to an embodiment creates a house based on the user's request and collects feedback, thereby realizing an ideal home.

[0030] The request understanding unit learns the user's past request history, predicts the user's preferences and tendencies, and can proactively suggest requests. For example, the request understanding unit stores the user's past request history in a database, and the conversational AI learns that history. For example, if a user has requested a larger living room in the past, a larger living room will be suggested next time. The request understanding unit also analyzes the user's past request history, and the conversational AI predicts the user's preferences and tendencies. For example, if a user has requested a larger window in the past, a larger window will be suggested next time. The request understanding unit also learns the user's past request history, and the conversational AI proactively suggests requests. For example, if a user has requested a higher ceiling in the past, a higher ceiling will be suggested next time. In this way, by learning the user's past request history, more appropriate suggestions can be made.

[0031] The request understanding unit can make more personalized suggestions by taking into account background information such as the user's lifestyle or family composition. The request understanding unit stores background information such as the user's lifestyle and family composition in a database, and the conversational AI understands the request based on that information. For example, it might suggest a large living room for a household with children. The request understanding unit also takes into account the user's lifestyle and family composition, allowing the conversational AI to make personalized suggestions. For example, it might suggest a compact home for a user living alone. The request understanding unit also takes into account the user's background information, allowing the conversational AI to understand the request and make personalized suggestions. For example, it might suggest a space for pets for a household with pets. This makes it possible to make more appropriate suggestions by taking into account the user's background information.

[0032] The request understanding unit can add a function to visually confirm a user's request using images or videos when understanding the user's request. For example, the request understanding unit adds a function that allows the user to upload images or videos when communicating a request to the conversational AI. For example, the user may upload an image of their ideal living room, and the request is understood based on that. The request understanding unit also adds a function to visually confirm a user's request using images or videos when the conversational AI understands the user's request. For example, the user may explain their ideal window size in a video, and the request is understood based on that. The request understanding unit also adds a function to visually confirm a user's request using images or videos when communicating the user's request to the conversational AI. For example, the user may show an image of their ideal ceiling height, and the request is understood based on that. This makes it possible to understand more specific requests by using images and videos.

[0033] The request understanding unit can understand requests in different languages ​​and build a multilingual system. For example, the request understanding unit introduces multilingual natural language processing technology so that the conversational AI can understand requests in different languages. For example, requests can be accepted in multiple languages, such as English, French, and Chinese. The request understanding unit also builds a multilingual system so that the conversational AI can understand requests in different languages. For example, even if a user communicates a request in Spanish, the conversational AI can understand that request. The request understanding unit also builds a multilingual database so that the conversational AI can understand requests in different languages. For example, even if a user communicates a request in Japanese, the conversational AI can understand that request. This multilingual support makes it possible to accommodate users who speak different languages.

[0034] The house creation unit, when creating a house based on the user's request, incorporates architectural expertise into the design, allowing for the creation of a more realistic house. For example, the house creation unit, when the generation AI creates a house based on the user's request, incorporates architectural expertise into the design. For example, a design based on the Building Standards Act is performed to create a more realistic house. Furthermore, the house creation unit, when the generation AI creates a house based on the user's request, incorporates architectural expertise into the design. For example, a design that takes earthquake resistance and insulation into consideration is performed to create a more realistic house. Furthermore, the house creation unit, when the generation AI creates a house based on the user's request, incorporates architectural expertise into the design. For example, a design that takes energy efficiency into consideration is performed to create a more realistic house. In this way, by incorporating architectural expertise, it becomes possible to create a more realistic house.

[0035] The house creation unit can take environmental factors into consideration when creating a house that reflects the user's requests. For example, the house creation unit takes environmental factors into consideration when the generation AI creates a house that reflects the user's requests. For example, it designs a sunny living room. The house creation unit also takes environmental factors into consideration when the generation AI creates a house that reflects the user's requests. For example, it designs the placement of windows that allow for good ventilation. The house creation unit also takes environmental factors into consideration when the generation AI creates a house that reflects the user's requests. For example, it designs large windows to let in natural light. This makes it possible to create a more comfortable house by taking environmental factors into consideration.

[0036] The house creation unit can add a function that allows different architectural styles to be selected when creating a house. For example, the house creation unit adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a Japanese-style house is selected. The house creation unit also adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a Western-style house is selected. The house creation unit also adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a modern house is selected. This makes it possible to respond to the diverse needs of users by allowing users to select different architectural styles.

[0037] The house creation unit can add a function that allows other users to evaluate a house that has been created and makes improvements based on those evaluations. The house creation unit, for example, adds a function that allows other users to evaluate a house that has been created by the generation AI. For example, the house is improved based on the evaluation score. The house creation unit also adds a function that allows other users to evaluate a house that has been created by the generation AI and makes improvements based on those evaluations. For example, the house is improved based on user comments. The house creation unit also adds a function that allows other users to evaluate a house that has been created by the generation AI and makes improvements based on those evaluations. For example, the parts with high evaluation scores are maintained and the parts with low evaluation scores are improved. This makes it possible to create a better house by making improvements based on the evaluations of other users.

[0038] The feedback collection unit provides an interface that allows the user to suggest specific improvements, thereby obtaining more detailed feedback. The feedback collection unit, for example, provides an interface that allows the user to suggest specific improvements, thereby collecting detailed feedback. For example, a text box or a checklist may be provided. The feedback collection unit also provides an interface that allows the user to suggest specific improvements when collecting feedback. For example, the feedback collection unit may allow the user to select improvements from a selection list. The feedback collection unit also provides an interface that allows the user to suggest specific improvements, thereby obtaining more detailed feedback. For example, the feedback collection unit may allow the user to show the improvements in diagrams or images. In this way, by providing an interface that allows the user to suggest specific improvements, it becomes possible to collect more detailed feedback.

[0039] The feedback collection unit allows the generation AI to learn past feedback history and make improvements that suit the user's preferences. For example, the feedback collection unit allows the generation AI to learn past feedback history and make improvements that suit the user's preferences. For example, if a user has previously requested a larger living room, the feedback collection unit will suggest a larger living room. Furthermore, when reflecting feedback, the feedback collection unit allows the generation AI to learn past feedback history and make improvements that suit the user's preferences. For example, if a user has previously requested larger windows, the feedback collection unit will suggest larger windows. Furthermore, the feedback collection unit allows the generation AI to learn past feedback history and make improvements that suit the user's preferences. For example, if a user has previously requested a higher ceiling, the feedback collection unit will suggest a higher ceiling. In this way, by learning past feedback history, it becomes possible to make improvements that suit the user's preferences.

[0040] The feedback collection unit also incorporates opinions and evaluations from other users, allowing improvements to be made from a more multifaceted perspective. The feedback collection unit, for example, incorporates opinions and evaluations from other users when collecting feedback. For example, feedback evaluated by other users is preferentially reflected. The feedback collection unit also incorporates opinions and evaluations from other users and makes improvements from a more multifaceted perspective. For example, if multiple users suggest the same improvement point, that improvement point is preferentially reflected. The feedback collection unit also incorporates opinions and evaluations from other users when collecting feedback, allowing improvements to be made. For example, feedback comments made by other users is used as a reference. In this way, by incorporating opinions and evaluations from other users, improvements can be made from a more multifaceted perspective.

[0041] The feedback collection unit can cause the generation AI to automatically generate multiple improvement plans and allow the user to select from them. For example, the feedback collection unit causes the generation AI to automatically generate multiple improvement plans and allow the user to select from them. For example, multiple plans for changing the size of the living room are presented. Furthermore, when reflecting feedback, the feedback collection unit causes the generation AI to automatically generate multiple improvement plans and allow the user to select from them. For example, multiple plans for changing the size of the window are presented. Furthermore, the feedback collection unit causes the generation AI to automatically generate multiple improvement plans and allow the user to select from them. For example, multiple plans for changing the ceiling height are presented. In this way, by generating multiple improvement plans and allowing the user to select from them, more appropriate improvements can be made.

[0042] The house creation unit allows the generation AI to learn the history of past improvements and efficiently make improvements when creating a house as many times as necessary until the user is satisfied. For example, the house creation unit allows the generation AI to learn the history of past improvements and efficiently make improvements when creating a house as many times as necessary until the user is satisfied. For example, the generation AI makes efficient improvements based on the history of past changes to the size of the living room. Furthermore, the house creation unit allows the generation AI to learn the history of past improvements and efficiently make improvements when creating a house as many times as necessary until the user is satisfied. For example, the generation AI makes efficient improvements based on the history of past changes to the size of the window. Furthermore, the house creation unit allows the generation AI to learn the history of past improvements and efficiently make improvements when creating a house as many times as necessary until the user is satisfied. For example, the generation AI makes efficient improvements based on the history of past changes to the ceiling height. In this way, by learning the history of past improvements, it becomes possible to make improvements efficiently.

[0043] The house creation unit can make suggestions that take into consideration the user's lifestyle and hobbies and preferences. In the house creation unit, for example, the generation AI makes suggestions that take into consideration the user's lifestyle and hobbies and preferences. For example, for a user who likes the outdoors, the generation AI makes a suggestion to make the garden larger. Furthermore, in order to realize the ideal home, the generation AI makes suggestions that take into consideration the user's lifestyle and hobbies and preferences. For example, for a user who likes reading, the generation AI makes a suggestion to set up a study. Furthermore, in the house creation unit, the generation AI makes suggestions that take into consideration the user's lifestyle and hobbies and preferences. For example, for a user who likes cooking, the generation AI makes a suggestion. In this way, by taking into consideration the user's lifestyle and hobbies and preferences, it is possible to make more appropriate suggestions.

[0044] The house creation unit can add a function to refer to house designs created by other users when realizing one's ideal house. The house creation unit adds a function to refer to house designs created by other users when realizing one's ideal house. For example, the house creation unit refers to living room designs created by other users. The house creation unit also adds a function to refer to house designs created by other users to realize one's ideal house. For example, the house creation unit refers to the window placement created by other users. The house creation unit also adds a function to refer to house designs created by other users when realizing one's ideal house. For example, the house creation unit refers to ceiling heights created by other users. This makes it possible to incorporate a wider variety of ideas by referring to house designs created by other users.

[0045] The house creation unit can perform a design taking different scenarios into consideration when realizing the user's ideal home. For example, the house creation unit performs a design taking different scenarios into consideration when the generation AI realizes the user's ideal home. For example, the number of rooms is increased in consideration of future changes in family composition. The house creation unit also performs a design taking different scenarios into consideration, and the generation AI realizes the user's ideal home. For example, the position of walls is changed in consideration of future renovations. The house creation unit also performs a design taking different scenarios into consideration when the generation AI realizes the user's ideal home. For example, the layout of rooms is changed in consideration of future children's rooms. In this way, by taking different scenarios into consideration, it becomes possible to perform a design that can accommodate future changes.

[0046] The house creation unit can add a function that allows other users to evaluate a saved house design and make improvements based on the evaluations. The house creation unit, for example, adds a function that allows other users to evaluate a saved house design. For example, the house is improved based on the evaluation score. The house creation unit also adds a function that allows other users to evaluate a saved house design and make improvements based on the evaluations. For example, the house is improved based on user comments. The house creation unit also adds a function that allows other users to evaluate a saved house design and make improvements based on the evaluations. For example, the parts with high evaluation scores are maintained and the parts with low evaluation scores are improved. This makes it possible to create a better house by making improvements based on the evaluations of other users.

[0047] The house creation unit may enable the saved house design to be displayed on different platforms. For example, the house creation unit may enable the saved house design to be displayed on a VR platform. For example, the interior of the house may be virtually experienced using a VR headset. The house creation unit may also enable the saved house design to be displayed on an AR platform. For example, the house design may be displayed overlaid on the real world using a smartphone camera. The house creation unit may also enable the saved house design to be displayed on different platforms. For example, the house design may be displayed on various devices using a 3D model. This allows the house design to be viewed in more diverse ways by being able to be displayed on different platforms.

[0048] The house creation unit may add a function that allows the saved house design to be imported into different games or simulation software. For example, the house creation unit adds a function that allows the saved house design to be imported into different games. For example, the house design may be imported into another construction simulation game. The house creation unit also adds a function that allows the saved house design to be imported into different simulation software. For example, the house design may be imported into urban planning simulation software. The house creation unit also adds a function that allows the saved house design to be imported into different games or simulation software. For example, the house design may be imported into educational simulation software. This allows the house design to be used in more diverse ways by being able to be imported into different games or simulation software.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] When understanding a user's requests, the request understanding unit learns the user's past request history, predicts the user's preferences and tendencies, and makes proactive suggestions to meet the request. For example, if a user has requested a larger living room in the past, a larger living room will be suggested next time. The request understanding unit also analyzes the user's past request history, and the conversational AI predicts the user's preferences and tendencies. For example, if a user has requested a larger window in the past, a larger window will be suggested next time. The request understanding unit also learns the user's past request history, and the conversational AI makes proactive suggestions to meet the request. For example, if a user has requested a higher ceiling in the past, a higher ceiling will be suggested next time. In this way, by learning the user's past request history, more appropriate suggestions can be made.

[0051] The request understanding unit can make more personalized suggestions by taking into account background information such as the user's lifestyle or family composition. For example, background information such as the user's lifestyle and family composition is stored in a database, and the conversational AI understands the request based on that information. For example, a large living room is suggested for a household with children. The request understanding unit also takes into account the user's lifestyle and family composition, allowing the conversational AI to make personalized suggestions. For example, a compact home is suggested for a user living alone. The request understanding unit also takes into account the user's background information, allowing the conversational AI to understand the request and make personalized suggestions. For example, a space for pets is suggested for a household with pets. This makes it possible to make more appropriate suggestions by taking into account the user's background information.

[0052] The request understanding unit can add a function to visually confirm a user's request using images or videos when understanding the user's request. For example, a function can be added that allows the user to upload images or videos when communicating a request to the conversational AI. For example, the user can upload an image of their ideal living room, and the request can be understood based on that. The request understanding unit can also add a function to visually confirm a user's request using images or videos when the conversational AI understands the user's request. For example, the user can explain their ideal window size in a video, and the request can be understood based on that. The request understanding unit can also add a function to visually confirm a user's request using images or videos when communicating the user's request to the conversational AI. For example, the user can show an image of their ideal ceiling height, and the request can be understood based on that. This makes it possible to understand more specific requests by using images and videos.

[0053] The request understanding unit can understand requests in different languages ​​and build a multilingual system. For example, multilingual natural language processing technology is introduced so that the conversational AI can understand requests in different languages. For example, requests can be accepted in multiple languages, such as English, French, and Chinese. The request understanding unit also builds a multilingual system so that the conversational AI can understand requests in different languages. For example, even if a user expresses a request in Spanish, the conversational AI will understand that request. The request understanding unit also builds a multilingual database so that the conversational AI can understand requests in different languages. For example, even if a user expresses a request in Japanese, the conversational AI will understand that request. This multilingual support makes it possible to accommodate users who speak different languages.

[0054] The house creation unit, when creating a house based on the user's requests, incorporates architectural expertise into the design, allowing for the creation of a more realistic house. For example, when the generation AI creates a house based on the user's requests, it incorporates architectural expertise into the design. For example, it creates a design based on the Building Standards Act, creating a more realistic house. Furthermore, when the generation AI creates a house based on the user's requests, it incorporates architectural expertise into the design. For example, it creates a design that takes earthquake resistance and insulation into consideration, creating a more realistic house. Furthermore, when the generation AI creates a house based on the user's requests, it incorporates architectural expertise into the design. For example, it creates a design that takes energy efficiency into consideration, creating a more realistic house. In this way, by incorporating architectural expertise, it becomes possible to create a more realistic house.

[0055] The house creation unit can take environmental factors into consideration when creating a house that reflects the user's requests. For example, when the generation AI creates a house that reflects the user's requests, it takes environmental factors into consideration when designing. For example, it designs a sunny living room. The house creation unit also takes environmental factors into consideration when creating a house that reflects the user's requests. For example, it designs the placement of windows that allow for good ventilation. The house creation unit also takes environmental factors into consideration when creating a house that reflects the user's requests. For example, it designs large windows to let in natural light. This makes it possible to create a more comfortable house by taking environmental factors into consideration.

[0056] The house creation unit can add a function that allows different architectural styles to be selected. For example, a function is added that allows different architectural styles to be selected when the generation AI creates a house. For example, a Japanese-style house is selected. The house creation unit also adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a Western-style house is selected. The house creation unit also adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a modern house is selected. This makes it possible to respond to the diverse needs of users by allowing them to select different architectural styles.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The request understanding unit understands the user's request. For example, if the user requests, "I want the living room to be bigger," the request understanding unit analyzes that request. The request understanding unit also receives the user's request in natural language and understands its content. For example, it receives a specific request such as, "I want the windows to be bigger so that more light can come in." Step 2: The house creation unit creates a house based on the request understood by the request understanding unit. For example, if the user requests that the living room be made larger, the house creation unit creates a house with a larger living room. The house creation unit also generates a blueprint that reflects the user's request and builds the house based on that blueprint. Step 3: The feedback collection unit collects user feedback on the house created by the house creation unit. For example, the user may provide specific feedback such as, "The living room is now larger, but I wish the ceiling was a little higher." The feedback collection unit analyzes this feedback and reflects it in the next sample house.

[0059] (Example 2) A system according to an embodiment of the present invention combines a conversational AI with Minecraft, allowing users to repeatedly create their dream home until they are satisfied. In this system, the conversational AI understands the user's requests and automatically creates a sample home in the Minecraft game based on those requests. The user provides feedback to the AI ​​through dialogue, and the AI ​​reflects that feedback and improves the home. This allows the system to repeatedly create the user's dream home until they are satisfied.

[0060] A system according to an embodiment includes a request understanding unit, a house creation unit, and a feedback collection unit. The request understanding unit understands a user's request. For example, if the user requests, "I want the living room to be bigger," the request understanding unit analyzes the request. The request understanding unit also receives the user's request in natural language and understands the content of the request. For example, the request understanding unit receives a specific request such as, "I want the windows to be bigger so that more light can come in." The house creation unit creates a house based on the request understood by the request understanding unit. For example, if the user requests, "I want the living room to be bigger," the house creation unit creates a house with a larger living room. The house creation unit also generates a blueprint that reflects the user's request and constructs the house based on the blueprint. The feedback collection unit collects user feedback on the house created by the house creation unit. For example, the user provides specific feedback such as, "The living room is bigger, but I wish the ceiling was a little higher." The feedback collection unit analyzes this feedback and reflects it in the next sample house. In this way, the system according to an embodiment creates a house based on the user's request and collects feedback, thereby realizing an ideal home.

[0061] The request understanding unit analyzes the user's tone of voice or speaking style, uses the emotion estimation function to understand the user's emotional state, and can extract requests based on those emotions. For example, when a user communicates a request to a conversational AI, the request understanding unit analyzes the user's tone of voice and speaking style and uses the emotion estimation function to understand the user's emotional state. For example, if the user is excited, the request understanding unit extracts a request based on that emotion. The request understanding unit also analyzes the user's tone of voice and speaking style in real time and uses the emotion estimation function to grasp the user's emotional state. For example, if the user is calm, the request understanding unit extracts a request based on that emotion. The request understanding unit also analyzes the user's tone of voice and speaking style when the user communicates a request to a conversational AI and uses the emotion estimation function to understand the user's emotional state. For example, if the user is feeling anxious, the request understanding unit extracts a request based on that emotion. This enables more personalized suggestions by extracting requests based on the user's emotional state.

[0062] The request understanding unit learns the user's past request history, predicts the user's preferences and tendencies, and can proactively suggest requests. For example, the request understanding unit stores the user's past request history in a database, and the conversational AI learns that history. For example, if a user has requested a larger living room in the past, a larger living room will be suggested next time. The request understanding unit also analyzes the user's past request history, and the conversational AI predicts the user's preferences and tendencies. For example, if a user has requested a larger window in the past, a larger window will be suggested next time. The request understanding unit also learns the user's past request history, and the conversational AI proactively suggests requests. For example, if a user has requested a higher ceiling in the past, a higher ceiling will be suggested next time. In this way, by learning the user's past request history, more appropriate suggestions can be made.

[0063] The request understanding unit can make more personalized suggestions by taking into account background information such as the user's lifestyle or family composition. The request understanding unit stores background information such as the user's lifestyle and family composition in a database, and the conversational AI understands the request based on that information. For example, it might suggest a large living room for a household with children. The request understanding unit also takes into account the user's lifestyle and family composition, allowing the conversational AI to make personalized suggestions. For example, it might suggest a compact home for a user living alone. The request understanding unit also takes into account the user's background information, allowing the conversational AI to understand the request and make personalized suggestions. For example, it might suggest a space for pets for a household with pets. This makes it possible to make more appropriate suggestions by taking into account the user's background information.

[0064] The request understanding unit can add a function to visually confirm a user's request using images or videos when understanding the user's request. For example, the request understanding unit adds a function that allows the user to upload images or videos when communicating a request to the conversational AI. For example, the user may upload an image of their ideal living room, and the request is understood based on that. The request understanding unit also adds a function to visually confirm a user's request using images or videos when the conversational AI understands the user's request. For example, the user may explain their ideal window size in a video, and the request is understood based on that. The request understanding unit also adds a function to visually confirm a user's request using images or videos when communicating the user's request to the conversational AI. For example, the user may show an image of their ideal ceiling height, and the request is understood based on that. This makes it possible to understand more specific requests by using images and videos.

[0065] The request understanding unit can understand requests in different languages ​​and build a multilingual system. For example, the request understanding unit introduces multilingual natural language processing technology so that the conversational AI can understand requests in different languages. For example, requests can be accepted in multiple languages, such as English, French, and Chinese. The request understanding unit also builds a multilingual system so that the conversational AI can understand requests in different languages. For example, even if a user communicates a request in Spanish, the conversational AI can understand that request. The request understanding unit also builds a multilingual database so that the conversational AI can understand requests in different languages. For example, even if a user communicates a request in Japanese, the conversational AI can understand that request. This multilingual support makes it possible to accommodate users who speak different languages.

[0066] The request understanding unit uses the emotion estimation function to analyze the emotions of the user when inputting a request in real time, and can conduct a dialogue that elicits positive emotions. For example, the request understanding unit uses the emotion estimation function to analyze the emotions of the user when inputting a request in real time. For example, if the user is feeling anxious, the conversational AI will engage in positive dialogue to give the user a sense of security. The request understanding unit also uses the emotion estimation function to analyze the emotions of the user when inputting a request in real time, and conduct a dialogue that elicits positive emotions. For example, if the user is excited, the dialogue will further enhance that emotion. The request understanding unit also uses the emotion estimation function to analyze the emotions of the user when inputting a request in real time, and conduct a dialogue that elicits positive emotions. For example, if the user is calm, the dialogue will maintain that emotion. This enables better dialogue by analyzing the user's emotions in real time and eliciting positive emotions.

[0067] The house creation unit, when creating a house based on the user's request, incorporates architectural expertise into the design, allowing for the creation of a more realistic house. For example, the house creation unit, when the generation AI creates a house based on the user's request, incorporates architectural expertise into the design. For example, a design based on the Building Standards Act is performed to create a more realistic house. Furthermore, the house creation unit, when the generation AI creates a house based on the user's request, incorporates architectural expertise into the design. For example, a design that takes earthquake resistance and insulation into consideration is performed to create a more realistic house. Furthermore, the house creation unit, when the generation AI creates a house based on the user's request, incorporates architectural expertise into the design. For example, a design that takes energy efficiency into consideration is performed to create a more realistic house. In this way, by incorporating architectural expertise, it becomes possible to create a more realistic house.

[0068] The house creation unit can take environmental factors into consideration when creating a house that reflects the user's requests. For example, the house creation unit takes environmental factors into consideration when the generation AI creates a house that reflects the user's requests. For example, it designs a sunny living room. The house creation unit also takes environmental factors into consideration when the generation AI creates a house that reflects the user's requests. For example, it designs the placement of windows that allow for good ventilation. The house creation unit also takes environmental factors into consideration when the generation AI creates a house that reflects the user's requests. For example, it designs large windows to let in natural light. This makes it possible to create a more comfortable house by taking environmental factors into consideration.

[0069] The house creation unit fine-tunes the house design based on the user's emotion estimation result, thereby providing a design that is more satisfying to the user. The house creation unit, for example, fine-tunes the house design created by the generation AI based on the user's emotion estimation result. For example, if the user is not satisfied, the design is changed based on the user's emotion. The house creation unit also fine-tunes the house design created by the generation AI based on the user's emotion estimation result. For example, if the user is excited, the design is changed based on the user's emotion. The house creation unit also fine-tunes the house design created by the generation AI based on the user's emotion estimation result. For example, if the user is feeling anxious, the design is changed based on the user's emotion. In this way, by fine-tuning the design based on the user's emotion estimation result, it is possible to provide a house that is more satisfying.

[0070] The house creation unit can add a function that allows different architectural styles to be selected when creating a house. For example, the house creation unit adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a Japanese-style house is selected. The house creation unit also adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a Western-style house is selected. The house creation unit also adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a modern house is selected. This makes it possible to respond to the diverse needs of users by allowing users to select different architectural styles.

[0071] The house creation unit can add a function that allows other users to evaluate a house that has been created and makes improvements based on those evaluations. The house creation unit, for example, adds a function that allows other users to evaluate a house that has been created by the generation AI. For example, the house is improved based on the evaluation score. The house creation unit also adds a function that allows other users to evaluate a house that has been created by the generation AI and makes improvements based on those evaluations. For example, the house is improved based on user comments. The house creation unit also adds a function that allows other users to evaluate a house that has been created by the generation AI and makes improvements based on those evaluations. For example, the parts with high evaluation scores are maintained and the parts with low evaluation scores are improved. This makes it possible to create a better house by making improvements based on the evaluations of other users.

[0072] The house creation unit can use the emotion estimation function to analyze in real time what emotions the user has regarding the house design and reflect the emotions in the design. The house creation unit, for example, uses the emotion estimation function to analyze in real time what emotions the user has regarding the house design. For example, if the user is satisfied, the emotion is reflected in the design. The house creation unit also uses the emotion estimation function to analyze in real time what emotions the user has regarding the house design and reflect the emotions in the design. For example, if the user is dissatisfied, the emotion is reflected in the design. The house creation unit also uses the emotion estimation function to analyze in real time what emotions the user has regarding the house design and reflect the emotions in the design. For example, if the user is excited, the emotion is reflected in the design. In this way, by analyzing the user's emotions in real time and reflecting them in the design, it is possible to provide a house that provides greater satisfaction.

[0073] The feedback collection unit can use the emotion estimation function to analyze the emotional intensity of the user's feedback and determine its importance. For example, when collecting user feedback, the feedback collection unit uses the emotion estimation function to analyze the emotional intensity of the feedback. For example, it determines that feedback with a strong positive emotion has high importance. The feedback collection unit also uses the emotion estimation function to analyze the emotional intensity of the user's feedback and determine its importance. For example, it determines that feedback with a strong negative emotion has high importance. When collecting user feedback, the feedback collection unit also uses the emotion estimation function to analyze the emotional intensity of the feedback and determine its importance. For example, it preferentially reflects feedback with a high emotional intensity. This makes it possible to determine the importance and make more appropriate improvements by analyzing the emotional intensity of the feedback.

[0074] The feedback collection unit provides an interface that allows the user to suggest specific improvements, thereby obtaining more detailed feedback. The feedback collection unit, for example, provides an interface that allows the user to suggest specific improvements, thereby collecting detailed feedback. For example, a text box or a checklist may be provided. The feedback collection unit also provides an interface that allows the user to suggest specific improvements when collecting feedback. For example, the feedback collection unit may allow the user to select improvements from a selection list. The feedback collection unit also provides an interface that allows the user to suggest specific improvements, thereby obtaining more detailed feedback. For example, the feedback collection unit may allow the user to show the improvements in diagrams or images. In this way, by providing an interface that allows the user to suggest specific improvements, it becomes possible to collect more detailed feedback.

[0075] The feedback collection unit allows the generation AI to learn past feedback history and make improvements that suit the user's preferences. For example, the feedback collection unit allows the generation AI to learn past feedback history and make improvements that suit the user's preferences. For example, if a user has previously requested a larger living room, the feedback collection unit will suggest a larger living room. Furthermore, when reflecting feedback, the feedback collection unit allows the generation AI to learn past feedback history and make improvements that suit the user's preferences. For example, if a user has previously requested larger windows, the feedback collection unit will suggest larger windows. Furthermore, the feedback collection unit allows the generation AI to learn past feedback history and make improvements that suit the user's preferences. For example, if a user has previously requested a higher ceiling, the feedback collection unit will suggest a higher ceiling. In this way, by learning past feedback history, it becomes possible to make improvements that suit the user's preferences.

[0076] The feedback collection unit also incorporates opinions and evaluations from other users, allowing improvements to be made from a more multifaceted perspective. The feedback collection unit, for example, incorporates opinions and evaluations from other users when collecting feedback. For example, feedback evaluated by other users is preferentially reflected. The feedback collection unit also incorporates opinions and evaluations from other users and makes improvements from a more multifaceted perspective. For example, if multiple users suggest the same improvement point, that improvement point is preferentially reflected. The feedback collection unit also incorporates opinions and evaluations from other users when collecting feedback, allowing improvements to be made. For example, feedback comments made by other users is used as a reference. In this way, by incorporating opinions and evaluations from other users, improvements can be made from a more multifaceted perspective.

[0077] The feedback collection unit can cause the generation AI to automatically generate multiple improvement plans and allow the user to select from them. For example, the feedback collection unit causes the generation AI to automatically generate multiple improvement plans and allow the user to select from them. For example, multiple plans for changing the size of the living room are presented. Furthermore, when reflecting feedback, the feedback collection unit causes the generation AI to automatically generate multiple improvement plans and allow the user to select from them. For example, multiple plans for changing the size of the window are presented. Furthermore, the feedback collection unit causes the generation AI to automatically generate multiple improvement plans and allow the user to select from them. For example, multiple plans for changing the ceiling height are presented. In this way, by generating multiple improvement plans and allowing the user to select from them, more appropriate improvements can be made.

[0078] The feedback collection unit can use the emotion estimation function to analyze the user's emotion regarding the feedback and make improvements that draw out positive emotions. The feedback collection unit, for example, uses the emotion estimation function to analyze the user's emotion regarding the feedback and make improvements that draw out positive emotions. For example, it emphasizes parts that the user is satisfied with. The feedback collection unit also uses the emotion estimation function to analyze the user's emotion regarding the feedback and make improvements that draw out positive emotions. For example, it improves parts that the user is dissatisfied with. The feedback collection unit also uses the emotion estimation function to analyze the user's emotion regarding the feedback and make improvements that draw out positive emotions. For example, it emphasizes parts that the user is excited about. This makes it possible to analyze the user's emotion regarding the feedback and draw out positive emotions, thereby making better improvements.

[0079] The house creation unit can monitor the user's emotional state using the emotion estimation function and suggest improvements at the optimal timing. For example, when the generation AI is realizing the user's ideal home, the house creation unit uses the emotion estimation function to monitor the user's emotional state. For example, if the user is satisfied, the house creation unit suggests improvements at that timing. The house creation unit also uses the emotion estimation function to monitor the user's emotional state and suggest improvements at the optimal timing. For example, if the user is dissatisfied, the house creation unit also uses the emotion estimation function to monitor the user's emotional state when the generation AI is realizing the user's ideal home and suggest improvements at the optimal timing. For example, if the user is excited, the house creation unit suggests improvements at that timing. In this way, by monitoring the user's emotional state and suggesting improvements at the optimal timing, it is possible to provide a home that provides greater satisfaction.

[0080] The house creation unit allows the generation AI to learn the history of past improvements and efficiently make improvements when creating a house as many times as necessary until the user is satisfied. For example, the house creation unit allows the generation AI to learn the history of past improvements and efficiently make improvements when creating a house as many times as necessary until the user is satisfied. For example, the generation AI makes efficient improvements based on the history of past changes to the size of the living room. Furthermore, the house creation unit allows the generation AI to learn the history of past improvements and efficiently make improvements when creating a house as many times as necessary until the user is satisfied. For example, the generation AI makes efficient improvements based on the history of past changes to the size of the window. Furthermore, the house creation unit allows the generation AI to learn the history of past improvements and efficiently make improvements when creating a house as many times as necessary until the user is satisfied. For example, the generation AI makes efficient improvements based on the history of past changes to the ceiling height. In this way, by learning the history of past improvements, it becomes possible to make improvements efficiently.

[0081] The house creation unit can make suggestions that take into consideration the user's lifestyle and hobbies and preferences. In the house creation unit, for example, the generation AI makes suggestions that take into consideration the user's lifestyle and hobbies and preferences. For example, for a user who likes the outdoors, the generation AI makes a suggestion to make the garden larger. Furthermore, in order to realize the ideal home, the generation AI makes suggestions that take into consideration the user's lifestyle and hobbies and preferences. For example, for a user who likes reading, the generation AI makes a suggestion to set up a study. Furthermore, in the house creation unit, the generation AI makes suggestions that take into consideration the user's lifestyle and hobbies and preferences. For example, for a user who likes cooking, the generation AI makes a suggestion. In this way, by taking into consideration the user's lifestyle and hobbies and preferences, it is possible to make more appropriate suggestions.

[0082] The house creation unit can add a function to refer to house designs created by other users when realizing one's ideal house. The house creation unit adds a function to refer to house designs created by other users when realizing one's ideal house. For example, the house creation unit refers to living room designs created by other users. The house creation unit also adds a function to refer to house designs created by other users to realize one's ideal house. For example, the house creation unit refers to the window placement created by other users. The house creation unit also adds a function to refer to house designs created by other users when realizing one's ideal house. For example, the house creation unit refers to ceiling heights created by other users. This makes it possible to incorporate a wider variety of ideas by referring to house designs created by other users.

[0083] The house creation unit can perform a design taking different scenarios into consideration when realizing the user's ideal home. For example, the house creation unit performs a design taking different scenarios into consideration when the generation AI realizes the user's ideal home. For example, the number of rooms is increased in consideration of future changes in family composition. The house creation unit also performs a design taking different scenarios into consideration, and the generation AI realizes the user's ideal home. For example, the position of walls is changed in consideration of future renovations. The house creation unit also performs a design taking different scenarios into consideration when the generation AI realizes the user's ideal home. For example, the layout of rooms is changed in consideration of future children's rooms. In this way, by taking different scenarios into consideration, it becomes possible to perform a design that can accommodate future changes.

[0084] The house creation unit can use the emotion estimation function to analyze in real time what emotions the user has about their dream house and reflect the results in the design. For example, the house creation unit uses the emotion estimation function to analyze in real time what emotions the user has about their dream house and reflect the results in the design. For example, if the user is satisfied, the emotion is reflected in the design. The house creation unit also uses the emotion estimation function to analyze in real time what emotions the user has about their dream house and reflect the results in the design. For example, if the user is dissatisfied, the emotion is reflected in the design. The house creation unit also uses the emotion estimation function to analyze in real time what emotions the user has about their dream house and reflect the results in the design. For example, if the user is excited, the emotion is reflected in the design. In this way, by analyzing the user's emotions in real time and reflecting them in the design, it is possible to provide a house that provides greater satisfaction.

[0085] The house creation unit can use the emotion estimation function to record the user's emotional state when saving a house design created by the user, allowing for later reference. For example, the house creation unit uses the emotion estimation function to record the user's emotional state when saving a house design created by the user. For example, the emotion score at the time of saving is recorded so that it can be referenced later. The house creation unit also uses the emotion estimation function to record the user's emotional state when saving a house design created by the user, allowing for later reference. For example, the emotion at the time of saving is recorded as text data. The house creation unit also uses the emotion estimation function to record the user's emotional state when saving a house design created by the user, allowing for later reference. For example, the emotion at the time of saving is displayed as a graph. In this way, by recording the user's emotional state, it becomes possible to facilitate later reference.

[0086] The house creation unit can add a function that allows other users to evaluate a saved house design and make improvements based on the evaluations. The house creation unit, for example, adds a function that allows other users to evaluate a saved house design. For example, the house is improved based on the evaluation score. The house creation unit also adds a function that allows other users to evaluate a saved house design and make improvements based on the evaluations. For example, the house is improved based on user comments. The house creation unit also adds a function that allows other users to evaluate a saved house design and make improvements based on the evaluations. For example, the parts with high evaluation scores are maintained and the parts with low evaluation scores are improved. This makes it possible to create a better house by making improvements based on the evaluations of other users.

[0087] The house creation unit may enable the saved house design to be displayed on different platforms. For example, the house creation unit may enable the saved house design to be displayed on a VR platform. For example, the interior of the house may be virtually experienced using a VR headset. The house creation unit may also enable the saved house design to be displayed on an AR platform. For example, the house design may be displayed overlaid on the real world using a smartphone camera. The house creation unit may also enable the saved house design to be displayed on different platforms. For example, the house design may be displayed on various devices using a 3D model. This allows the house design to be viewed in more diverse ways by being able to be displayed on different platforms.

[0088] When sharing a saved house design with another user, the house creation unit can use the emotion estimation function to analyze the emotional reaction of the sharing partner and share the design at optimal timing. For example, when sharing a saved house design with another user, the house creation unit uses the emotion estimation function to analyze the emotional reaction of the sharing partner. For example, the house creation unit shares the design at a timing when the sharing partner is feeling positive emotions. Furthermore, the house creation unit uses the emotion estimation function to analyze the emotional reaction when sharing the saved house design with another user and share the design at optimal timing. For example, the house creation unit shares the design at a timing when the sharing partner is excited. Furthermore, when sharing a saved house design with another user, the house creation unit uses the emotion estimation function to analyze the emotional reaction and share the design at optimal timing. For example, the house creation unit shares the design at a timing when the sharing partner is calm. In this way, by analyzing the emotional reaction of the sharing partner and sharing the design at optimal timing, more effective sharing is possible.

[0089] The house creation unit may add a function that allows the saved house design to be imported into different games or simulation software. For example, the house creation unit adds a function that allows the saved house design to be imported into different games. For example, the house design may be imported into another construction simulation game. The house creation unit also adds a function that allows the saved house design to be imported into different simulation software. For example, the house design may be imported into urban planning simulation software. The house creation unit also adds a function that allows the saved house design to be imported into different games or simulation software. For example, the house design may be imported into educational simulation software. This allows the house design to be used in more diverse ways by being able to be imported into different games or simulation software.

[0090] The house creation unit can use the emotion estimation function to analyze how the user feels about the saved house design and communicate that emotion when sharing. For example, the house creation unit uses the emotion estimation function to analyze how the user feels about the saved house design and communicate that emotion when sharing. For example, the user communicates that they are satisfied to the sharing partner. The house creation unit also uses the emotion estimation function to analyze how the user feels about the saved house design and communicate that emotion when sharing. For example, the user communicates that they are excited to the sharing partner. The house creation unit also uses the emotion estimation function to analyze how the user feels about the saved house design and communicate that emotion when sharing. For example, the user communicates that they are anxious to the sharing partner. In this way, by analyzing the user's emotion and communicating that emotion when sharing, more effective sharing is possible.

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

[0092] When understanding a user's requests, the request understanding unit learns the user's past request history, predicts the user's preferences and tendencies, and makes proactive suggestions to meet the request. For example, if a user has requested a larger living room in the past, a larger living room will be suggested next time. The request understanding unit also analyzes the user's past request history, and the conversational AI predicts the user's preferences and tendencies. For example, if a user has requested a larger window in the past, a larger window will be suggested next time. The request understanding unit also learns the user's past request history, and the conversational AI makes proactive suggestions to meet the request. For example, if a user has requested a higher ceiling in the past, a higher ceiling will be suggested next time. In this way, by learning the user's past request history, more appropriate suggestions can be made.

[0093] The request understanding unit analyzes the user's tone of voice or speaking style, uses the emotion estimation function to understand the user's emotional state, and can extract requests based on those emotions. For example, when a user communicates a request to a conversational AI, the unit analyzes the user's tone of voice and speaking style and uses the emotion estimation function to understand the user's emotional state. For example, if the user is excited, the unit extracts a request based on that emotion. The request understanding unit also analyzes the user's tone of voice and speaking style in real time and uses the emotion estimation function to grasp the user's emotional state. For example, if the user is calm, the unit extracts a request based on that emotion. The request understanding unit also analyzes the user's tone of voice and speaking style when the user communicates a request to a conversational AI, and uses the emotion estimation function to understand the user's emotional state. For example, if the user is feeling anxious, the unit extracts a request based on that emotion. This enables more personalized suggestions to be made by extracting requests based on the user's emotional state.

[0094] The request understanding unit can make more personalized suggestions by taking into account background information such as the user's lifestyle or family composition. For example, background information such as the user's lifestyle and family composition is stored in a database, and the conversational AI understands the request based on that information. For example, a large living room is suggested for a household with children. The request understanding unit also takes into account the user's lifestyle and family composition, allowing the conversational AI to make personalized suggestions. For example, a compact home is suggested for a user living alone. The request understanding unit also takes into account the user's background information, allowing the conversational AI to understand the request and make personalized suggestions. For example, a space for pets is suggested for a household with pets. This makes it possible to make more appropriate suggestions by taking into account the user's background information.

[0095] The request understanding unit can add a function to visually confirm a user's request using images or videos when understanding the user's request. For example, a function can be added that allows the user to upload images or videos when communicating a request to the conversational AI. For example, the user can upload an image of their ideal living room, and the request can be understood based on that. The request understanding unit can also add a function to visually confirm a user's request using images or videos when the conversational AI understands the user's request. For example, the user can explain their ideal window size in a video, and the request can be understood based on that. The request understanding unit can also add a function to visually confirm a user's request using images or videos when communicating the user's request to the conversational AI. For example, the user can show an image of their ideal ceiling height, and the request can be understood based on that. This makes it possible to understand more specific requests by using images and videos.

[0096] The request understanding unit can understand requests in different languages ​​and build a multilingual system. For example, multilingual natural language processing technology is introduced so that the conversational AI can understand requests in different languages. For example, requests can be accepted in multiple languages, such as English, French, and Chinese. The request understanding unit also builds a multilingual system so that the conversational AI can understand requests in different languages. For example, even if a user expresses a request in Spanish, the conversational AI will understand that request. The request understanding unit also builds a multilingual database so that the conversational AI can understand requests in different languages. For example, even if a user expresses a request in Japanese, the conversational AI will understand that request. This multilingual support makes it possible to accommodate users who speak different languages.

[0097] The request understanding unit uses the emotion estimation function to analyze the emotions of the user when inputting a request in real time, and can conduct a dialogue that elicits positive emotions. For example, when a user inputs a request, the emotion estimation function is used to analyze the emotions in real time. For example, if the user is feeling anxious, the conversational AI will conduct positive dialogue to give them a sense of security. The request understanding unit also uses the emotion estimation function to analyze the emotions of the user when inputting a request in real time, and conduct a dialogue that elicits positive emotions. For example, if the user is excited, the dialogue will further enhance that emotion. The request understanding unit also uses the emotion estimation function to analyze the emotions of the user when inputting a request in real time, and conduct a dialogue that elicits positive emotions. For example, if the user is calm, the dialogue will maintain that emotion. This allows for better dialogue by analyzing the user's emotions in real time and eliciting positive emotions.

[0098] The house creation unit, when creating a house based on the user's requests, incorporates architectural expertise into the design, allowing for the creation of a more realistic house. For example, when the generation AI creates a house based on the user's requests, it incorporates architectural expertise into the design. For example, it creates a design based on the Building Standards Act, creating a more realistic house. Furthermore, when the generation AI creates a house based on the user's requests, it incorporates architectural expertise into the design. For example, it creates a design that takes earthquake resistance and insulation into consideration, creating a more realistic house. Furthermore, when the generation AI creates a house based on the user's requests, it incorporates architectural expertise into the design. For example, it creates a design that takes energy efficiency into consideration, creating a more realistic house. In this way, by incorporating architectural expertise, it becomes possible to create a more realistic house.

[0099] The house creation unit can take environmental factors into consideration when creating a house that reflects the user's requests. For example, when the generation AI creates a house that reflects the user's requests, it takes environmental factors into consideration when designing. For example, it designs a sunny living room. The house creation unit also takes environmental factors into consideration when creating a house that reflects the user's requests. For example, it designs the placement of windows that allow for good ventilation. The house creation unit also takes environmental factors into consideration when creating a house that reflects the user's requests. For example, it designs large windows to let in natural light. This makes it possible to create a more comfortable house by taking environmental factors into consideration.

[0100] The house creation unit fine-tunes the house design based on the user's emotion estimation result, thereby providing a design that is more satisfying to the user. For example, the house design created by the generation AI is fine-tuned based on the user's emotion estimation result. For example, if the user is not satisfied, the design is changed based on that emotion. The house creation unit also fine-tunes the house design created by the generation AI based on the user's emotion estimation result. For example, if the user is excited, the design is changed based on that emotion. The house creation unit also fine-tunes the house design created by the generation AI based on the user's emotion estimation result. For example, if the user is feeling anxious, the design is changed based on that emotion. In this way, by fine-tuning the design based on the user's emotion estimation result, it is possible to provide a house that is more satisfying to the user.

[0101] The house creation unit can add a function that allows different architectural styles to be selected. For example, a function is added that allows different architectural styles to be selected when the generation AI creates a house. For example, a Japanese-style house is selected. The house creation unit also adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a Western-style house is selected. The house creation unit also adds a function that allows different architectural styles to be selected when the generation AI creates a house. For example, a modern house is selected. This makes it possible to respond to the diverse needs of users by allowing them to select different architectural styles.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The request understanding unit understands the user's request. For example, if the user requests, "I want the living room to be bigger," the request understanding unit analyzes that request. The request understanding unit also receives the user's request in natural language and understands its content. For example, it receives a specific request such as, "I want the windows to be bigger so that more light can come in." Step 2: The house creation unit creates a house based on the request understood by the request understanding unit. For example, if the user requests that the living room be made larger, the house creation unit creates a house with a larger living room. The house creation unit also generates a blueprint that reflects the user's request and builds the house based on that blueprint. Step 3: The feedback collection unit collects user feedback on the house created by the house creation unit. For example, the user may provide specific feedback such as, "The living room is now larger, but I wish the ceiling was a little higher." The feedback collection unit analyzes this feedback and reflects it in the next sample house.

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

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 request understanding unit that understands a user's request; a house creation unit that creates a house based on the request understood by the request understanding unit; a feedback collection unit that collects user feedback on the house created by the house creation unit. A system characterized by:

2. The request understanding unit Analyzing the user's tone of voice or speaking style to understand the user's emotional state and extract requests based on the emotions.

2. The system of claim 1.

3. The request understanding unit The system learns the user's past request history, predicts the user's preferences and tendencies, and makes suggestions in advance to meet the user's requests.

2. The system of claim 1.

4. The request understanding unit Taking into account background information about the user's lifestyle or family structure, we can make more personalized suggestions.

2. The system of claim 1.

5. The request understanding unit When understanding the user's needs, add a function to visually confirm the needs using images or videos.

2. The system of claim 1.

Images