system

Abstract

The system according to the embodiment aims to create floor plans and image images that quickly and accurately reflect customer requests. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a creation unit, and a generation unit. The reception unit receives customer requests. The analysis unit analyzes the requests received by the reception unit. The creation unit creates a floor plan based on the requests analyzed by the analysis unit. The creation unit generates an image or 3D perspective drawing based on the floor plan created by the creation unit.

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, it is difficult to quickly and accurately create floor plans and image images that reflect customer requests, and there is room for improvement.

[0005] The system according to the embodiment aims to create floor plans and image images that quickly and accurately reflect customer requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a creation unit, and a generation unit. The reception unit receives customer requests. The analysis unit analyzes the requests received by the reception unit. The creation unit creates a floor plan based on the requests analyzed by the analysis unit. The creation unit generates an image or a 3D perspective drawing based on the floor plan created by the creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately create floor plans and image images that reflect customer requests. [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 home design support system according to an embodiment of the present invention creates floor plans, conceptual images, and 3D perspective drawings that quickly and accurately reflect customer requests. In this system, customers input their requests by uploading text and images. A generation AI analyzes the requests and creates floor plans based on architectural rules. The generation AI then generates conceptual images and 3D perspective drawings based on the created floor plan. For example, a customer may enter requests such as "I want a larger living room" or "I want a larger south-facing window" in text or upload reference images. This information is input into the generation AI. The generation AI then analyzes the input requests. The generation AI, having learned architectural rules, creates an optimal floor plan based on the customer's requests. For example, to make the living room larger, it may adjust the layout of other rooms or change the position of a wall to enlarge a south-facing window. The generation AI then generates conceptual images and 3D perspective drawings based on the created floor plan. For example, it generates conceptual images that reflect the living room's size and window size, and 3D perspective drawings that show the room layout in three dimensions. This allows customers to visually confirm how their requests are reflected in the design. This system allows customers' requests to be reflected quickly and with high accuracy. Customers can visualize their ideal home in concrete terms, increasing satisfaction. Manufacturers can also efficiently reflect customer requests, reducing the burden of design work. For example, the generation AI automatically creates floor plans and image images, tasks that designers previously had to do manually, significantly reducing work time. As a result, the home design support system can quickly and accurately reflect customer requests, improving customer satisfaction and reducing the burden on manufacturers.

[0029] A home design support system according to an embodiment includes a reception unit, an analysis unit, a creation unit, and a generation unit. The reception unit receives customer requests. Customer requests may include, but are not limited to, text, images, and audio. For example, the reception unit allows customers to input their requests by uploading text or images. The analysis unit analyzes the requests received by the reception unit. The analysis unit analyzes the requests using, for example, natural language processing, image analysis, or machine learning algorithms. The analysis unit learns architectural rules and creates an optimal floor plan based on the customer's requests. For example, the analysis unit may adjust the layout of other rooms to make the living room larger or change the position of a wall to enlarge a south-facing window. The creation unit creates the floor plan based on the requests analyzed by the analysis unit. The creation unit creates the floor plan taking into account, for example, the layout, area, and purpose of the rooms. The creation unit generates an image or a 3D perspective based on the floor plan created by the creation unit. For example, the generation unit generates the image or the 3D perspective using rendering technology. The generation unit generates an image that reflects the size of the living room and windows, and a 3D perspective that shows the layout of the rooms in three dimensions. As a result, the home design support system according to the embodiment can quickly and accurately reflect customer requests, improve customer satisfaction, and reduce the burden on manufacturers.

[0030] The reception unit allows a customer to input their requests by uploading text or images. For example, the reception unit allows a customer to input their requests by uploading text or images. For example, a customer may input requests such as "I want to make the living room bigger" or "I want to make the south-facing window bigger" in text or upload reference images. This allows the customer to input their requests using text or images. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the text or images uploaded by the customer into a generation AI and have the generation AI analyze the requests.

[0031] The analysis unit learns architectural rules and can create an appropriate floor plan based on the customer's requests. The analysis unit analyzes the requests using, for example, natural language processing, image analysis, machine learning algorithms, etc. For example, the analysis unit adjusts the layout of other rooms to make the living room larger, or changes the position of a wall to enlarge a south-facing window. This allows the creation of an optimal floor plan based on the architectural rules. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's requests into a generation AI and have the generation AI create a floor plan.

[0032] The generation unit can generate an image image or a 3D perspective based on the created floor plan. The generation unit generates the image image or the 3D perspective using, for example, rendering technology. For example, the generation unit generates an image image that reflects the size of the living room and the size of the windows, or a 3D perspective that three-dimensionally represents the layout of the rooms. This allows the generation of an image image or a 3D perspective based on the created floor plan. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the created floor plan data into a generation AI and cause the generation AI to generate an image image or a 3D perspective.

[0033] The generation unit can generate an image that reflects the area of ​​the living room or the dimensions of the windows. The generation unit generates an image that reflects, for example, the size of the living room or the size of the windows. For example, the generation unit can also generate an image that reflects the size of the living room. The generation unit can also generate an image that reflects the size of the windows. This makes it possible to generate an image that reflects the size of the living room or the size of the windows. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the size of the living room or the size of the windows into the generation AI and cause the generation AI to generate an image.

[0034] The generation unit can generate a 3D perspective that three-dimensionally represents the layout of the rooms. The generation unit generates, for example, a 3D perspective that three-dimensionally represents the layout of the rooms. For example, the generation unit can also generate a 3D perspective that three-dimensionally represents the layout of the rooms. The generation unit can also generate a 3D perspective that three-dimensionally represents the layout of the rooms. In this way, a 3D perspective that three-dimensionally represents the layout of the rooms can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input room layout data to a generation AI and cause the generation AI to generate a 3D perspective.

[0035] The reception unit can analyze the customer's past request history and select an appropriate reception method. For example, the reception unit can prioritize suggesting reception methods (text, image, etc.) that the customer has frequently used in the past. For example, the reception unit can also prioritize suggesting reception methods (text, image, etc.) that the customer has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the customer's past request history. The reception unit can also analyze the customer's past request history and select the most efficient reception method. This makes it possible to select the optimal reception method based on the customer's past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's past request history data into the generation AI and have the generation AI select the optimal reception method.

[0036] When receiving a request, the reception unit can filter the requests based on the customer's current project or area of ​​interest. For example, the reception unit can prioritize receiving requests related to a project currently in progress. For example, the reception unit can also prioritize receiving requests related to a project currently in progress. The reception unit can also filter and receive related requests based on the customer's area of ​​interest. The reception unit can also receive the most appropriate request depending on the progress of the customer's current project. This makes it possible to filter requests based on the customer's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input customer project data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0037] When receiving a request, the reception unit can prioritize receiving highly relevant requests based on the customer's geographical location information. For example, the reception unit can prioritize receiving requests related to projects close to the customer's current location. For example, the reception unit can also prioritize receiving requests related to projects close to the customer's current location. The reception unit can also filter and accept related requests based on the customer's geographical location information. The reception unit can also accept the most appropriate request taking into account the weather and traffic conditions in the customer's current location. This allows highly relevant requests to be preferentially accepted based on the customer's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's geographical location information data into the generation AI and cause the generation AI to select highly relevant requests.

[0038] The reception unit can analyze the customer's online activities when receiving a request and receive related requests. For example, the reception unit can prioritize receiving requests related to current interests from the customer's social media activities. For example, the reception unit can prioritize receiving requests related to current interests from the customer's social media activities. The reception unit can also analyze the content of the customer's social media posts and filter and receive related requests. The reception unit can also accept optimal requests based on the frequency of the customer's social media activities. This makes it possible to receive related requests based on the customer's social media activities. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the customer's online activity data into a generation AI and cause the generation AI to select related requests.

[0039] The analysis unit can adjust the accuracy of the analysis based on the importance of the request during analysis. For example, the analysis unit performs a detailed analysis on a request with a high level of importance. For example, the analysis unit can also perform a detailed analysis on a request with a high level of importance. The analysis unit can also perform a simplified analysis on a request with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the request. This makes it possible to adjust the level of detail of the analysis according to the importance of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input request importance data to the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the classification of the request. For example, the analysis unit can apply an analysis algorithm specialized for architectural design to a request regarding a floor plan. For example, the analysis unit can apply an analysis algorithm specialized for architectural design to a request regarding a floor plan. The analysis unit can also apply an analysis algorithm specialized for design to a request regarding interior design. The analysis unit can also apply an analysis algorithm specialized for ecology to a request regarding the environment. This makes it possible to apply the optimal analysis algorithm depending on the category of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request classification data into the generation AI and cause the generation AI to select an analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the date and time of request submission. The analysis unit can determine the priority of analysis based on, for example, when the request was submitted. For example, the analysis unit can also determine the priority of analysis based on when the request was submitted. The analysis unit can also prioritize analysis of requests that were submitted earlier. The analysis unit can also quickly analyze requests that were submitted more recently. This makes it possible to determine the priority of analysis based on when the request was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request submission date and time data into the generation AI and have the generation AI determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of requests during analysis. The analysis unit, for example, prioritizes analysis of highly relevant requests. For example, the analysis unit can also prioritize analysis of highly relevant requests. The analysis unit can also postpone analysis of less relevant requests. The analysis unit can also dynamically adjust the order of analysis according to the relevance of requests. This makes it possible to adjust the order of analysis based on the relevance of requests. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input request relevance data into the generation AI and cause the generation AI to adjust the order of analysis.

[0043] When creating a floor plan, the creation unit can create an appropriate floor plan based on the customer's past requests. The creation unit, for example, creates an optimal floor plan based on requests previously submitted by the customer. For example, the creation unit can also create an optimal floor plan based on requests previously submitted by the customer. The creation unit can also create the most efficient floor plan from the customer's past request history. The creation unit can also analyze the customer's past requests and propose an optimal floor plan. This makes it possible to create an optimal floor plan based on the customer's past requests. Some or all of the above-mentioned processing in the creation unit may be performed using AI, for example, or may be performed without using AI. For example, the creation unit can input the customer's past request data into the generation AI and cause the generation AI to create the floor plan.

[0044] When creating a floor plan, the creation unit can customize the floor plan based on the customer's current living environment. The creation unit, for example, creates an optimal floor plan based on the customer's current family composition. For example, the creation unit can also create an optimal floor plan based on the customer's current family composition. The creation unit can also customize the floor plan based on the customer's current lifestyle. The creation unit can also propose an optimal floor plan taking into account the customer's current living situation. This makes it possible to customize an optimal floor plan based on the customer's current living situation. Some or all of the above-described processing in the creation unit may be performed using AI, for example, or may be performed without using AI. For example, the creation unit can input customer living environment data into the generation AI and cause the generation AI to customize the floor plan.

[0045] When creating a floor plan, the creation unit can create an appropriate floor plan based on the customer's geographical location information. The creation unit can, for example, create an optimal floor plan by taking into account the climatic conditions of the customer's current location. For example, the creation unit can also create an optimal floor plan by taking into account the climatic conditions of the customer's current location. The creation unit can also propose an optimal floor plan based on the customer's geographical location information. The creation unit can also create an optimal floor plan by taking into account the environmental conditions of the customer's current location. This allows the creation of an optimal floor plan based on the customer's geographical location information. Some or all of the above-described processing in the creation unit may be performed using, or without, AI, for example. For example, the creation unit can input the customer's geographical location information data into the generation AI and cause the generation AI to create a floor plan.

[0046] When creating a floor plan, the creation unit can analyze the customer's online activity and propose a floor plan. For example, the creation unit can propose a floor plan related to the customer's current interests based on the customer's social media activity. For example, the creation unit can propose a floor plan related to the customer's current interests based on the customer's social media activity. The creation unit can also analyze the customer's social media posts and propose a related floor plan. The creation unit can also propose an optimal floor plan based on the frequency of the customer's social media activity. This makes it possible to propose an optimal floor plan based on the customer's social media activity. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, AI. For example, the creation unit can input the customer's online activity data into a generation AI and cause the generation AI to propose a floor plan.

[0047] When generating an image or 3D perspective, the generation unit can generate an appropriate image or perspective based on the customer's past requests. The generation unit, for example, generates an optimal image based on requests previously submitted by the customer. For example, the generation unit can generate an optimal image based on requests previously submitted by the customer. The generation unit can also generate the most efficient image based on the customer's past request history. The generation unit can also analyze the customer's past requests and suggest the optimal image. This makes it possible to generate an optimal image or 3D perspective based on the customer's past requests. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the customer's past request data into the generation AI and cause the generation AI to generate an image or 3D perspective.

[0048] When generating an image or 3D perspective, the generation unit can customize the image or perspective based on the customer's current living environment. The generation unit generates an optimal image based on, for example, the customer's current family composition. For example, the generation unit can generate an optimal image based on the customer's current family composition. The generation unit can also customize the image based on the customer's current lifestyle. The generation unit can also suggest an optimal image taking into account the customer's current living situation. This makes it possible to customize an optimal image or 3D perspective based on the customer's current living situation. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer living environment data into the generation AI and cause the generation AI to customize the image or 3D perspective.

[0049] When generating an image or 3D perspective, the generation unit can generate an appropriate image or perspective based on the customer's geographical location information. The generation unit, for example, generates an optimal image by taking into account the climatic conditions of the customer's current location. For example, the generation unit can generate an optimal image by taking into account the climatic conditions of the customer's current location. The generation unit can also suggest an optimal image based on the customer's geographical location information. The generation unit can also generate an optimal image by taking into account the environmental conditions of the customer's current location. This makes it possible to generate an optimal image or 3D perspective based on the customer's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the customer's geographical location information data into the generation AI and cause the generation AI to generate an image or 3D perspective.

[0050] When generating images and 3D perspective drawings, the generation unit can analyze the customer's online activities to suggest images and perspective drawings. For example, the generation unit can suggest images related to current interests based on the customer's social media activities. For example, the generation unit can suggest images related to current interests based on the customer's social media activities. The generation unit can also analyze the customer's social media posts and suggest related images. The generation unit can also suggest optimal images based on the frequency of the customer's social media activities. This makes it possible to suggest optimal images and 3D perspective drawings based on the customer's social media activities. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's online activity data into the generation AI and cause the generation AI to suggest images and 3D perspective drawings.

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

[0052] When analyzing a customer's requests, the analysis unit can refer to the customer's past request history and evaluate the relationship between past and current requests. For example, if a customer who previously placed importance on the size of the living room again requests something related to the living room, that request can be analyzed as a priority. Also, if a customer who previously preferred a specific design style requests a new request, that style can be taken into consideration in the analysis. Furthermore, the analysis unit can learn the customer's preferences and tendencies from past request history and predict future requests and reflect them in the analysis. This makes it possible to create floor plans that more accurately reflect the customer's consistent requests.

[0053] The reception unit can provide real-time feedback when a customer enters their requests. For example, when a customer enters "I want a larger living room," a preview of how that request will be reflected can be displayed immediately. Also, when a customer uploads an image, it can visually show how that image will affect the floor plan. Furthermore, as the customer enters their request, the system can automatically make suggestions to help them realize their request. This allows customers to enter more specific requests while checking in real time how their request will be reflected.

[0054] The generation unit can take the customer's current living environment into consideration when generating image images and 3D perspectives based on the created floor plan. For example, if a customer has a pet, space for the pet can be reflected in the floor plan and displayed in the image images and 3D perspectives. Also, if the customer has specific furniture, it can arrange that furniture in the floor plan so that it can be visually confirmed. Furthermore, it can simulate the usability of the floor plan based on the customer's lifestyle and propose optimal layouts. This allows the system to provide a floor plan that is in line with the customer's actual living environment, creating a more realistic image.

[0055] When receiving a customer's request, the reception department can provide appropriate advice based on the content of the request. For example, if a customer requests a larger living room, the system can propose the optimal layout and design for that request. Similarly, if a customer requests a larger south-facing window, the system can explain the architectural constraints and advantages and disadvantages of that request. Furthermore, when a customer enters their request, the system can automatically present related information and reference examples to help them make their request a reality. This allows customers to get a more concrete image of their request and create a floor plan that satisfies them.

[0056] When analyzing customer requests, the analysis unit can apply different analysis algorithms based on the content of the request. For example, an algorithm specialized in architectural design can be applied to requests regarding floor plans, and an algorithm specialized in design can be applied to requests regarding interior design. Also, an algorithm specialized in ecology can be applied to requests regarding the environment. Furthermore, the optimal analysis method can be selected depending on the category of request, allowing for more accurate analysis. This makes it possible to perform the optimal analysis for customer requests and provide floor plans that will provide greater satisfaction.

[0057] When analyzing customer requests, the analysis unit can determine the priority of analysis based on the date and time of request submission. For example, the priority of analysis can be determined based on when the request was submitted. It is also possible to prioritize analysis of requests that were submitted earlier. It is also possible to quickly analyze requests that were submitted more recently. This allows the analysis priority to be determined based on when the request was submitted, making it possible to respond to customer requests quickly and appropriately.

[0058] The generation unit can take into account the customer's geographical location information when generating image images and 3D perspectives based on the created floor plan. For example, it can generate an optimal image image taking into account the climatic conditions of the customer's current location. It can also propose an optimal image image based on the customer's geographical location information. It can also generate an optimal image image taking into account the environmental conditions of the customer's current location. This makes it possible to generate optimal image images and 3D perspectives based on the customer's geographical location information, providing a more realistic image.

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

[0060] Step 1: The reception unit receives a customer request. The customer request may include, but is not limited to, text, images, and voice. For example, the reception unit allows the customer to input the request by uploading text or images. Step 2: The analysis unit analyzes the request received by the reception unit. The analysis unit analyzes the request using, for example, natural language processing, image analysis, or machine learning algorithms. The analysis unit learns architectural rules and creates the optimal floor plan based on the customer's request. For example, the analysis unit adjusts the layout of other rooms to make the living room larger, or changes the position of a wall to make a south-facing window larger. Step 3: The creation unit creates a floor plan based on the needs analyzed by the analysis unit. The creation unit creates the floor plan taking into consideration, for example, the layout, area, and use of rooms. Step 4: The generation unit generates an image or 3D perspective based on the floor plan created by the creation unit. The generation unit generates the image or 3D perspective using, for example, rendering technology. The generation unit generates an image that reflects the size of the living room and the size of the windows, and a 3D perspective that three-dimensionally represents the layout of the rooms.

[0061] (Example 2) A home design support system according to an embodiment of the present invention creates floor plans, conceptual images, and 3D perspective drawings that quickly and accurately reflect customer requests. In this system, customers input their requests by uploading text and images. A generation AI analyzes the requests and creates floor plans based on architectural rules. The generation AI then generates conceptual images and 3D perspective drawings based on the created floor plan. For example, a customer may enter requests such as "I want a larger living room" or "I want a larger south-facing window" in text or upload reference images. This information is input into the generation AI. The generation AI then analyzes the input requests. The generation AI, having learned architectural rules, creates an optimal floor plan based on the customer's requests. For example, to make the living room larger, it may adjust the layout of other rooms or change the position of a wall to enlarge a south-facing window. The generation AI then generates conceptual images and 3D perspective drawings based on the created floor plan. For example, it generates conceptual images that reflect the living room's size and window size, and 3D perspective drawings that show the room layout in three dimensions. This allows customers to visually confirm how their requests are reflected in the design. This system allows customers' requests to be reflected quickly and with high accuracy. Customers can visualize their ideal home in concrete terms, increasing satisfaction. Manufacturers can also efficiently reflect customer requests, reducing the burden of design work. For example, the generation AI automatically creates floor plans and image images, tasks that designers previously had to do manually, significantly reducing work time. As a result, the home design support system can quickly and accurately reflect customer requests, improving customer satisfaction and reducing the burden on manufacturers.

[0062] A home design support system according to an embodiment includes a reception unit, an analysis unit, a creation unit, and a generation unit. The reception unit receives customer requests. Customer requests may include, but are not limited to, text, images, and audio. For example, the reception unit allows customers to input their requests by uploading text or images. The analysis unit analyzes the requests received by the reception unit. The analysis unit analyzes the requests using, for example, natural language processing, image analysis, or machine learning algorithms. The analysis unit learns architectural rules and creates an optimal floor plan based on the customer's requests. For example, the analysis unit may adjust the layout of other rooms to make the living room larger or change the position of a wall to enlarge a south-facing window. The creation unit creates the floor plan based on the requests analyzed by the analysis unit. The creation unit creates the floor plan taking into account, for example, the layout, area, and purpose of the rooms. The creation unit generates an image or a 3D perspective based on the floor plan created by the creation unit. For example, the generation unit generates the image or the 3D perspective using rendering technology. The generation unit generates an image that reflects the size of the living room and windows, and a 3D perspective that shows the layout of the rooms in three dimensions. As a result, the home design support system according to the embodiment can quickly and accurately reflect customer requests, improve customer satisfaction, and reduce the burden on manufacturers.

[0063] The reception unit allows a customer to input their requests by uploading text or images. For example, the reception unit allows a customer to input their requests by uploading text or images. For example, a customer may input requests such as "I want to make the living room bigger" or "I want to make the south-facing window bigger" in text or upload reference images. This allows the customer to input their requests using text or images. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the text or images uploaded by the customer into a generation AI and have the generation AI analyze the requests.

[0064] The analysis unit learns architectural rules and can create an appropriate floor plan based on the customer's requests. The analysis unit analyzes the requests using, for example, natural language processing, image analysis, machine learning algorithms, etc. For example, the analysis unit adjusts the layout of other rooms to make the living room larger, or changes the position of a wall to enlarge a south-facing window. This allows the creation of an optimal floor plan based on the architectural rules. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's requests into a generation AI and have the generation AI create a floor plan.

[0065] The generation unit can generate an image image or a 3D perspective based on the created floor plan. The generation unit generates the image image or the 3D perspective using, for example, rendering technology. For example, the generation unit generates an image image that reflects the size of the living room and the size of the windows, or a 3D perspective that three-dimensionally represents the layout of the rooms. This allows the generation of an image image or a 3D perspective based on the created floor plan. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the created floor plan data into a generation AI and cause the generation AI to generate an image image or a 3D perspective.

[0066] The generation unit can generate an image that reflects the area of ​​the living room or the dimensions of the windows. The generation unit generates an image that reflects, for example, the size of the living room or the size of the windows. For example, the generation unit can also generate an image that reflects the size of the living room. The generation unit can also generate an image that reflects the size of the windows. This makes it possible to generate an image that reflects the size of the living room or the size of the windows. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the size of the living room or the size of the windows into the generation AI and cause the generation AI to generate an image.

[0067] The generation unit can generate a 3D perspective that three-dimensionally represents the layout of the rooms. The generation unit generates, for example, a 3D perspective that three-dimensionally represents the layout of the rooms. For example, the generation unit can also generate a 3D perspective that three-dimensionally represents the layout of the rooms. The generation unit can also generate a 3D perspective that three-dimensionally represents the layout of the rooms. In this way, a 3D perspective that three-dimensionally represents the layout of the rooms can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input room layout data to a generation AI and cause the generation AI to generate a 3D perspective.

[0068] The reception unit can estimate the customer's emotions and adjust the timing of request acceptance based on the estimated customer emotions. For example, if a customer is feeling stressed, the reception unit can delay the timing of request acceptance so that the customer can enter the request in a relaxed state. For example, if a customer is feeling stressed, the reception unit can delay the timing of request acceptance so that the customer can enter the request in a relaxed state. Furthermore, if a customer is excited, the reception unit can immediately accept the request and respond quickly. Furthermore, if a customer is tired, the reception unit can adjust the timing of request acceptance so that the customer can enter the request after resting. This allows the timing of request acceptance to be adjusted according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input customer emotion data into the generation AI and have the generation AI execute emotion estimation.

[0069] The reception unit can analyze the customer's past request history and select an appropriate reception method. For example, the reception unit can prioritize suggesting reception methods (text, image, etc.) that the customer has frequently used in the past. For example, the reception unit can also prioritize suggesting reception methods (text, image, etc.) that the customer has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the customer's past request history. The reception unit can also analyze the customer's past request history and select the most efficient reception method. This makes it possible to select the optimal reception method based on the customer's past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's past request history data into the generation AI and have the generation AI select the optimal reception method.

[0070] When receiving a request, the reception unit can filter the requests based on the customer's current project or area of ​​interest. For example, the reception unit can prioritize receiving requests related to a project currently in progress. For example, the reception unit can also prioritize receiving requests related to a project currently in progress. The reception unit can also filter and receive related requests based on the customer's area of ​​interest. The reception unit can also receive the most appropriate request depending on the progress of the customer's current project. This makes it possible to filter requests based on the customer's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input customer project data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0071] The reception unit can estimate the customer's emotions and determine the priority of requests to be received based on the estimated customer emotions. For example, when a customer is stressed, the reception unit can prioritize requests of higher importance. For example, when a customer is stressed, the reception unit can also prioritize requests of higher importance. Furthermore, when a customer is relaxed, the reception unit can prioritize requests that require a quick response. Furthermore, when a customer is in a hurry, the reception unit can prioritize requests that require a quick response. This allows the priority of requests to be determined based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input customer emotion data into the generation AI and have the generation AI determine the priority of requests.

[0072] When receiving a request, the reception unit can prioritize receiving highly relevant requests based on the customer's geographical location information. For example, the reception unit can prioritize receiving requests related to projects close to the customer's current location. For example, the reception unit can also prioritize receiving requests related to projects close to the customer's current location. The reception unit can also filter and accept related requests based on the customer's geographical location information. The reception unit can also accept the most appropriate request taking into account the weather and traffic conditions in the customer's current location. This allows highly relevant requests to be preferentially accepted based on the customer's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's geographical location information data into the generation AI and cause the generation AI to select highly relevant requests.

[0073] The reception unit can analyze the customer's online activities when receiving a request and receive related requests. For example, the reception unit can prioritize receiving requests related to current interests from the customer's social media activities. For example, the reception unit can prioritize receiving requests related to current interests from the customer's social media activities. The reception unit can also analyze the content of the customer's social media posts and filter and receive related requests. The reception unit can also accept optimal requests based on the frequency of the customer's social media activities. This makes it possible to receive related requests based on the customer's social media activities. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the customer's online activity data into a generation AI and cause the generation AI to select related requests.

[0074] The analysis unit can estimate the customer's emotions and adjust the way the analysis is presented based on the estimated customer emotions. For example, if the customer is feeling stressed, the analysis unit can provide a simple, highly visible analysis result. For example, if the customer is feeling stressed, the analysis unit can provide a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the customer is relaxed. The analysis unit can also provide a concise analysis result if the customer is in a hurry. This allows the way the analysis is presented to be adjusted according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can input customer emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0075] The analysis unit can adjust the accuracy of the analysis based on the importance of the request during analysis. For example, the analysis unit performs a detailed analysis on a request with a high level of importance. For example, the analysis unit can also perform a detailed analysis on a request with a high level of importance. The analysis unit can also perform a simplified analysis on a request with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the request. This makes it possible to adjust the level of detail of the analysis according to the importance of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input request importance data to the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the classification of the request. For example, the analysis unit can apply an analysis algorithm specialized for architectural design to a request regarding a floor plan. For example, the analysis unit can apply an analysis algorithm specialized for architectural design to a request regarding a floor plan. The analysis unit can also apply an analysis algorithm specialized for design to a request regarding interior design. The analysis unit can also apply an analysis algorithm specialized for ecology to a request regarding the environment. This makes it possible to apply the optimal analysis algorithm depending on the category of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request classification data into the generation AI and cause the generation AI to select an analysis algorithm.

[0077] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. For example, if the customer is in a hurry, the analysis unit can perform a short, concise analysis. For example, if the customer is in a hurry, the analysis unit can perform a short, concise analysis. Furthermore, if the customer is relaxed, the analysis unit can perform a longer analysis including detailed explanations. Furthermore, if the customer is excited, the analysis unit can perform an analysis with visually stimulating effects. This allows the length of the analysis to be adjusted according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input customer emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0078] During analysis, the analysis unit can determine the priority of analysis based on the date and time of request submission. The analysis unit can determine the priority of analysis based on, for example, when the request was submitted. For example, the analysis unit can also determine the priority of analysis based on when the request was submitted. The analysis unit can also prioritize analysis of requests that were submitted earlier. The analysis unit can also quickly analyze requests that were submitted more recently. This makes it possible to determine the priority of analysis based on when the request was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request submission date and time data into the generation AI and have the generation AI determine the priority of analysis.

[0079] The analysis unit can adjust the order of analysis based on the relevance of requests during analysis. The analysis unit, for example, prioritizes analysis of highly relevant requests. For example, the analysis unit can also prioritize analysis of highly relevant requests. The analysis unit can also postpone analysis of less relevant requests. The analysis unit can also dynamically adjust the order of analysis according to the relevance of requests. This makes it possible to adjust the order of analysis based on the relevance of requests. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input request relevance data into the generation AI and cause the generation AI to adjust the order of analysis.

[0080] The creation unit can estimate the customer's emotions and adjust the floor plan creation method based on the estimated customer emotions. For example, if the customer is feeling stressed, the creation unit can create a simple, highly visible floor plan. For example, if the customer is feeling stressed, the creation unit can also create a simple, highly visible floor plan. Furthermore, if the customer is relaxed, the creation unit can create a detailed floor plan. Furthermore, if the customer is in a hurry, the creation unit can create a floor plan that focuses on the main points. This allows the floor plan creation method to be adjusted according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the creation unit may be performed using AI, or may be performed without AI. For example, the creation unit can input customer emotion data into the generation AI and cause the generation AI to adjust the floor plan creation method.

[0081] When creating a floor plan, the creation unit can create an appropriate floor plan based on the customer's past requests. The creation unit, for example, creates an optimal floor plan based on requests previously submitted by the customer. For example, the creation unit can also create an optimal floor plan based on requests previously submitted by the customer. The creation unit can also create the most efficient floor plan from the customer's past request history. The creation unit can also analyze the customer's past requests and propose an optimal floor plan. This makes it possible to create an optimal floor plan based on the customer's past requests. Some or all of the above-mentioned processing in the creation unit may be performed using AI, for example, or may be performed without using AI. For example, the creation unit can input the customer's past request data into the generation AI and cause the generation AI to create the floor plan.

[0082] When creating a floor plan, the creation unit can customize the floor plan based on the customer's current living environment. The creation unit, for example, creates an optimal floor plan based on the customer's current family composition. For example, the creation unit can also create an optimal floor plan based on the customer's current family composition. The creation unit can also customize the floor plan based on the customer's current lifestyle. The creation unit can also propose an optimal floor plan taking into account the customer's current living situation. This makes it possible to customize an optimal floor plan based on the customer's current living situation. Some or all of the above-described processing in the creation unit may be performed using AI, for example, or may be performed without using AI. For example, the creation unit can input customer living environment data into the generation AI and cause the generation AI to customize the floor plan.

[0083] The creation unit can estimate the customer's emotions and prioritize floor plans based on the estimated customer emotions. For example, if the customer is feeling stressed, the creation unit prioritizes creating floor plans with high importance. For example, if the customer is feeling stressed, the creation unit can also prioritize creating floor plans with high importance. Furthermore, if the customer is feeling relaxed, the creation unit can prioritize creating detailed floor plans. Furthermore, if the customer is in a hurry, the creation unit can prioritize creating floor plans that require quick response. This allows floor plan prioritization to be determined according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input customer emotion data into the generation AI and have the generation AI determine the floor plan priorities.

[0084] When creating a floor plan, the creation unit can create an appropriate floor plan based on the customer's geographical location information. The creation unit can, for example, create an optimal floor plan by taking into account the climatic conditions of the customer's current location. For example, the creation unit can also create an optimal floor plan by taking into account the climatic conditions of the customer's current location. The creation unit can also propose an optimal floor plan based on the customer's geographical location information. The creation unit can also create an optimal floor plan by taking into account the environmental conditions of the customer's current location. This allows the creation of an optimal floor plan based on the customer's geographical location information. Some or all of the above-described processing in the creation unit may be performed using, or without, AI, for example. For example, the creation unit can input the customer's geographical location information data into the generation AI and cause the generation AI to create a floor plan.

[0085] When creating a floor plan, the creation unit can analyze the customer's online activity and propose a floor plan. For example, the creation unit can propose a floor plan related to the customer's current interests based on the customer's social media activity. For example, the creation unit can propose a floor plan related to the customer's current interests based on the customer's social media activity. The creation unit can also analyze the customer's social media posts and propose a related floor plan. The creation unit can also propose an optimal floor plan based on the frequency of the customer's social media activity. This makes it possible to propose an optimal floor plan based on the customer's social media activity. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, AI. For example, the creation unit can input the customer's online activity data into a generation AI and cause the generation AI to propose a floor plan.

[0086] The generation unit can estimate the customer's emotions and adjust the image and 3D perspective generation method based on the estimated customer emotions. For example, if the customer is feeling stressed, the generation unit can generate a simple, highly visible image. For example, if the customer is feeling stressed, the generation unit can generate a simple, highly visible image. For example, if the customer is feeling stressed, the generation unit can generate a detailed image. For example, if the customer is relaxed, the generation unit can generate an image that focuses on the main points if the customer is in a hurry. This allows the image and 3D perspective generation method to be adjusted according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input customer emotion data into the generation AI and cause the generation AI to adjust the image and 3D perspective generation method.

[0087] When generating an image or 3D perspective, the generation unit can generate an appropriate image or perspective based on the customer's past requests. The generation unit, for example, generates an optimal image based on requests previously submitted by the customer. For example, the generation unit can generate an optimal image based on requests previously submitted by the customer. The generation unit can also generate the most efficient image based on the customer's past request history. The generation unit can also analyze the customer's past requests and suggest the optimal image. This makes it possible to generate an optimal image or 3D perspective based on the customer's past requests. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the customer's past request data into the generation AI and cause the generation AI to generate an image or 3D perspective.

[0088] When generating an image or 3D perspective, the generation unit can customize the image or perspective based on the customer's current living environment. The generation unit generates an optimal image based on, for example, the customer's current family composition. For example, the generation unit can generate an optimal image based on the customer's current family composition. The generation unit can also customize the image based on the customer's current lifestyle. The generation unit can also suggest an optimal image taking into account the customer's current living situation. This makes it possible to customize an optimal image or 3D perspective based on the customer's current living situation. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer living environment data into the generation AI and cause the generation AI to customize the image or 3D perspective.

[0089] The generation unit can estimate the customer's emotions and prioritize images and 3D perspectives based on the estimated customer emotions. For example, if the customer is feeling stressed, the generation unit can prioritize generating images of high importance. For example, if the customer is feeling stressed, the generation unit can also prioritize generating images of high importance. Furthermore, if the customer is feeling relaxed, the generation unit can prioritize generating detailed images. Furthermore, if the customer is in a hurry, the generation unit can prioritize generating images that require a quick response. This allows the prioritization of images and 3D perspectives to be determined according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input customer emotion data into the generation AI and have the generation AI determine the priority of images and 3D perspectives.

[0090] When generating an image or 3D perspective, the generation unit can generate an appropriate image or perspective based on the customer's geographical location information. The generation unit, for example, generates an optimal image by taking into account the climatic conditions of the customer's current location. For example, the generation unit can generate an optimal image by taking into account the climatic conditions of the customer's current location. The generation unit can also suggest an optimal image based on the customer's geographical location information. The generation unit can also generate an optimal image by taking into account the environmental conditions of the customer's current location. This makes it possible to generate an optimal image or 3D perspective based on the customer's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the customer's geographical location information data into the generation AI and cause the generation AI to generate an image or 3D perspective.

[0091] When generating images and 3D perspective drawings, the generation unit can analyze the customer's online activities to suggest images and perspective drawings. For example, the generation unit can suggest images related to current interests based on the customer's social media activities. For example, the generation unit can suggest images related to current interests based on the customer's social media activities. The generation unit can also analyze the customer's social media posts and suggest related images. The generation unit can also suggest optimal images based on the frequency of the customer's social media activities. This makes it possible to suggest optimal images and 3D perspective drawings based on the customer's social media activities. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's online activity data into the generation AI and cause the generation AI to suggest images and 3D perspective drawings. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, creation unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and allows customers to input their requests by uploading text or images. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs natural language processing and image analysis. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a floor plan based on the customer's requests. The generation unit is realized, for example, by the output device 40 of the smart device 14, and generates an image image or a 3D perspective based on the created floor plan. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, creation unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the customer to input their requests by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs natural language processing and image analysis. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a floor plan based on the customer's requests. The generation unit is realized, for example, by the speaker 240 of the smart glasses 214, and generates an image image or a 3D perspective based on the created floor plan. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, creation unit, and generation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, allowing the customer to input their requests by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs natural language processing and image analysis. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a floor plan based on the customer's requests. The generation unit is realized, for example, by the display 343 of the headset terminal 314, and generates an image image or a 3D perspective based on the created floor plan. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, creation unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the customer to input their requests by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs natural language processing and image analysis. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a floor plan based on the customer's requests. The generation unit is realized, for example, by the speaker 240 of the robot 414, and generates an image image or a 3D perspective based on the created floor plan.

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

[0093] When analyzing a customer's requests, the analysis unit can refer to the customer's past request history and evaluate the relationship between past and current requests. For example, if a customer who previously placed importance on the size of the living room again requests something related to the living room, that request can be analyzed as a priority. Also, if a customer who previously preferred a specific design style requests a new request, that style can be taken into consideration in the analysis. Furthermore, the analysis unit can learn the customer's preferences and tendencies from past request history and predict future requests and reflect them in the analysis. This makes it possible to create floor plans that more accurately reflect the customer's consistent requests.

[0094] The reception unit can provide real-time feedback when a customer enters their requests. For example, when a customer enters "I want a larger living room," a preview of how that request will be reflected can be displayed immediately. Also, when a customer uploads an image, it can visually show how that image will affect the floor plan. Furthermore, as the customer enters their request, the system can automatically make suggestions to help them realize their request. This allows customers to enter more specific requests while checking in real time how their request will be reflected.

[0095] When analyzing customer requests, the analysis unit can dynamically adjust the priority of the requests. For example, if a customer submits multiple requests, the order of analysis can be determined based on the importance and urgency of those requests. Also, if a customer has strong feelings about a particular request, it can prioritize the analysis of that request. Furthermore, it can adjust the level of detail of the analysis depending on the content of the request, performing a more detailed analysis for important requests and a simplified analysis for general requests. This makes it possible to perform an optimal analysis of the customer's requests and provide a floor plan that will provide greater satisfaction.

[0096] The generation unit can take the customer's current living environment into consideration when generating image images and 3D perspectives based on the created floor plan. For example, if a customer has a pet, space for the pet can be reflected in the floor plan and displayed in the image images and 3D perspectives. Also, if the customer has specific furniture, it can arrange that furniture in the floor plan so that it can be visually confirmed. Furthermore, it can simulate the usability of the floor plan based on the customer's lifestyle and propose optimal layouts. This allows the system to provide a floor plan that is in line with the customer's actual living environment, creating a more realistic image.

[0097] When receiving a customer's request, the reception department can provide appropriate advice based on the content of the request. For example, if a customer requests a larger living room, the system can propose the optimal layout and design for that request. Similarly, if a customer requests a larger south-facing window, the system can explain the architectural constraints and advantages and disadvantages of that request. Furthermore, when a customer enters their request, the system can automatically present related information and reference examples to help them make their request a reality. This allows customers to get a more concrete image of their request and create a floor plan that satisfies them.

[0098] When analyzing customer requests, the analysis unit can apply different analysis algorithms based on the content of the request. For example, an algorithm specialized in architectural design can be applied to requests regarding floor plans, and an algorithm specialized in design can be applied to requests regarding interior design. Also, an algorithm specialized in ecology can be applied to requests regarding the environment. Furthermore, the optimal analysis method can be selected depending on the category of request, allowing for more accurate analysis. This makes it possible to perform the optimal analysis for customer requests and provide floor plans that will provide greater satisfaction.

[0099] The reception unit can estimate the customer's emotions and adjust the method of receiving requests based on the estimated customer emotions. For example, if the customer is feeling stressed, a simple and easy-to-use interface can be provided to allow the customer to smoothly input their requests. If the customer is relaxed, more options can be provided to allow the customer to input detailed requests. Furthermore, if the customer is excited, an interface incorporating interactive elements can be provided to make inputting requests more enjoyable. In this way, the optimal reception method can be provided according to the customer's emotions, making it easier for the customer to input their requests.

[0100] When analyzing customer requests, the analysis unit can determine the priority of analysis based on the date and time of request submission. For example, the priority of analysis can be determined based on when the request was submitted. It is also possible to prioritize analysis of requests that were submitted earlier. It is also possible to quickly analyze requests that were submitted more recently. This allows the analysis priority to be determined based on when the request was submitted, making it possible to respond to customer requests quickly and appropriately.

[0101] The generation unit can take into account the customer's geographical location information when generating image images and 3D perspectives based on the created floor plan. For example, it can generate an optimal image image taking into account the climatic conditions of the customer's current location. It can also propose an optimal image image based on the customer's geographical location information. It can also generate an optimal image image taking into account the environmental conditions of the customer's current location. This makes it possible to generate optimal image images and 3D perspectives based on the customer's geographical location information, providing a more realistic image.

[0102] The analysis unit can estimate the customer's emotions and adjust the way the analysis is presented based on the estimated customer emotions. For example, if the customer is feeling stressed, it can provide simple, highly visible analysis results. If the customer is relaxed, it can provide detailed analysis results. Furthermore, if the customer is in a hurry, it can provide analysis results that focus on the main points. This allows it to adjust the way the analysis is presented according to the customer's emotions and provide analysis results that are easier to understand.

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

[0104] Step 1: The reception unit receives a customer request. The customer request may include, but is not limited to, text, images, and voice. For example, the reception unit allows the customer to input the request by uploading text or images. Step 2: The analysis unit analyzes the request received by the reception unit. The analysis unit analyzes the request using, for example, natural language processing, image analysis, or machine learning algorithms. The analysis unit learns architectural rules and creates the optimal floor plan based on the customer's request. For example, the analysis unit adjusts the layout of other rooms to make the living room larger, or changes the position of a wall to make a south-facing window larger. Step 3: The creation unit creates a floor plan based on the needs analyzed by the analysis unit. The creation unit creates the floor plan taking into consideration, for example, the layout, area, and use of rooms. Step 4: The generation unit generates an image or 3D perspective based on the floor plan created by the creation unit. The generation unit generates the image or 3D perspective using, for example, rendering technology. The generation unit generates an image that reflects the size of the living room and the size of the windows, and a 3D perspective that three-dimensionally represents the layout of the rooms.

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

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

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

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

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

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

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

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

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

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

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0176] [Explanation of symbols]

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

Claims

1. a reception department that receives customer requests; an analysis unit that analyzes the request received by the reception unit; a creation unit that creates a floor plan based on the needs analyzed by the analysis unit; a generation unit that generates an image or a 3D perspective based on the floor plan created by the creation unit; Equipped with A system characterized by:

2. The reception unit Customers enter their requests by uploading text or images The system of claim 1 .

3. The analysis unit Learned architectural rules and created appropriate floor plans based on customer requirements The system of claim 1 .

4. The generation unit Generate an image or 3D perspective based on the floor plan you created The system of claim 1 .

5. The generation unit Generate an image that reflects the living room area or window dimensions The system of claim 1 .

6. The generation unit Generate a 3D perspective that shows the layout of the room in three dimensions The system of claim 1 .

7. The reception unit Estimate customer emotions and adjust the timing of request acceptance based on the estimated customer emotions The system of claim 1 .

8. The reception unit Analyze the customer's past request history and select the appropriate reception method The system of claim 1 .

Images