system

Abstract

The system according to the embodiment aims to efficiently create architectural design patterns from photographs and drawings. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit inputs photographs and drawings. The analysis unit analyzes the information input by the reception unit. The generation unit creates a pattern of building guidelines based on the information analyzed by the analysis 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] Conventional techniques have had the problem of making it difficult to efficiently create architectural design patterns from photographs and drawings.

[0005] The system according to the embodiment aims to efficiently create architectural design patterns from photographs and drawings. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives photographs and drawings. The analysis unit analyzes the information received by the reception unit. The generation unit creates a pattern of architectural guidelines based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create architectural design patterns from photographs and drawings. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention creates architectural design patterns based on photographs and drawings of land in a subdivision. This system inputs photographs and drawings of the land, and a generation AI analyzes this information to create architectural design patterns. The generation AI then generates perspective drawings, design drawings, and a standard price estimate for each pattern. It also estimates costs such as land leveling and ground improvement costs by judging slopes and other factors from images. Furthermore, more accurate estimates can be achieved by learning relevant local government laws and regulations and ground information. For example, a user inputs photographs and drawings of the land in a subdivision. To do this, the user simply uploads the photographs and drawings to the system. For example, the user inputs a panoramic photograph of the land and a detailed drawing. This information is then input into the generation AI. The generation AI then analyzes the input information. The generation AI identifies the shape and characteristics of the land from the photographs and drawings and creates architectural design patterns. For example, it considers the shape of the land, the presence or absence of slopes, the surrounding environment, and other factors to propose optimal architectural designs. The generation AI then generates perspective drawings, design drawings, and a standard price estimate for each architectural design pattern. For example, the system generates a building's exterior perspective, interior design drawings, and construction cost estimates. This allows users to understand the specific building plan. Furthermore, the AI ​​can determine slopes and other factors from images and estimate costs for land leveling and ground improvement. For example, it analyzes the land's slope and ground condition to estimate the necessary land leveling and ground improvement costs. This allows users to understand the total construction cost. The AI ​​can also perform more accurate estimates by learning relevant local government laws and ground information. For example, it can propose appropriate building plans based on the Building Standards Act and ground characteristics, enabling construction that complies with laws and regulations. This system allows users to obtain specific building guidelines and cost estimates simply by entering photos and drawings of the land in a subdivision. This streamlines building planning and enables construction based on more accurate information. The system analyzes photos and drawings of the land in a subdivision, creates patterns for the building guidelines, generates perspective drawings, design drawings, and standard price estimates, and estimates land leveling and ground improvement costs by determining slopes and other factors from images.

[0029] A construction policy creation system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit inputs photos and drawings of land in a subdivision. For example, a user can upload photos and drawings of the subdivision to the system. The photos include, for example, panoramic photos of the land and detailed drawings. The analysis unit analyzes the information input by the reception unit. For example, the analysis unit identifies the shape and characteristics of the land from the photos and drawings and creates a construction policy pattern. The analysis unit uses image analysis algorithms and data analysis techniques to analyze the shape of the land, the presence or absence of slopes, the surrounding environment, and the like. The generation unit creates a construction policy pattern based on the information analyzed by the analysis unit. For example, the generation unit proposes an optimal construction policy taking into account the shape of the land, the presence or absence of slopes, the surrounding environment, and the like. The generation unit creates perspective drawings, design drawings, and standard price estimates for each construction policy pattern. For example, the generation unit generates exterior perspective drawings of the building, interior design drawings, and construction cost estimates. This allows the user to understand the specific construction plan. As a result, the construction policy creation system of the embodiment can analyze photographs and drawings of land in a subdivision, create construction policy patterns, and generate perspective drawings, design drawings, and standard price estimates.

[0030] The generation unit can create perspective drawings, design drawings, and price estimates for each pattern. The generation unit, for example, creates 3D perspective drawings and hand-drawn perspective drawings for each pattern. For example, the generation unit generates exterior perspective drawings and interior design drawings of a building. The generation unit can also create design drawings such as floor plans, elevations, and cross sections for each pattern. Furthermore, the generation unit creates a standard price estimate for each pattern. For example, the generation unit generates an estimate of construction costs taking into account material costs, labor costs, and other expenses. This allows the generation unit to create perspective drawings, design drawings, and standard price estimates for each pattern.

[0031] The analysis unit can determine slopes from images and estimate land leveling costs and ground improvement costs. The analysis unit, for example, determines the slope angle and topographical characteristics using an image analysis algorithm. For example, the analysis unit analyzes the slope of the land and the condition of the ground and estimates the necessary land leveling costs and ground improvement costs. The analysis unit takes into account the area of ​​the land and the difficulty of leveling when calculating land leveling costs. For example, the analysis unit calculates so that the larger the land area, the higher the land leveling costs. The analysis unit also takes into account the type of ground and the improvement method when calculating ground improvement costs. For example, the analysis unit calculates so that the softer the ground, the higher the ground improvement costs. This allows the analysis unit to determine slopes, etc. from images and estimate land leveling costs and ground improvement costs.

[0032] The analysis unit can learn the relevant laws and regulations of the local government and ground information. The analysis unit learns relevant laws and regulations such as the Building Standards Act and the City Planning Act. For example, the analysis unit proposes an appropriate building plan based on the Building Standards Act. The analysis unit also learns ground information such as geological survey data and ground strength. For example, the analysis unit proposes an appropriate building plan based on the characteristics of the ground. This allows the analysis unit to learn the relevant laws and regulations of the local government and ground information.

[0033] The reception unit allows the user to upload photos and drawings of the subdivision land to the system. The reception unit, for example, provides an interface for the user to upload photos and drawings of the subdivision land to the system. For example, the reception unit allows the user to upload photos and drawings of the subdivision land by drag and drop. The reception unit also allows the user to upload photos and drawings of the subdivision land through a file selection dialog. Furthermore, the reception unit allows the user to directly upload photos and drawings of the subdivision land from cloud storage. In this way, the reception unit allows the user to upload photos and drawings of the subdivision land to the system.

[0034] The generation unit can propose a building policy taking into consideration the shape of the land, the presence or absence of slopes, and the surrounding environment. The generation unit, for example, analyzes the shape of the land and proposes an optimal building policy. For example, the generation unit proposes an appropriate building policy depending on the shape of the land, such as flat land or sloping land. The generation unit also proposes a building policy taking into consideration the presence or absence of slopes. For example, if there is a slope, the generation unit proposes a building policy that makes use of the slope. Furthermore, the generation unit proposes a building policy taking into consideration the surrounding environment. For example, the generation unit proposes an appropriate building policy taking into consideration the surrounding environment, such as transportation access and nearby facilities. In this way, the generation unit can propose a building policy taking into consideration the shape of the land, the presence or absence of slopes, the surrounding environment, etc.

[0035] The reception unit can analyze the user's past upload history and suggest the optimal upload method. The reception unit can, for example, analyze the user's past upload history and suggest the optimal upload method. For example, the reception unit can automatically display, as candidates, the formats of photos and drawings that the user has frequently uploaded in the past. The reception unit can also preferentially suggest upload methods (audio, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the upload method to be used in a specific time period based on the user's past upload history. This allows the reception unit to analyze the user's past upload history and suggest the optimal upload method.

[0036] The reception unit can filter photos and drawings based on the user's current project and areas of interest when uploading them. For example, the reception unit prioritizes uploading photos and drawings related to a project currently underway by the user. For example, the reception unit can automatically select highly relevant photos and drawings based on the user's areas of interest. Furthermore, if the user is concentrating on a specific project, the reception unit can prioritize uploading information related to that project. This allows the reception unit to filter photos and drawings based on the user's current project and areas of interest when uploading them.

[0037] The reception unit can prioritize uploading highly relevant information in consideration of the user's geographical location information when uploading photos or drawings. For example, when the user is in a specific area, the reception unit prioritizes uploading photos or drawings related to that area. For example, the reception unit can prioritize uploading information about locations close to the user's current location. Furthermore, when the user is traveling, the reception unit can also upload highly relevant information in real time based on the user's current location. This allows the reception unit to prioritize uploading highly relevant information in consideration of the user's geographical location information when uploading photos or drawings.

[0038] The reception unit can analyze the user's social media activity and upload related information when uploading photos or drawings. The reception unit uploads related photos and drawings based on, for example, information shared by the user on social media. For example, the reception unit can analyze the content of the user's social media posts and prioritize uploading highly relevant information. Furthermore, if the user shares information about a specific project on social media, the reception unit can also upload photos and drawings related to that project. This allows the reception unit to analyze the user's social media activity and upload related information when uploading photos or drawings.

[0039] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, selects an optimal analysis algorithm based on past analysis data. For example, the analysis unit improves the accuracy of the analysis algorithm by referring to past analysis results. The analysis unit can also analyze past analysis data and optimize parameters of the analysis algorithm. This allows the analysis unit to optimize the analysis algorithm by referring to past analysis data during analysis.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the shape of the land, the presence or absence of slopes, and the surrounding environment. The analysis unit, for example, analyzes the shape of the land and proposes an optimal construction plan. For example, the analysis unit proposes an appropriate construction plan depending on the shape of the land, such as flat land or sloping land. The analysis unit also estimates the cost of leveling and ground improvement, taking into account the presence or absence of slopes. For example, if there is a slope, the analysis unit proposes a construction plan that makes use of the slope. Furthermore, the analysis unit optimizes the pattern of the construction plan by taking into account the surrounding environment. For example, the analysis unit proposes an appropriate construction plan by taking into account the surrounding environment, such as transportation access and nearby facilities. This allows the analysis unit to improve the accuracy of the analysis by taking into account the shape of the land, the presence or absence of slopes, the surrounding environment, etc.

[0041] The analysis unit can perform the analysis while taking into account the geographical distribution of land. The analysis unit, for example, proposes an optimal construction policy based on the geographical distribution of land. For example, the analysis unit estimates the cost of land leveling and ground improvement while taking into account the geographical distribution. The analysis unit can also analyze the geographical distribution and optimize the pattern of the construction policy. This allows the analysis unit to perform the analysis while taking into account the geographical distribution of land.

[0042] The analysis unit can improve the accuracy of the analysis by referring to related literature and databases during analysis. The analysis unit, for example, refers to related literature to improve the accuracy of the analysis algorithm. For example, the analysis unit refers to a database to improve the reliability of the analysis results. The analysis unit can also optimize parameters of the analysis algorithm based on related literature and databases. This allows the analysis unit to improve the accuracy of the analysis by referring to related literature and databases during analysis.

[0043] The generation unit can propose a building policy during generation by taking into consideration the shape of the land, the presence or absence of slopes, and the surrounding environment. The generation unit, for example, analyzes the shape of the land and proposes an optimal building policy. For example, the generation unit proposes an appropriate building policy depending on the shape of the land, such as flat land or sloping land. The generation unit also estimates the cost of leveling and ground improvement by taking into consideration the presence or absence of slopes. For example, if there is a slope, the generation unit proposes a building policy that makes use of the slope. Furthermore, the generation unit optimizes the pattern of the building policy by taking into consideration the surrounding environment. For example, the generation unit proposes an appropriate building policy by taking into consideration the surrounding environment, such as transportation access and surrounding facilities. This allows the generation unit to propose a building policy during generation by taking into consideration the shape of the land, the presence or absence of slopes, the surrounding environment, etc.

[0044] The generation unit can optimize the generation algorithm by referring to past generation data at the time of generation. The generation unit selects an optimal generation algorithm, for example, based on past generation data. For example, the generation unit improves the accuracy of the generation algorithm by referring to past generation results. The generation unit can also analyze past generation data and optimize parameters of the generation algorithm. This allows the generation unit to optimize the generation algorithm by referring to past generation data at the time of generation.

[0045] The generation unit can generate the land taking into consideration the geographic distribution of the land. The generation unit, for example, proposes an optimal construction policy based on the geographic distribution of the land. For example, the generation unit estimates the cost of leveling the land and the cost of ground improvement taking into consideration the geographic distribution. The generation unit can also analyze the geographic distribution and optimize the pattern of the construction policy. This allows the generation unit to generate the land taking into consideration the geographic distribution of the land.

[0046] The generation unit can improve the accuracy of generation by referring to related literature and databases during generation. The generation unit, for example, improves the accuracy of the generation algorithm by referring to related literature. For example, the generation unit improves the reliability of the generation result by referring to a database. The generation unit can also optimize parameters of the generation algorithm based on related literature and databases. This allows the generation unit to improve the accuracy of generation by referring to related literature and databases during generation.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can automatically evaluate the quality of photos and drawings uploaded by users and prompt them to re-upload if necessary. For example, the reception unit analyzes the image resolution, brightness, contrast, etc., and notifies the user if the quality is low. The reception unit can also evaluate the accuracy of reading the drawings and prompt the user to re-upload if there are any unclear parts. Furthermore, the reception unit can analyze the metadata (such as the date and time of the photo and location) of the photos and drawings uploaded by users and prompt the user to complete the information if it does not contain appropriate information. This allows the reception unit to ensure the quality of the photos and drawings uploaded by users and improve the analysis accuracy.

[0049] The generation unit can take into consideration the user's lifestyle and preferences when creating a pattern of architectural guidelines. For example, the generation unit can propose an optimal architectural guideline based on lifestyle information (such as family composition and hobbies) provided by the user. The generation unit can also customize the architectural guideline pattern by taking into consideration the user's preferences (such as design preferences and color preferences). Furthermore, the generation unit can analyze the user's past selection history and propose an optimal architectural guideline for the user. This allows the generation unit to propose an architectural guideline that reflects the user's lifestyle and preferences.

[0050] The analysis unit can perform analysis taking into account the climatic conditions of the land. For example, the analysis unit can propose optimal construction policies based on climatic data such as the land's annual precipitation, temperature, and wind direction. The analysis unit can also select building materials according to climatic conditions and evaluate insulation performance. Furthermore, the analysis unit can also propose construction policies that are suited to future climatic conditions, taking into account the impact of climate change. This allows the analysis unit to perform analysis taking into account the climatic conditions of the land and propose more appropriate construction policies.

[0051] The analysis unit can perform an analysis taking into account the historical background and cultural value of the land. For example, the analysis unit can propose a construction policy taking into account the presence of historical buildings and cultural assets on the land. The analysis unit can also propose a construction policy that incorporates the traditional architectural style and cultural elements of the region. Furthermore, the analysis unit can propose a construction policy that reflects the opinions and requests of local residents. In this way, the analysis unit can perform an analysis taking into account the historical background and cultural value of the land and propose a construction policy that is suitable for the region.

[0052] The reception unit can provide a function for strengthening privacy protection for photos and drawings uploaded by users. For example, the reception unit can automatically detect personal information from uploaded photos and drawings and perform a masking process. The reception unit can also provide an interface that allows users to select privacy settings when uploading. Furthermore, the reception unit can encrypt uploaded data to prevent unauthorized access by third parties. In this way, the reception unit can protect users' privacy and allow them to upload photos and drawings safely.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit inputs photos and drawings of the land in the subdivision. For example, a user can upload photos and drawings of the subdivision to the system. The photos can include, for example, a panoramic view of the land and detailed drawings. Step 2: The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit identifies the shape and characteristics of the land from photographs and drawings, and creates a pattern for the construction policy. The analysis unit uses image analysis algorithms and data analysis methods to analyze the shape of the land, the presence or absence of slopes, the surrounding environment, etc. Step 3: The generation unit creates patterns of construction policies based on the information analyzed by the analysis unit. For example, the generation unit considers the shape of the land, the presence or absence of slopes, the surrounding environment, and other factors to propose optimal construction policies. The generation unit creates perspective drawings, design drawings, and standard price estimates for each pattern of construction policies. For example, the generation unit generates exterior perspective drawings of the building, interior design drawings, and construction cost estimates.

[0055] (Example 2) A system according to an embodiment of the present invention creates architectural design patterns based on photographs and drawings of land in a subdivision. This system inputs photographs and drawings of the land, and a generation AI analyzes this information to create architectural design patterns. The generation AI then generates perspective drawings, design drawings, and a standard price estimate for each pattern. It also estimates costs such as land leveling and ground improvement costs by judging slopes and other factors from images. Furthermore, more accurate estimates can be achieved by learning relevant local government laws and regulations and ground information. For example, a user inputs photographs and drawings of the land in a subdivision. To do this, the user simply uploads the photographs and drawings to the system. For example, the user inputs a panoramic photograph of the land and a detailed drawing. This information is then input into the generation AI. The generation AI then analyzes the input information. The generation AI identifies the shape and characteristics of the land from the photographs and drawings and creates architectural design patterns. For example, it considers the shape of the land, the presence or absence of slopes, the surrounding environment, and other factors to propose optimal architectural designs. The generation AI then generates perspective drawings, design drawings, and a standard price estimate for each architectural design pattern. For example, the system generates a building's exterior perspective, interior design drawings, and construction cost estimates. This allows users to understand the specific building plan. Furthermore, the AI ​​can determine slopes and other factors from images and estimate costs for land leveling and ground improvement. For example, it analyzes the land's slope and ground condition to estimate the necessary land leveling and ground improvement costs. This allows users to understand the total construction cost. The AI ​​can also perform more accurate estimates by learning relevant local government laws and ground information. For example, it can propose appropriate building plans based on the Building Standards Act and ground characteristics, enabling construction that complies with laws and regulations. This system allows users to obtain specific building guidelines and cost estimates simply by entering photos and drawings of the land in a subdivision. This streamlines building planning and enables construction based on more accurate information. The system analyzes photos and drawings of the land in a subdivision, creates patterns for the building guidelines, generates perspective drawings, design drawings, and standard price estimates, and estimates land leveling and ground improvement costs by determining slopes and other factors from images.

[0056] A construction policy creation system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit inputs photos and drawings of land in a subdivision. For example, a user can upload photos and drawings of the subdivision to the system. The photos include, for example, panoramic photos of the land and detailed drawings. The analysis unit analyzes the information input by the reception unit. For example, the analysis unit identifies the shape and characteristics of the land from the photos and drawings and creates a construction policy pattern. The analysis unit uses image analysis algorithms and data analysis techniques to analyze the shape of the land, the presence or absence of slopes, the surrounding environment, and the like. The generation unit creates a construction policy pattern based on the information analyzed by the analysis unit. For example, the generation unit proposes an optimal construction policy taking into account the shape of the land, the presence or absence of slopes, the surrounding environment, and the like. The generation unit creates perspective drawings, design drawings, and standard price estimates for each construction policy pattern. For example, the generation unit generates exterior perspective drawings of the building, interior design drawings, and construction cost estimates. This allows the user to understand the specific construction plan. As a result, the construction policy creation system of the embodiment can analyze photographs and drawings of land in a subdivision, create construction policy patterns, and generate perspective drawings, design drawings, and standard price estimates.

[0057] The generation unit can create perspective drawings, design drawings, and price estimates for each pattern. The generation unit, for example, creates 3D perspective drawings and hand-drawn perspective drawings for each pattern. For example, the generation unit generates exterior perspective drawings and interior design drawings of a building. The generation unit can also create design drawings such as floor plans, elevations, and cross sections for each pattern. Furthermore, the generation unit creates a standard price estimate for each pattern. For example, the generation unit generates an estimate of construction costs taking into account material costs, labor costs, and other expenses. This allows the generation unit to create perspective drawings, design drawings, and standard price estimates for each pattern.

[0058] The analysis unit can determine slopes from images and estimate land leveling costs and ground improvement costs. The analysis unit, for example, determines the slope angle and topographical characteristics using an image analysis algorithm. For example, the analysis unit analyzes the slope of the land and the condition of the ground and estimates the necessary land leveling costs and ground improvement costs. The analysis unit takes into account the area of ​​the land and the difficulty of leveling when calculating land leveling costs. For example, the analysis unit calculates so that the larger the land area, the higher the land leveling costs. The analysis unit also takes into account the type of ground and the improvement method when calculating ground improvement costs. For example, the analysis unit calculates so that the softer the ground, the higher the ground improvement costs. This allows the analysis unit to determine slopes, etc. from images and estimate land leveling costs and ground improvement costs.

[0059] The analysis unit can learn the relevant laws and regulations of the local government and ground information. The analysis unit learns relevant laws and regulations such as the Building Standards Act and the City Planning Act. For example, the analysis unit proposes an appropriate building plan based on the Building Standards Act. The analysis unit also learns ground information such as geological survey data and ground strength. For example, the analysis unit proposes an appropriate building plan based on the characteristics of the ground. This allows the analysis unit to learn the relevant laws and regulations of the local government and ground information.

[0060] The reception unit allows the user to upload photos and drawings of the subdivision land to the system. The reception unit, for example, provides an interface for the user to upload photos and drawings of the subdivision land to the system. For example, the reception unit allows the user to upload photos and drawings of the subdivision land by drag and drop. The reception unit also allows the user to upload photos and drawings of the subdivision land through a file selection dialog. Furthermore, the reception unit allows the user to directly upload photos and drawings of the subdivision land from cloud storage. In this way, the reception unit allows the user to upload photos and drawings of the subdivision land to the system.

[0061] The generation unit can propose a building policy taking into consideration the shape of the land, the presence or absence of slopes, and the surrounding environment. The generation unit, for example, analyzes the shape of the land and proposes an optimal building policy. For example, the generation unit proposes an appropriate building policy depending on the shape of the land, such as flat land or sloping land. The generation unit also proposes a building policy taking into consideration the presence or absence of slopes. For example, if there is a slope, the generation unit proposes a building policy that makes use of the slope. Furthermore, the generation unit proposes a building policy taking into consideration the surrounding environment. For example, the generation unit proposes an appropriate building policy taking into consideration the surrounding environment, such as transportation access and nearby facilities. In this way, the generation unit can propose a building policy taking into consideration the shape of the land, the presence or absence of slopes, the surrounding environment, etc.

[0062] The reception unit can estimate the user's emotions and adjust the timing of uploading photos and drawings based on the estimated user emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the reception unit to estimate the user's emotions and adjust the timing of uploading photos and drawings based on the estimated user emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0063] The reception unit can analyze the user's past upload history and suggest the optimal upload method. The reception unit can, for example, analyze the user's past upload history and suggest the optimal upload method. For example, the reception unit can automatically display, as candidates, the formats of photos and drawings that the user has frequently uploaded in the past. The reception unit can also preferentially suggest upload methods (audio, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the upload method to be used in a specific time period based on the user's past upload history. This allows the reception unit to analyze the user's past upload history and suggest the optimal upload method.

[0064] The reception unit can filter photos and drawings based on the user's current project and areas of interest when uploading them. For example, the reception unit prioritizes uploading photos and drawings related to a project currently underway by the user. For example, the reception unit can automatically select highly relevant photos and drawings based on the user's areas of interest. Furthermore, if the user is concentrating on a specific project, the reception unit can prioritize uploading information related to that project. This allows the reception unit to filter photos and drawings based on the user's current project and areas of interest when uploading them.

[0065] The reception unit can estimate the user's emotions and determine the priority of photos and drawings to be uploaded based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on heart rate fluctuations. This allows the reception unit to estimate the user's emotions and determine the priority of photos and drawings to be uploaded based on the estimated user emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The reception unit can prioritize uploading highly relevant information in consideration of the user's geographical location information when uploading photos or drawings. For example, when the user is in a specific area, the reception unit prioritizes uploading photos or drawings related to that area. For example, the reception unit can prioritize uploading information about locations close to the user's current location. Furthermore, when the user is traveling, the reception unit can also upload highly relevant information in real time based on the user's current location. This allows the reception unit to prioritize uploading highly relevant information in consideration of the user's geographical location information when uploading photos or drawings.

[0067] The reception unit can analyze the user's social media activity and upload related information when uploading photos or drawings. The reception unit uploads related photos and drawings based on, for example, information shared by the user on social media. For example, the reception unit can analyze the content of the user's social media posts and prioritize uploading highly relevant information. Furthermore, if the user shares information about a specific project on social media, the reception unit can also upload photos and drawings related to that project. This allows the reception unit to analyze the user's social media activity and upload related information when uploading photos or drawings.

[0068] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the analysis unit to estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, selects an optimal analysis algorithm based on past analysis data. For example, the analysis unit improves the accuracy of the analysis algorithm by referring to past analysis results. The analysis unit can also analyze past analysis data and optimize parameters of the analysis algorithm. This allows the analysis unit to optimize the analysis algorithm by referring to past analysis data during analysis.

[0070] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the shape of the land, the presence or absence of slopes, and the surrounding environment. The analysis unit, for example, analyzes the shape of the land and proposes an optimal construction plan. For example, the analysis unit proposes an appropriate construction plan depending on the shape of the land, such as flat land or sloping land. The analysis unit also estimates the cost of leveling and ground improvement, taking into account the presence or absence of slopes. For example, if there is a slope, the analysis unit proposes a construction plan that makes use of the slope. Furthermore, the analysis unit optimizes the pattern of the construction plan by taking into account the surrounding environment. For example, the analysis unit proposes an appropriate construction plan by taking into account the surrounding environment, such as transportation access and nearby facilities. This allows the analysis unit to improve the accuracy of the analysis by taking into account the shape of the land, the presence or absence of slopes, the surrounding environment, etc.

[0071] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the analysis unit to estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The analysis unit can perform the analysis while taking into account the geographical distribution of land. The analysis unit, for example, proposes an optimal construction policy based on the geographical distribution of land. For example, the analysis unit estimates the cost of land leveling and ground improvement while taking into account the geographical distribution. The analysis unit can also analyze the geographical distribution and optimize the pattern of the construction policy. This allows the analysis unit to perform the analysis while taking into account the geographical distribution of land.

[0073] The analysis unit can improve the accuracy of the analysis by referring to related literature and databases during analysis. The analysis unit, for example, refers to related literature to improve the accuracy of the analysis algorithm. For example, the analysis unit refers to a database to improve the reliability of the analysis results. The analysis unit can also optimize parameters of the analysis algorithm based on related literature and databases. This allows the analysis unit to improve the accuracy of the analysis by referring to related literature and databases during analysis.

[0074] The generation unit can estimate the user's emotion and adjust the generated building policy pattern based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the generation unit to estimate the user's emotion and adjust the generated building policy pattern based on the estimated user emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The generation unit can propose a building policy during generation by taking into consideration the shape of the land, the presence or absence of slopes, and the surrounding environment. The generation unit, for example, analyzes the shape of the land and proposes an optimal building policy. For example, the generation unit proposes an appropriate building policy depending on the shape of the land, such as flat land or sloping land. The generation unit also estimates the cost of leveling and ground improvement by taking into consideration the presence or absence of slopes. For example, if there is a slope, the generation unit proposes a building policy that makes use of the slope. Furthermore, the generation unit optimizes the pattern of the building policy by taking into consideration the surrounding environment. For example, the generation unit proposes an appropriate building policy by taking into consideration the surrounding environment, such as transportation access and surrounding facilities. This allows the generation unit to propose a building policy during generation by taking into consideration the shape of the land, the presence or absence of slopes, the surrounding environment, etc.

[0076] The generation unit can optimize the generation algorithm by referring to past generation data at the time of generation. The generation unit selects an optimal generation algorithm, for example, based on past generation data. For example, the generation unit improves the accuracy of the generation algorithm by referring to past generation results. The generation unit can also analyze past generation data and optimize parameters of the generation algorithm. This allows the generation unit to optimize the generation algorithm by referring to past generation data at the time of generation.

[0077] The generation unit can estimate the user's emotion and adjust the display method of the generated perspective drawings and design drawings based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the generation unit to estimate the user's emotion and adjust the display method of the generated perspective drawings and design drawings based on the estimated user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The generation unit can generate the land taking into consideration the geographic distribution of the land. The generation unit, for example, proposes an optimal construction policy based on the geographic distribution of the land. For example, the generation unit estimates the cost of leveling the land and the cost of ground improvement taking into consideration the geographic distribution. The generation unit can also analyze the geographic distribution and optimize the pattern of the construction policy. This allows the generation unit to generate the land taking into consideration the geographic distribution of the land.

[0079] The generation unit can improve the accuracy of generation by referring to related literature and databases during generation. The generation unit, for example, improves the accuracy of the generation algorithm by referring to related literature. For example, the generation unit improves the reliability of the generation result by referring to a database. The generation unit can also optimize parameters of the generation algorithm based on related literature and databases. This allows the generation unit to improve the accuracy of generation by referring to related literature and databases during generation. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis 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 control unit 46A of the smart device 14, and allows a user to upload photos and drawings of a subdivision lot. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the shape and characteristics of the land from the photos and drawings. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a pattern of construction guidelines and generates perspective drawings, design drawings, and standard price estimates. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis 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 control unit 46A of the smart glasses 214, and allows a user to upload photos and drawings of a subdivision lot. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the shape and characteristics of the land from the photos and drawings. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a pattern of construction guidelines and generates perspective drawings, design drawings, and standard price estimates. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows a user to upload photos and drawings of a subdivision lot. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the shape and characteristics of the land from the photos and drawings. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a pattern of construction guidelines and generates perspective drawings, design drawings, and standard price estimates. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis 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 control unit 46A of the robot 414, and allows a user to upload photographs and drawings of a subdivision lot. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the shape and characteristics of the land from the photographs and drawings. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a pattern of construction guidelines and generates perspective drawings, design drawings, and standard price estimates.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can automatically evaluate the quality of photos and drawings uploaded by users and prompt them to re-upload if necessary. For example, the reception unit analyzes the image resolution, brightness, contrast, etc., and notifies the user if the quality is low. The reception unit can also evaluate the accuracy of reading the drawings and prompt the user to re-upload if there are any unclear parts. Furthermore, the reception unit can analyze the metadata (such as the date and time of the photo and location) of the photos and drawings uploaded by users and prompt the user to complete the information if it does not contain appropriate information. This allows the reception unit to ensure the quality of the photos and drawings uploaded by users and improve the analysis accuracy.

[0082] The generation unit can take into consideration the user's lifestyle and preferences when creating a pattern of architectural guidelines. For example, the generation unit can propose an optimal architectural guideline based on lifestyle information (such as family composition and hobbies) provided by the user. The generation unit can also customize the architectural guideline pattern by taking into consideration the user's preferences (such as design preferences and color preferences). Furthermore, the generation unit can analyze the user's past selection history and propose an optimal architectural guideline for the user. This allows the generation unit to propose an architectural guideline that reflects the user's lifestyle and preferences.

[0083] The analysis unit can perform analysis taking into account the climatic conditions of the land. For example, the analysis unit can propose optimal construction policies based on climatic data such as the land's annual precipitation, temperature, and wind direction. The analysis unit can also select building materials according to climatic conditions and evaluate insulation performance. Furthermore, the analysis unit can also propose construction policies that are suited to future climatic conditions, taking into account the impact of climate change. This allows the analysis unit to perform analysis taking into account the climatic conditions of the land and propose more appropriate construction policies.

[0084] The analysis unit can perform an analysis taking into account the historical background and cultural value of the land. For example, the analysis unit can propose a construction policy taking into account the presence of historical buildings and cultural assets on the land. The analysis unit can also propose a construction policy that incorporates the traditional architectural style and cultural elements of the region. Furthermore, the analysis unit can propose a construction policy that reflects the opinions and requests of local residents. In this way, the analysis unit can perform an analysis taking into account the historical background and cultural value of the land and propose a construction policy that is suitable for the region.

[0085] The reception unit can provide a function for strengthening privacy protection for photos and drawings uploaded by users. For example, the reception unit can automatically detect personal information from uploaded photos and drawings and perform a masking process. The reception unit can also provide an interface that allows users to select privacy settings when uploading. Furthermore, the reception unit can encrypt uploaded data to prevent unauthorized access by third parties. In this way, the reception unit can protect users' privacy and allow them to upload photos and drawings safely.

[0086] The generation unit can estimate the user's emotions and propose patterns of architectural guidelines based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can propose an architectural guideline that makes extensive use of natural materials and has a relaxing effect. If the user is excited, the generation unit can also propose an architectural guideline with a modern and stimulating design. Furthermore, if the user is feeling anxious, the generation unit can propose an architectural guideline that emphasizes safety and a sense of security. In this way, the generation unit can propose an architectural guideline that reflects the user's emotions.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can display the analysis results in a simple and easy-to-understand format. If the user is excited, the analysis unit can provide detailed analysis results to attract the user's interest. Furthermore, if the user is feeling anxious, the analysis unit can add explanations or supplementary information that provide a sense of security. In this way, the analysis unit can provide a display method of the analysis results that corresponds to the user's emotions.

[0088] The reception unit can estimate the user's emotions and determine the priority of photos and drawings to be uploaded based on the estimated user's emotions. For example, when the user is in a hurry, the reception unit can prioritize uploading important photos and drawings. When the user is relaxed, the reception unit can also upload detailed photos and drawings sequentially. Furthermore, when the user is feeling anxious, the reception unit can prioritize uploading photos and drawings that give a sense of security. In this way, the reception unit can provide an upload priority according to the user's emotions.

[0089] The generation unit can estimate the user's emotions and adjust the display method of the generated perspectives and design drawings based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can display perspectives that make use of soft colors and natural light. If the user is excited, the generation unit can display perspectives with vivid colors and dynamic designs. Furthermore, if the user is feeling anxious, the generation unit can display perspectives with simple, calm designs that give a sense of security. In this way, the generation unit can provide a display method of perspectives and design drawings that corresponds to the user's emotions.

[0090] The analysis unit can estimate the user's emotions and adjust the priority of analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can prioritize important analyses. If the user is relaxed, the analysis unit can also sequentially perform detailed analyses. Furthermore, if the user is feeling anxious, the analysis unit can prioritize providing analysis results that give a sense of security. In this way, the analysis unit can provide analysis priorities according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit inputs photos and drawings of the land in the subdivision. For example, a user can upload photos and drawings of the subdivision to the system. The photos can include, for example, a panoramic view of the land and detailed drawings. Step 2: The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit identifies the shape and characteristics of the land from photographs and drawings, and creates a pattern for the construction policy. The analysis unit uses image analysis algorithms and data analysis methods to analyze the shape of the land, the presence or absence of slopes, the surrounding environment, etc. Step 3: The generation unit creates patterns of construction policies based on the information analyzed by the analysis unit. For example, the generation unit considers the shape of the land, the presence or absence of slopes, the surrounding environment, and other factors to propose optimal construction policies. The generation unit creates perspective drawings, design drawings, and standard price estimates for each pattern of construction policies. For example, the generation unit generates exterior perspective drawings of the building, interior design drawings, and construction cost estimates.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 desk where photos and drawings are input; an analysis unit that analyzes the information input by the reception unit; a generation unit that generates a pattern of a construction policy based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. The generation unit Create perspective drawings, design drawings, and price estimates for each pattern 2. The system of claim 1.

3. The analysis unit Identify slopes from images and estimate costs for leveling and ground improvement.

2. The system of claim 1.

4. The analysis unit Learn about the relevant laws and regulations of the local government and geological information 2. The system of claim 1.

5. The reception unit Users upload photos and drawings of subdivision lots to the system 2. The system of claim 1.

6. The generation unit We propose architectural plans taking into consideration the shape of the land, whether there are slopes, and the surrounding environment.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of uploading photos and drawings based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze users' past upload history and suggest upload methods 2. The system of claim 1.

9. The reception unit Filter photos and drawings as they are uploaded based on your current projects and areas of interest 2. The system of claim 1.

Images