system

Abstract

To provide a system that efficiently detects deterioration of bridges and promptly takes appropriate countermeasures.SOLUTION: A system comprises: an analysis unit; a proposal unit; and a generation unit. The analysis unit analyzes photographs and detects signs of deterioration. The proposal unit proposes designs and candidates with high safety on the basis of the signs of deterioration detected by the analysis unit. The generation unit automatically generates the CAD design drawing on the basis of the designs proposed by the proposal unit and calculates the necessary costs and materials.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently detect bridge deterioration and quickly take appropriate measures.

[0005] The system according to the embodiment aims to efficiently detect deterioration of bridges and quickly take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, and a generation unit. The analysis unit analyzes a photo and detects signs of deterioration. The proposal unit proposes candidates with high design and safety based on the signs of deterioration detected by the analysis unit. The generation unit automatically generates a CAD design drawing based on the design proposed by the proposal unit, and calculates the necessary costs and materials. Effect of the Invention

[0007] The system according to the embodiment can efficiently detect deterioration of a bridge and quickly take appropriate measures. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The bridge deterioration countermeasure system according to the embodiment of the present invention is a system that improves the efficiency of bridge deterioration countermeasures. This system uses photo analysis to identify bridges that are likely to deteriorate and proposes candidates with high design and safety. Furthermore, it automatically calculates CAD design drawings and costs and materials to realize a rapid process. This mechanism can support efficient bridge design and renewal. For example, the system uses photo analysis to identify bridges that are likely to deteriorate. At this time, AI analyzes the photos and detects signs of bridge deterioration. For example, AI can detect abnormalities such as cracks and rust. This makes it possible to quickly identify bridges that are likely to deteriorate. Next, the system proposes candidates with high design and safety for the identified bridge. AI generates optimal designs and candidates with high safety based on past data and design standards. For example, AI can propose the structure of the bridge and the selection of materials. This makes it possible to efficiently propose candidates with high design and safety. Furthermore, the system automatically calculates CAD design drawings and costs and materials. AI automatically generates CAD design drawings based on the proposed design and calculates the necessary costs and materials. For example, bridge design drawings can be automatically generated, and AI can calculate the necessary materials and costs. This allows the design process to proceed quickly. This allows the system to support efficient bridge design and renewal. For example, bridges with a high probability of deterioration can be quickly identified and bridge renewal can be efficiently promoted by proposing optimal designs and candidates with high safety. In addition, the design process can be quickly promoted by CAD design drawings and automatic calculation of costs and materials. This allows bridge deterioration countermeasures to be made more efficient and safety to be improved. This allows the bridge deterioration countermeasures system to be able to make bridge deterioration countermeasures more efficient and safety to be improved.

[0029] The bridge deterioration countermeasure system according to the embodiment includes an analysis unit, a proposal unit, and a generation unit. The analysis unit analyzes a photograph and detects signs of deterioration. The analysis unit analyzes the photograph using, for example, image recognition technology or deep learning, and detects abnormalities such as cracks and rust. For example, the analysis unit can detect the width and depth of cracks in the photograph using AI. The analysis unit can also detect the spread of rust. Furthermore, the analysis unit can detect discoloration and other signs of deterioration. For example, the analysis unit can identify an abnormal part in the photograph using image recognition technology and acquire its location information. The proposal unit proposes candidates with high design and safety based on the signs of deterioration detected by the analysis unit. The proposal unit proposes optimal candidates with high design and safety based on, for example, past design data and safety standards. For example, the proposal unit can select the structure and materials of the bridge using AI. Furthermore, the proposal unit can propose a design that takes durability and safety into consideration. Furthermore, the proposal unit can propose optimal design standards based on past data. For example, the proposal unit can refer to past project data and propose an optimal design. The generation unit automatically generates a CAD design based on the design proposed by the proposal unit and calculates the necessary costs and materials. The generation unit automatically generates a CAD design based on the proposed design, for example. For example, the generation unit can automatically generate a bridge design using AI. The generation unit can also calculate the necessary materials and costs. For example, the generation unit can calculate the necessary costs based on the unit price of materials and construction costs. Furthermore, the generation unit can calculate the necessary materials and costs based on the database to be used. For example, the generation unit can calculate the necessary materials by referring to the material database. As a result, the bridge deterioration countermeasure system according to the embodiment can improve the efficiency of bridge deterioration countermeasures and support rapid design and renewal. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a design using an AI model that inputs the design proposed by the proposal unit and outputs a CAD design.

[0030] The analysis unit analyzes the photos and detects signs of deterioration. The analysis unit analyzes the photos using, for example, image recognition technology and deep learning to detect abnormalities such as cracks and rust. Specifically, the analysis unit can use AI to detect the width and depth of cracks in the photos. The AI ​​is trained in advance with a large amount of crack image data and can detect cracks with high accuracy even under different angles and lighting conditions. The analysis unit can also detect the spread of rust. An algorithm that combines hue and texture analysis is used to detect rust, and the progress and spread of rust can be quantitatively evaluated. In addition, the analysis unit can detect discoloration and other signs of deterioration. For example, the analysis unit can use image recognition technology to identify abnormal areas in the photos and obtain their location information. The location information is accurately identified using GPS data and photo metadata and is used for subsequent processing. The analysis unit can also centrally manage these data and track the history of abnormal areas. This enables the analysis unit to obtain a detailed understanding of the bridge's deterioration status and provide basic data for taking appropriate measures.

[0031] The proposal unit proposes a design and a candidate with high safety based on the signs of deterioration detected by the analysis unit. The proposal unit proposes an optimal design and a candidate with high safety based on, for example, past design data and safety standards. Specifically, the proposal unit can use AI to select the structure and materials of the bridge. The AI ​​learns past design data and safety standards and builds a model for generating an optimal design. For example, the proposal unit can propose a design that takes into account the load distribution and durability of the bridge. The proposal unit can also propose a design that takes into account durability and safety. This includes material selection and structural optimization, and specific proposals are made to ensure long-term durability and safety. Furthermore, the proposal unit can propose optimal design standards based on past data. For example, the proposal unit can refer to past project data and make proposals based on designs that have been successful under similar conditions. As a result, the proposal unit can quickly propose optimal designs and candidates with high safety based on the data provided by the analysis unit, and efficiently proceed with measures against aging of the bridge.

[0032] The generating unit automatically generates a CAD design based on the design proposed by the proposing unit, and calculates the necessary costs and materials. The generating unit automatically generates a CAD design based on the proposed design, for example. Specifically, the generating unit can automatically generate a bridge design using AI. The AI ​​takes the design data provided by the proposing unit as input, and operates CAD software to generate a design. This significantly streamlines the design process. The generating unit can also calculate the necessary materials and costs. For example, the generating unit can calculate the necessary costs based on the unit price of materials and construction costs. The unit price of materials is obtained from the latest market database, and the construction costs are calculated based on past project data. Furthermore, the generating unit can calculate the necessary materials and costs based on the database to be used. For example, the generating unit can refer to the material database and calculate the necessary materials. As a result, the generating unit can quickly and accurately generate a design based on the design provided by the proposing unit, and calculate the necessary costs and materials. As a result, the bridge deterioration countermeasure system according to the embodiment can streamline bridge deterioration countermeasures and support rapid design and renewal. Some or all of the above-mentioned processes in the generating unit may be performed using AI, for example, or may be performed without using AI. For example, the generating unit may generate a design drawing using an AI model that takes the design proposed by the proposing unit as input and outputs a CAD design drawing.

[0033] The analysis unit can analyze the photo using image recognition technology or deep learning to detect abnormalities such as cracks or rust. The analysis unit can, for example, use image recognition technology to detect the width and depth of cracks in the photo. For example, the analysis unit can use AI to measure the width of the crack in millimeters and estimate its depth. The analysis unit can also detect the spread of rust. For example, the analysis unit can use deep learning to detect the spread of rust in pixels and calculate its area. Furthermore, the analysis unit can detect discoloration and other signs of aging. For example, the analysis unit can use image recognition technology to identify abnormal parts in the photo and obtain their location information. In this way, signs of aging can be detected with high accuracy by using image recognition technology or deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can detect abnormalities using an AI model that inputs a photo and outputs abnormalities in cracks and rust.

[0034] The proposal unit can propose an optimal design and a candidate with high safety based on past design data or safety standards. The proposal unit, for example, proposes an optimal design based on past design data. For example, the proposal unit can use AI to analyze past project data and propose an optimal design. The proposal unit can also propose an optimal design and a candidate with high safety based on safety standards. For example, the proposal unit can propose a design that takes into account the Building Standards Act and earthquake resistance standards. Furthermore, the proposal unit can also propose optimal design standards based on past data. For example, the proposal unit can refer to past project data and propose an optimal design. In this way, by utilizing past data and standards, it is possible to efficiently propose an optimal design and a candidate with high safety. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can propose a design using an AI model that inputs past design data and safety standards and outputs an optimal design.

[0035] The generating unit can automatically generate a CAD design based on a proposed design and calculate the necessary costs and materials. The generating unit automatically generates a CAD design based on a proposed design, for example. For example, the generating unit can automatically generate a bridge design using AI. The generating unit can also calculate the necessary materials and costs. For example, the generating unit can calculate the necessary costs based on the unit price of materials and construction costs. Furthermore, the generating unit can calculate the necessary materials and costs based on the database to be used. For example, the generating unit can calculate the necessary materials by referring to a material database. This allows the design process to be quickly advanced by automatically generating a CAD design and calculating costs and materials. Some or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate a design using an AI model that inputs a design proposed by the proposing unit and outputs a CAD design.

[0036] The generation unit can use a specific algorithm when generating the blueprint. The generation unit can use, for example, an optimization algorithm when generating the blueprint. For example, the generation unit can optimize the blueprint using AI. The generation unit can also generate the blueprint using a generation algorithm. For example, the generation unit can generate the blueprint using AI. As a result, the accuracy and efficiency of the blueprint are improved by using a specific algorithm. A part or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate the blueprint using an AI model that inputs an optimization algorithm or a generation algorithm and outputs a blueprint.

[0037] The generation unit can calculate the necessary materials and costs based on the database to be used. The generation unit, for example, refers to a material database and calculates the necessary materials. For example, the generation unit can use AI to analyze the material database and calculate the necessary materials. The generation unit can also refer to a cost database and calculate the necessary costs. For example, the generation unit can use AI to analyze the cost database and calculate the necessary costs. This makes it possible to accurately and efficiently calculate materials and costs by utilizing the database. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can calculate materials and costs using an AI model that inputs a material database or a cost database and outputs the necessary materials and costs.

[0038] The analysis unit can compare photos taken under different weather conditions and detect signs of deterioration more accurately. The analysis unit, for example, compares photos taken on sunny days and rainy days to detect the progress of cracks. For example, the analysis unit can use AI to analyze photos taken on sunny days and rainy days to detect the progress of cracks. The analysis unit can also compare photos taken on snowy days and sunny days to detect the progress of rust. For example, the analysis unit can use AI to analyze photos taken on snowy days and sunny days to detect the progress of rust. Furthermore, the analysis unit can compare photos taken on windy days and calm days to detect deformation of the structure. For example, the analysis unit can use AI to analyze photos taken on windy days and calm days to detect deformation of the structure. As a result, signs of deterioration can be detected more accurately by comparing photos taken under different weather conditions. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can detect signs of deterioration using an AI model that takes photos taken under different weather conditions as input and outputs signs of deterioration.

[0039] The analysis unit can predict the degree of deterioration by referring to the past maintenance history of the bridge. The analysis unit predicts the degree of cracking, for example, based on the past maintenance history. For example, the analysis unit can use AI to analyze the past maintenance history and predict the degree of cracking. The analysis unit can also predict the degree of rusting, based on the history of past rust removal work. For example, the analysis unit can use AI to analyze the history of past rust removal work and predict the degree of rusting. Furthermore, the analysis unit can predict the degree of deterioration of the structure, based on the history of past reinforcement work. For example, the analysis unit can use AI to analyze the history of past reinforcement work and predict the degree of deterioration of the structure. As a result, the degree of deterioration can be accurately predicted by referring to the past maintenance history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can predict the degree of deterioration using an AI model that inputs the past maintenance history and outputs the degree of deterioration.

[0040] The analysis unit can identify deterioration factors specific to a region based on the geographical location information of the bridge. For example, in the case of a bridge near the coast, the analysis unit identifies deterioration due to salt damage. For example, the analysis unit can use AI to analyze a photo of a bridge near the coast and identify deterioration due to salt damage. In addition, in the case of a bridge in a mountainous area, the analysis unit can also identify cracks caused by freezing. For example, the analysis unit can use AI to analyze a photo of a bridge in a mountainous area and identify cracks caused by freezing. In addition, in the case of a bridge in an urban area, the analysis unit can also identify rust caused by exhaust gas. For example, the analysis unit can use AI to analyze a photo of a bridge in an urban area and identify rust caused by exhaust gas. In this way, by taking into account the geographical location information, it is possible to accurately identify deterioration factors specific to a region. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can identify factors using an AI model that inputs geographical location information of a bridge and outputs deterioration factors specific to a region.

[0041] The analysis unit can identify weak points in the design by comparing with the design drawing of the bridge. The analysis unit, for example, compares the design drawing with an actual photograph and identifies the location of cracks. For example, the analysis unit can use AI to analyze the design drawing with the actual photograph and identify the location of cracks. The analysis unit can also compare the design drawing with the actual photograph and identify the progress of rust. For example, the analysis unit can use AI to analyze the design drawing with the actual photograph and identify the progress of rust. Furthermore, the analysis unit can compare the design drawing with the actual photograph and identify deformation of the structure. For example, the analysis unit can use AI to analyze the design drawing with the actual photograph and identify deformation of the structure. This makes it possible to accurately identify weak points in the design by comparing with the design drawing. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can identify weak points by using an AI model that inputs the design drawing and the actual photograph and outputs weak points in the design.

[0042] The proposal unit can select an optimal design based on the purpose of use and traffic volume of the bridge. For example, in the case of a highway bridge, the proposal unit proposes a design that emphasizes durability. For example, the proposal unit can use AI to analyze the purpose of use and traffic volume of the highway bridge and propose a design that emphasizes durability. In addition, the proposal unit can also propose a design that emphasizes safety and aesthetics in the case of a pedestrian-only bridge. For example, the proposal unit can use AI to analyze the purpose of use and traffic volume of the pedestrian-only bridge and propose a design that emphasizes safety and aesthetics. In addition, the proposal unit can also propose a design that emphasizes vibration countermeasures in the case of a railway bridge. For example, the proposal unit can use AI to analyze the purpose of use and traffic volume of the railway bridge and propose a design that emphasizes vibration countermeasures. As a result, the functionality and safety of the bridge are improved by selecting a design based on the purpose of use and traffic volume. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can select a design using an AI model that inputs the purpose of use and traffic volume of the bridge and outputs an optimal design.

[0043] The proposal unit can compare the durability and cost of different materials and propose the optimal material. The proposal unit, for example, compares the durability of steel and concrete and proposes the optimal material. For example, the proposal unit can use AI to analyze the durability of steel and concrete and propose the optimal material. The proposal unit can also compare the cost of wood and composite materials and propose the optimal material. For example, the proposal unit can use AI to analyze the cost of wood and composite materials and propose the optimal material. Furthermore, the proposal unit can compare the performance of new materials and conventional materials and propose the optimal material. For example, the proposal unit can use AI to analyze the performance of new materials and conventional materials and propose the optimal material. This makes it possible to select the optimal material by comparing the durability and cost of materials. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can propose materials using an AI model that inputs the durability and cost of different materials and outputs the optimal material.

[0044] The proposal unit can propose a design that takes into consideration the surrounding environment and scenery of the bridge. For example, in the case of a bridge in a natural park, the proposal unit proposes a design that blends into the surrounding scenery. For example, the proposal unit can use AI to analyze the surrounding environment and scenery of the bridge in the natural park and propose a design that blends into the surrounding scenery. In addition, in the case of a bridge in an urban area, the proposal unit can also propose a design that harmonizes with the surrounding buildings. For example, the proposal unit can use AI to analyze the surrounding environment and scenery of the bridge in the urban area and propose a design that harmonizes with the surrounding buildings. Furthermore, in the case of a bridge in a historical place, the proposal unit can also propose a design that takes into consideration the historical background. For example, the proposal unit can use AI to analyze the surrounding environment and scenery of the bridge in a historical place and propose a design that takes into consideration the historical background. As a result, by proposing a design that takes into consideration the surrounding environment and scenery, harmony with the region can be achieved. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can propose a design using an AI model that inputs the surrounding environment and scenery of the bridge and outputs an optimal design.

[0045] The proposal unit can improve the accuracy of the proposal by referring to the success cases of similar projects in the past. The proposal unit, for example, proposes an optimal design based on the past success cases. For example, the proposal unit can use AI to analyze the success cases of similar projects in the past and propose an optimal design. The proposal unit can also propose a highly safe design by referring to the data of similar projects. For example, the proposal unit can use AI to analyze the data of similar projects and propose a highly safe design. Furthermore, the proposal unit can also propose a cost-efficient design based on the cost data of the success cases. For example, the proposal unit can use AI to analyze the cost data of the success cases and propose a cost-efficient design. As a result, the accuracy of the proposal is improved by referring to the past success cases. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can propose a design using an AI model that inputs the success cases of similar projects in the past and outputs the optimal design.

[0046] The generating unit can perform detailed structural analysis necessary for designing the bridge and reflect it in the design drawing. The generating unit, for example, analyzes the load distribution of the bridge and reflects it in the design drawing. For example, the generating unit can use AI to analyze the load distribution of the bridge and reflect the result in the design drawing. The generating unit can also analyze the vibration characteristics of the bridge and reflect it in the design drawing. For example, the generating unit can use AI to analyze the vibration characteristics of the bridge and reflect the result in the design drawing. Furthermore, the generating unit can analyze the durability of the bridge and reflect it in the design drawing. For example, the generating unit can use AI to analyze the durability of the bridge and reflect the result in the design drawing. As a result, the accuracy of the design drawing is improved by performing detailed structural analysis. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can use an AI model that inputs data necessary for designing the bridge and outputs detailed structural analysis results to reflect them in the design drawing.

[0047] The generation unit can compare the cost and effect of different design options to select the optimal design. The generation unit, for example, compares the costs of multiple design options and selects the optimal design. For example, the generation unit can use AI to analyze the costs of multiple design options and select the optimal design. The generation unit can also compare the durability of multiple design options and select the optimal design. For example, the generation unit can use AI to analyze the durability of multiple design options and select the optimal design. Furthermore, the generation unit can compare the construction time of multiple design options and select the optimal design. For example, the generation unit can use AI to analyze the construction time of multiple design options and select the optimal design. This allows the optimal design to be selected by comparing the cost and effect. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can select a design using an AI model that inputs data of different design options, compares the cost and effect, and outputs the optimal design.

[0048] The generation unit can automatically check the laws and regulations and standards required for the bridge design and reflect them in the design drawing. The generation unit, for example, checks the Building Standards Act required for the bridge design and reflects them in the design drawing. For example, the generation unit can use AI to analyze the Building Standards Act required for the bridge design and reflect the results in the design drawing. The generation unit can also check the earthquake resistance standards required for the bridge design and reflect them in the design drawing. For example, the generation unit can use AI to analyze the earthquake resistance standards required for the bridge design and reflect the results in the design drawing. Furthermore, the generation unit can check the environmental standards required for the bridge design and reflect them in the design drawing. For example, the generation unit can use AI to analyze the environmental standards required for the bridge design and reflect the results in the design drawing. As a result, the suitability of the design drawing is improved by automatically checking the laws and regulations and standards. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the laws and regulations and standards required for the bridge design and perform the check using an AI model that reflects them in the design drawing.

[0049] The generation unit can perform an environmental impact assessment required for the bridge design and reflect it in the design drawing. The generation unit, for example, performs an environmental impact assessment required for the bridge design and reflects it in the design drawing. For example, the generation unit can use AI to analyze the environmental impact assessment required for the bridge design and reflect the result in the design drawing. The generation unit can also perform a noise impact assessment required for the bridge design and reflect it in the design drawing. For example, the generation unit can use AI to analyze the noise impact assessment required for the bridge design and reflect the result in the design drawing. Furthermore, the generation unit can perform a landscape impact assessment required for the bridge design and reflect it in the design drawing. For example, the generation unit can use AI to analyze the landscape impact assessment required for the bridge design and reflect the result in the design drawing. Thereby, the environmental compatibility of the design drawing is improved by performing the environmental impact assessment. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can perform an evaluation using an AI model that inputs environmental impact assessment data required for the bridge design and reflects it in the design drawing.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can acquire vibration data of the bridge in real time and detect abnormal vibrations. For example, the analysis unit can acquire vibration data from sensors installed on the bridge and detect abnormal vibrations using AI. When abnormal vibrations are detected, the analysis unit can also identify their location and indicate areas that require repairs. Furthermore, the analysis unit can estimate the cause of the abnormal vibrations and propose appropriate countermeasures. This makes it possible to improve the safety of bridges through the analysis of vibration data in real time.

[0052] The proposal unit can analyze the traffic conditions around the bridge and propose an optimal construction schedule. For example, the proposal unit can obtain traffic volume data and use AI to propose optimal construction time periods. The proposal unit can also propose construction plans to minimize traffic congestion. Furthermore, the proposal unit can propose a construction schedule that takes into account the impact on surrounding residents. This makes it possible to propose an efficient construction schedule that takes traffic conditions into account.

[0053] The generation unit can optimize energy efficiency in bridge design. For example, the generation unit can optimize the installation positions of solar panels and generate blueprints that maximize energy efficiency. The generation unit can also optimize the installation positions of wind power generation equipment and generate blueprints that improve energy efficiency. Furthermore, the generation unit can select materials to minimize energy consumption and reflect them in the blueprints. This makes it possible to realize a sustainable bridge design that takes energy efficiency into consideration.

[0054] The proposal unit can propose a design that takes into account evacuation routes in the event of a disaster when designing a bridge. For example, the proposal unit can simulate disaster scenarios such as earthquakes and floods and propose optimal evacuation routes. The proposal unit can also propose a design that optimizes the installation positions of evacuation route signs and guide boards. Furthermore, the proposal unit can also propose structural reinforcement plans to ensure the safety of evacuation routes. This makes it possible to realize a bridge design that takes into account safety in the event of a disaster.

[0055] The generation unit can use environmentally friendly materials in the bridge design. For example, the generation unit can select renewable materials and low-carbon materials and reflect them in the design drawings. The generation unit can also propose construction methods to minimize the environmental load. Furthermore, the generation unit can evaluate the environmental impact throughout the entire life cycle of the bridge and propose an optimal design. This makes it possible to realize an environmentally friendly and sustainable bridge design.

[0056] The Proposal Department can propose designs that reflect the culture and history of the region when designing the bridge. For example, the Proposal Department can propose designs that incorporate the traditional architectural style of the region. The Proposal Department can also propose designs based on the theme of historical events or people of the region. Furthermore, the Proposal Department can also propose designs that incorporate art and crafts of the region. In this way, it is possible to realize a bridge design that respects the culture and history of the region.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The analysis unit analyzes the photo and detects signs of deterioration. The analysis unit uses image recognition technology and deep learning to analyze the photo and detects abnormalities such as cracks and rust. For example, the analysis unit can use AI to detect the width and depth of cracks in the photo. It can also detect signs of deterioration such as the spread of rust and discoloration. Furthermore, the analysis unit can use image recognition technology to identify abnormal areas in the photo and obtain their location information. Step 2: The proposal unit proposes design and safety candidates based on the signs of deterioration detected by the analysis unit. The proposal unit proposes optimal designs and safety candidates based on past design data and safety standards. For example, the proposal unit can use AI to select the bridge structure and materials. It can also propose designs that take durability and safety into consideration. It can also refer to past project data to propose optimal design standards. Step 3: The generation unit automatically generates a CAD design based on the design proposed by the proposal unit, and calculates the necessary costs and materials. The generation unit automatically generates a CAD design based on the proposed design. For example, the generation unit can use AI to automatically generate a design for a bridge. It can also calculate the necessary materials and costs. It can calculate the necessary costs based on the unit price of materials and construction costs. It can also calculate the necessary materials and costs based on the database used.

[0059] (Example 2) The bridge deterioration countermeasure system according to the embodiment of the present invention is a system that improves the efficiency of bridge deterioration countermeasures. This system uses photo analysis to identify bridges that are likely to deteriorate and proposes candidates with high design and safety. Furthermore, it automatically calculates CAD design drawings and costs and materials to realize a rapid process. This mechanism can support efficient bridge design and renewal. For example, the system uses photo analysis to identify bridges that are likely to deteriorate. At this time, AI analyzes the photos and detects signs of bridge deterioration. For example, AI can detect abnormalities such as cracks and rust. This makes it possible to quickly identify bridges that are likely to deteriorate. Next, the system proposes candidates with high design and safety for the identified bridge. AI generates optimal designs and candidates with high safety based on past data and design standards. For example, AI can propose the structure of the bridge and the selection of materials. This makes it possible to efficiently propose candidates with high design and safety. Furthermore, the system automatically calculates CAD design drawings and costs and materials. AI automatically generates CAD design drawings based on the proposed design and calculates the necessary costs and materials. For example, bridge design drawings can be automatically generated, and AI can calculate the necessary materials and costs. This allows the design process to proceed quickly. This allows the system to support efficient bridge design and renewal. For example, bridges with a high probability of deterioration can be quickly identified and bridge renewal can be efficiently promoted by proposing optimal designs and candidates with high safety. In addition, the design process can be quickly promoted by CAD design drawings and automatic calculation of costs and materials. This allows bridge deterioration countermeasures to be made more efficient and safety to be improved. This allows the bridge deterioration countermeasures system to be able to make bridge deterioration countermeasures more efficient and safety to be improved.

[0060] The bridge deterioration countermeasure system according to the embodiment includes an analysis unit, a proposal unit, and a generation unit. The analysis unit analyzes a photograph and detects signs of deterioration. The analysis unit analyzes the photograph using, for example, image recognition technology or deep learning, and detects abnormalities such as cracks and rust. For example, the analysis unit can detect the width and depth of cracks in the photograph using AI. The analysis unit can also detect the spread of rust. Furthermore, the analysis unit can detect discoloration and other signs of deterioration. For example, the analysis unit can identify an abnormal part in the photograph using image recognition technology and acquire its location information. The proposal unit proposes candidates with high design and safety based on the signs of deterioration detected by the analysis unit. The proposal unit proposes optimal candidates with high design and safety based on, for example, past design data and safety standards. For example, the proposal unit can select the structure and materials of the bridge using AI. Furthermore, the proposal unit can propose a design that takes durability and safety into consideration. Furthermore, the proposal unit can propose optimal design standards based on past data. For example, the proposal unit can refer to past project data and propose an optimal design. The generation unit automatically generates a CAD design based on the design proposed by the proposal unit and calculates the necessary costs and materials. The generation unit automatically generates a CAD design based on the proposed design, for example. For example, the generation unit can automatically generate a bridge design using AI. The generation unit can also calculate the necessary materials and costs. For example, the generation unit can calculate the necessary costs based on the unit price of materials and construction costs. Furthermore, the generation unit can calculate the necessary materials and costs based on the database to be used. For example, the generation unit can calculate the necessary materials by referring to the material database. As a result, the bridge deterioration countermeasure system according to the embodiment can improve the efficiency of bridge deterioration countermeasures and support rapid design and renewal. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a design using an AI model that inputs the design proposed by the proposal unit and outputs a CAD design.

[0061] The analysis unit analyzes the photos and detects signs of deterioration. The analysis unit analyzes the photos using, for example, image recognition technology and deep learning to detect abnormalities such as cracks and rust. Specifically, the analysis unit can use AI to detect the width and depth of cracks in the photos. The AI ​​is trained in advance with a large amount of crack image data and can detect cracks with high accuracy even under different angles and lighting conditions. The analysis unit can also detect the spread of rust. An algorithm that combines hue and texture analysis is used to detect rust, and the progress and spread of rust can be quantitatively evaluated. In addition, the analysis unit can detect discoloration and other signs of deterioration. For example, the analysis unit can use image recognition technology to identify abnormal areas in the photos and obtain their location information. The location information is accurately identified using GPS data and photo metadata and is used for subsequent processing. The analysis unit can also centrally manage these data and track the history of abnormal areas. This enables the analysis unit to obtain a detailed understanding of the bridge's deterioration status and provide basic data for taking appropriate measures.

[0062] The proposal unit proposes a design and a candidate with high safety based on the signs of deterioration detected by the analysis unit. The proposal unit proposes an optimal design and a candidate with high safety based on, for example, past design data and safety standards. Specifically, the proposal unit can use AI to select the structure and materials of the bridge. The AI ​​learns past design data and safety standards and builds a model for generating an optimal design. For example, the proposal unit can propose a design that takes into account the load distribution and durability of the bridge. The proposal unit can also propose a design that takes into account durability and safety. This includes material selection and structural optimization, and specific proposals are made to ensure long-term durability and safety. Furthermore, the proposal unit can propose optimal design standards based on past data. For example, the proposal unit can refer to past project data and make proposals based on designs that have been successful under similar conditions. As a result, the proposal unit can quickly propose optimal designs and candidates with high safety based on the data provided by the analysis unit, and efficiently proceed with measures against aging of the bridge.

[0063] The generating unit automatically generates a CAD design based on the design proposed by the proposing unit, and calculates the necessary costs and materials. The generating unit automatically generates a CAD design based on the proposed design, for example. Specifically, the generating unit can automatically generate a bridge design using AI. The AI ​​takes the design data provided by the proposing unit as input, and operates CAD software to generate a design. This significantly streamlines the design process. The generating unit can also calculate the necessary materials and costs. For example, the generating unit can calculate the necessary costs based on the unit price of materials and construction costs. The unit price of materials is obtained from the latest market database, and the construction costs are calculated based on past project data. Furthermore, the generating unit can calculate the necessary materials and costs based on the database to be used. For example, the generating unit can refer to the material database and calculate the necessary materials. As a result, the generating unit can quickly and accurately generate a design based on the design provided by the proposing unit, and calculate the necessary costs and materials. As a result, the bridge deterioration countermeasure system according to the embodiment can streamline bridge deterioration countermeasures and support rapid design and renewal. Some or all of the above-mentioned processes in the generating unit may be performed using AI, for example, or may be performed without using AI. For example, the generating unit may generate a design drawing using an AI model that takes the design proposed by the proposing unit as input and outputs a CAD design drawing.

[0064] The analysis unit can analyze the photo using image recognition technology or deep learning to detect abnormalities such as cracks or rust. The analysis unit can, for example, use image recognition technology to detect the width and depth of cracks in the photo. For example, the analysis unit can use AI to measure the width of the crack in millimeters and estimate its depth. The analysis unit can also detect the spread of rust. For example, the analysis unit can use deep learning to detect the spread of rust in pixels and calculate its area. Furthermore, the analysis unit can detect discoloration and other signs of aging. For example, the analysis unit can use image recognition technology to identify abnormal parts in the photo and obtain their location information. In this way, signs of aging can be detected with high accuracy by using image recognition technology or deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can detect abnormalities using an AI model that inputs a photo and outputs abnormalities in cracks and rust.

[0065] The proposal unit can propose an optimal design and a candidate with high safety based on past design data or safety standards. The proposal unit, for example, proposes an optimal design based on past design data. For example, the proposal unit can use AI to analyze past project data and propose an optimal design. The proposal unit can also propose an optimal design and a candidate with high safety based on safety standards. For example, the proposal unit can propose a design that takes into account the Building Standards Act and earthquake resistance standards. Furthermore, the proposal unit can also propose optimal design standards based on past data. For example, the proposal unit can refer to past project data and propose an optimal design. In this way, by utilizing past data and standards, it is possible to efficiently propose an optimal design and a candidate with high safety. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can propose a design using an AI model that inputs past design data and safety standards and outputs an optimal design.

[0066] The generating unit can automatically generate a CAD design based on a proposed design and calculate the necessary costs and materials. The generating unit automatically generates a CAD design based on a proposed design, for example. For example, the generating unit can automatically generate a bridge design using AI. The generating unit can also calculate the necessary materials and costs. For example, the generating unit can calculate the necessary costs based on the unit price of materials and construction costs. Furthermore, the generating unit can calculate the necessary materials and costs based on the database to be used. For example, the generating unit can calculate the necessary materials by referring to a material database. This allows the design process to be quickly advanced by automatically generating a CAD design and calculating costs and materials. Some or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate a design using an AI model that inputs a design proposed by the proposing unit and outputs a CAD design.

[0067] The generation unit can use a specific algorithm when generating the blueprint. The generation unit can use, for example, an optimization algorithm when generating the blueprint. For example, the generation unit can optimize the blueprint using AI. The generation unit can also generate the blueprint using a generation algorithm. For example, the generation unit can generate the blueprint using AI. As a result, the accuracy and efficiency of the blueprint are improved by using a specific algorithm. A part or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate the blueprint using an AI model that inputs an optimization algorithm or a generation algorithm and outputs a blueprint.

[0068] The generation unit can calculate the necessary materials and costs based on the database to be used. The generation unit, for example, refers to a material database and calculates the necessary materials. For example, the generation unit can use AI to analyze the material database and calculate the necessary materials. The generation unit can also refer to a cost database and calculate the necessary costs. For example, the generation unit can use AI to analyze the cost database and calculate the necessary costs. This makes it possible to accurately and efficiently calculate materials and costs by utilizing the database. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can calculate materials and costs using an AI model that inputs a material database or a cost database and outputs the necessary materials and costs.

[0069] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is stressed, the analysis unit displays the analysis result in a simple and highly visible format. For example, the analysis unit can estimate the user's emotion using AI and display the analysis result concisely when the user is stressed. In addition, the analysis unit can display detailed analysis results and provide additional information when the user is relaxed. For example, the analysis unit can estimate the user's emotion using AI and display detailed analysis results when the user is relaxed. In addition, the analysis unit can also display concise analysis results with the main points when the user is in a hurry. For example, the analysis unit can estimate the user's emotion using AI and display analysis results with the main points when the user is in a hurry. This provides a display method according to the user's emotion, thereby deepening understanding of the analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the display method using an AI model that inputs the user's emotion data and outputs the display method of the analysis result.

[0070] The analysis unit can compare photos taken under different weather conditions and detect signs of deterioration more accurately. The analysis unit, for example, compares photos taken on sunny days and rainy days to detect the progress of cracks. For example, the analysis unit can use AI to analyze photos taken on sunny days and rainy days to detect the progress of cracks. The analysis unit can also compare photos taken on snowy days and sunny days to detect the progress of rust. For example, the analysis unit can use AI to analyze photos taken on snowy days and sunny days to detect the progress of rust. Furthermore, the analysis unit can compare photos taken on windy days and calm days to detect deformation of the structure. For example, the analysis unit can use AI to analyze photos taken on windy days and calm days to detect deformation of the structure. As a result, signs of deterioration can be detected more accurately by comparing photos taken under different weather conditions. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can detect signs of deterioration using an AI model that takes photos taken under different weather conditions as input and outputs signs of deterioration.

[0071] The analysis unit can predict the degree of deterioration by referring to the past maintenance history of the bridge. The analysis unit predicts the degree of cracking, for example, based on the past maintenance history. For example, the analysis unit can use AI to analyze the past maintenance history and predict the degree of cracking. The analysis unit can also predict the degree of rusting, based on the history of past rust removal work. For example, the analysis unit can use AI to analyze the history of past rust removal work and predict the degree of rusting. Furthermore, the analysis unit can predict the degree of deterioration of the structure, based on the history of past reinforcement work. For example, the analysis unit can use AI to analyze the history of past reinforcement work and predict the degree of deterioration of the structure. As a result, the degree of deterioration can be accurately predicted by referring to the past maintenance history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can predict the degree of deterioration using an AI model that inputs the past maintenance history and outputs the degree of deterioration.

[0072] The analysis unit can estimate the user's emotion and determine the priority of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit can preferentially display the most important analysis result. For example, the analysis unit can estimate the user's emotion using AI and preferentially display the most important analysis result when the user is nervous. In addition, the analysis unit can also sequentially display detailed analysis results when the user is relaxed. For example, the analysis unit can estimate the user's emotion using AI and sequentially display detailed analysis results when the user is relaxed. In addition, the analysis unit can also preferentially display analysis results that focus on the main points when the user is in a hurry. For example, the analysis unit can estimate the user's emotion using AI and preferentially display analysis results that focus on the main points when the user is in a hurry. This allows important analysis results to be provided quickly by setting a priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority order using an AI model that inputs the user's emotion data and outputs the analysis priority order.

[0073] The analysis unit can identify deterioration factors specific to a region based on the geographical location information of the bridge. For example, in the case of a bridge near the coast, the analysis unit identifies deterioration due to salt damage. For example, the analysis unit can use AI to analyze a photo of a bridge near the coast and identify deterioration due to salt damage. In addition, in the case of a bridge in a mountainous area, the analysis unit can also identify cracks caused by freezing. For example, the analysis unit can use AI to analyze a photo of a bridge in a mountainous area and identify cracks caused by freezing. In addition, in the case of a bridge in an urban area, the analysis unit can also identify rust caused by exhaust gas. For example, the analysis unit can use AI to analyze a photo of a bridge in an urban area and identify rust caused by exhaust gas. In this way, by taking into account the geographical location information, it is possible to accurately identify deterioration factors specific to a region. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can identify factors using an AI model that inputs geographical location information of a bridge and outputs deterioration factors specific to a region.

[0074] The analysis unit can identify weak points in the design by comparing with the design drawing of the bridge. The analysis unit, for example, compares the design drawing with an actual photograph and identifies the location of cracks. For example, the analysis unit can use AI to analyze the design drawing with the actual photograph and identify the location of cracks. The analysis unit can also compare the design drawing with the actual photograph and identify the progress of rust. For example, the analysis unit can use AI to analyze the design drawing with the actual photograph and identify the progress of rust. Furthermore, the analysis unit can compare the design drawing with the actual photograph and identify deformation of the structure. For example, the analysis unit can use AI to analyze the design drawing with the actual photograph and identify deformation of the structure. This makes it possible to accurately identify weak points in the design by comparing with the design drawing. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can identify weak points by using an AI model that inputs the design drawing and the actual photograph and outputs weak points in the design.

[0075] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit makes a simple and highly visible proposal. For example, the suggestion unit can estimate the user's emotion using AI and make a simple and highly visible proposal when the user is stressed. In addition, the suggestion unit can make a detailed proposal and provide additional information when the user is relaxed. For example, the suggestion unit can estimate the user's emotion using AI and make a detailed proposal when the user is relaxed. In addition, the suggestion unit can also make a concise proposal that covers the main points when the user is in a hurry. For example, the suggestion unit can estimate the user's emotion using AI and make a proposal that covers the main points when the user is in a hurry. Thereby, by providing a method of expressing the proposal according to the user's emotion, the ease of accepting the proposal is improved. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can adjust the expression method using an AI model that inputs the user's emotion data and outputs a proposed expression method.

[0076] The proposal unit can select an optimal design based on the purpose of use and traffic volume of the bridge. For example, in the case of a highway bridge, the proposal unit proposes a design that emphasizes durability. For example, the proposal unit can use AI to analyze the purpose of use and traffic volume of the highway bridge and propose a design that emphasizes durability. In addition, the proposal unit can also propose a design that emphasizes safety and aesthetics in the case of a pedestrian-only bridge. For example, the proposal unit can use AI to analyze the purpose of use and traffic volume of the pedestrian-only bridge and propose a design that emphasizes safety and aesthetics. In addition, the proposal unit can also propose a design that emphasizes vibration countermeasures in the case of a railway bridge. For example, the proposal unit can use AI to analyze the purpose of use and traffic volume of the railway bridge and propose a design that emphasizes vibration countermeasures. As a result, the functionality and safety of the bridge are improved by selecting a design based on the purpose of use and traffic volume. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can select a design using an AI model that inputs the purpose of use and traffic volume of the bridge and outputs an optimal design.

[0077] The proposal unit can compare the durability and cost of different materials and propose the optimal material. The proposal unit, for example, compares the durability of steel and concrete and proposes the optimal material. For example, the proposal unit can use AI to analyze the durability of steel and concrete and propose the optimal material. The proposal unit can also compare the cost of wood and composite materials and propose the optimal material. For example, the proposal unit can use AI to analyze the cost of wood and composite materials and propose the optimal material. Furthermore, the proposal unit can compare the performance of new materials and conventional materials and propose the optimal material. For example, the proposal unit can use AI to analyze the performance of new materials and conventional materials and propose the optimal material. This makes it possible to select the optimal material by comparing the durability and cost of materials. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can propose materials using an AI model that inputs the durability and cost of different materials and outputs the optimal material.

[0078] The suggestion unit can estimate the user's emotion and determine the priority of the suggestions based on the estimated user's emotion. For example, when the user is nervous, the suggestion unit can preferentially display the most important suggestion. For example, the suggestion unit can estimate the user's emotion using AI and preferentially display the most important suggestion when the user is nervous. In addition, the suggestion unit can sequentially display detailed suggestions when the user is relaxed. For example, the suggestion unit can estimate the user's emotion using AI and sequentially display detailed suggestions when the user is relaxed. In addition, the suggestion unit can preferentially display a suggestion that focuses on the main points when the user is in a hurry. For example, the suggestion unit can estimate the user's emotion using AI and preferentially display a suggestion that focuses on the main points when the user is in a hurry. This allows important suggestions to be provided quickly by setting a priority according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may determine the priority order using an AI model that inputs the user's emotion data and outputs the priority order of suggestions.

[0079] The proposal unit can propose a design that takes into consideration the surrounding environment and scenery of the bridge. For example, in the case of a bridge in a natural park, the proposal unit proposes a design that blends into the surrounding scenery. For example, the proposal unit can use AI to analyze the surrounding environment and scenery of the bridge in the natural park and propose a design that blends into the surrounding scenery. In addition, in the case of a bridge in an urban area, the proposal unit can also propose a design that harmonizes with the surrounding buildings. For example, the proposal unit can use AI to analyze the surrounding environment and scenery of the bridge in the urban area and propose a design that harmonizes with the surrounding buildings. Furthermore, in the case of a bridge in a historical place, the proposal unit can also propose a design that takes into consideration the historical background. For example, the proposal unit can use AI to analyze the surrounding environment and scenery of the bridge in a historical place and propose a design that takes into consideration the historical background. As a result, by proposing a design that takes into consideration the surrounding environment and scenery, harmony with the region can be achieved. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can propose a design using an AI model that inputs the surrounding environment and scenery of the bridge and outputs an optimal design.

[0080] The proposal unit can improve the accuracy of the proposal by referring to the success cases of similar projects in the past. The proposal unit, for example, proposes an optimal design based on the past success cases. For example, the proposal unit can use AI to analyze the success cases of similar projects in the past and propose an optimal design. The proposal unit can also propose a highly safe design by referring to the data of similar projects. For example, the proposal unit can use AI to analyze the data of similar projects and propose a highly safe design. Furthermore, the proposal unit can also propose a cost-efficient design based on the cost data of the success cases. For example, the proposal unit can use AI to analyze the cost data of the success cases and propose a cost-efficient design. As a result, the accuracy of the proposal is improved by referring to the past success cases. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can propose a design using an AI model that inputs the success cases of similar projects in the past and outputs the optimal design.

[0081] The generation unit can estimate the user's emotions and adjust the display method of the generated blueprint based on the estimated user's emotions. For example, when the user is stressed, the generation unit displays a simple and highly visible blueprint. For example, the generation unit can use AI to estimate the user's emotions and display a simple and highly visible blueprint when the user is stressed. In addition, the generation unit can display a detailed blueprint and provide additional information when the user is relaxed. For example, the generation unit can use AI to estimate the user's emotions and display a detailed blueprint when the user is relaxed. In addition, the generation unit can also display a concise blueprint that covers the main points when the user is in a hurry. For example, the generation unit can use AI to estimate the user's emotions and display a blueprint that covers the main points when the user is in a hurry. This provides a display method according to the user's emotions, thereby deepening the understanding of the blueprint. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may adjust the display method using an AI model that receives user emotion data as input and outputs a display method for a blueprint.

[0082] The generating unit can perform detailed structural analysis necessary for designing the bridge and reflect it in the design drawing. The generating unit, for example, analyzes the load distribution of the bridge and reflects it in the design drawing. For example, the generating unit can use AI to analyze the load distribution of the bridge and reflect the result in the design drawing. The generating unit can also analyze the vibration characteristics of the bridge and reflect it in the design drawing. For example, the generating unit can use AI to analyze the vibration characteristics of the bridge and reflect the result in the design drawing. Furthermore, the generating unit can analyze the durability of the bridge and reflect it in the design drawing. For example, the generating unit can use AI to analyze the durability of the bridge and reflect the result in the design drawing. As a result, the accuracy of the design drawing is improved by performing detailed structural analysis. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can use an AI model that inputs data necessary for designing the bridge and outputs detailed structural analysis results to reflect them in the design drawing.

[0083] The generation unit can compare the cost and effect of different design options to select the optimal design. The generation unit, for example, compares the costs of multiple design options and selects the optimal design. For example, the generation unit can use AI to analyze the costs of multiple design options and select the optimal design. The generation unit can also compare the durability of multiple design options and select the optimal design. For example, the generation unit can use AI to analyze the durability of multiple design options and select the optimal design. Furthermore, the generation unit can compare the construction time of multiple design options and select the optimal design. For example, the generation unit can use AI to analyze the construction time of multiple design options and select the optimal design. This allows the optimal design to be selected by comparing the cost and effect. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can select a design using an AI model that inputs data of different design options, compares the cost and effect, and outputs the optimal design.

[0084] The generation unit can estimate the user's emotions and determine the priority of the generated blueprints based on the estimated user's emotions. For example, when the user is nervous, the generation unit can preferentially display the most important blueprint. For example, the generation unit can estimate the user's emotions using AI and preferentially display the most important blueprint when the user is nervous. In addition, the generation unit can sequentially display detailed blueprints when the user is relaxed. For example, the generation unit can estimate the user's emotions using AI and sequentially display detailed blueprints when the user is relaxed. In addition, the generation unit can preferentially display blueprints that highlight the main points when the user is in a hurry. For example, the generation unit can estimate the user's emotions using AI and preferentially display blueprints that highlight the main points when the user is in a hurry. This allows important blueprints to be provided quickly by setting priorities according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may determine priorities using an AI model that receives user emotion data as input and outputs priorities of blueprints.

[0085] The generation unit can automatically check the laws and regulations and standards required for the bridge design and reflect them in the design drawing. The generation unit, for example, checks the Building Standards Act required for the bridge design and reflects them in the design drawing. For example, the generation unit can use AI to analyze the Building Standards Act required for the bridge design and reflect the results in the design drawing. The generation unit can also check the earthquake resistance standards required for the bridge design and reflect them in the design drawing. For example, the generation unit can use AI to analyze the earthquake resistance standards required for the bridge design and reflect the results in the design drawing. Furthermore, the generation unit can check the environmental standards required for the bridge design and reflect them in the design drawing. For example, the generation unit can use AI to analyze the environmental standards required for the bridge design and reflect the results in the design drawing. As a result, the suitability of the design drawing is improved by automatically checking the laws and regulations and standards. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the laws and regulations and standards required for the bridge design and perform the check using an AI model that reflects them in the design drawing.

[0086] The generation unit can perform an environmental impact assessment required for the bridge design and reflect it in the design drawing. The generation unit, for example, performs an environmental impact assessment required for the bridge design and reflects it in the design drawing. For example, the generation unit can use AI to analyze the environmental impact assessment required for the bridge design and reflect the result in the design drawing. The generation unit can also perform a noise impact assessment required for the bridge design and reflect it in the design drawing. For example, the generation unit can use AI to analyze the noise impact assessment required for the bridge design and reflect the result in the design drawing. Furthermore, the generation unit can perform a landscape impact assessment required for the bridge design and reflect it in the design drawing. For example, the generation unit can use AI to analyze the landscape impact assessment required for the bridge design and reflect the result in the design drawing. Thereby, the environmental compatibility of the design drawing is improved by performing the environmental impact assessment. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can perform an evaluation using an AI model that inputs environmental impact assessment data required for the bridge design and reflects it in the design drawing.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The analysis unit can acquire vibration data of the bridge in real time and detect abnormal vibrations. For example, the analysis unit can acquire vibration data from sensors installed on the bridge and detect abnormal vibrations using AI. When abnormal vibrations are detected, the analysis unit can also identify their location and indicate areas that require repairs. Furthermore, the analysis unit can estimate the cause of the abnormal vibrations and propose appropriate countermeasures. This makes it possible to improve the safety of bridges through the analysis of vibration data in real time.

[0089] The proposal unit can analyze the traffic conditions around the bridge and propose an optimal construction schedule. For example, the proposal unit can obtain traffic volume data and use AI to propose optimal construction time periods. The proposal unit can also propose construction plans to minimize traffic congestion. Furthermore, the proposal unit can propose a construction schedule that takes into account the impact on surrounding residents. This makes it possible to propose an efficient construction schedule that takes traffic conditions into account.

[0090] The generation unit can optimize energy efficiency in bridge design. For example, the generation unit can optimize the installation positions of solar panels and generate blueprints that maximize energy efficiency. The generation unit can also optimize the installation positions of wind power generation equipment and generate blueprints that improve energy efficiency. Furthermore, the generation unit can select materials to minimize energy consumption and reflect them in the blueprints. This makes it possible to realize a sustainable bridge design that takes energy efficiency into consideration.

[0091] The analysis unit can estimate the emotions of users of the bridge and identify areas for improvement to improve user satisfaction. For example, the analysis unit can acquire the user's facial expressions and behavioral data and estimate emotions using AI. The analysis unit can also identify areas where the user is dissatisfied and suggest areas for improvement. Furthermore, the analysis unit can present improvement proposals for the bridge design and facilities based on the user's emotion data. In this way, user satisfaction with the bridge can be improved by making improvement proposals based on the user's emotions.

[0092] The proposal unit can propose a design that takes into account evacuation routes in the event of a disaster when designing a bridge. For example, the proposal unit can simulate disaster scenarios such as earthquakes and floods and propose optimal evacuation routes. The proposal unit can also propose a design that optimizes the installation positions of evacuation route signs and guide boards. Furthermore, the proposal unit can also propose structural reinforcement plans to ensure the safety of evacuation routes. This makes it possible to realize a bridge design that takes into account safety in the event of a disaster.

[0093] The generation unit can use environmentally friendly materials in the bridge design. For example, the generation unit can select renewable materials and low-carbon materials and reflect them in the design drawings. The generation unit can also propose construction methods to minimize the environmental load. Furthermore, the generation unit can evaluate the environmental impact throughout the entire life cycle of the bridge and propose an optimal design. This makes it possible to realize an environmentally friendly and sustainable bridge design.

[0094] The analysis unit can estimate the emotions of the bridge user and adjust the lighting of the bridge based on the user's emotions. For example, the analysis unit can obtain the user's emotion data and estimate the emotion using AI. In addition, the analysis unit can provide soft lighting when the user is relaxed. In addition, the analysis unit can provide bright lighting when the user is nervous. In this way, the comfort of the bridge user can be improved by adjusting the lighting according to the user's emotions.

[0095] The Proposal Department can propose designs that reflect the culture and history of the region when designing the bridge. For example, the Proposal Department can propose designs that incorporate the traditional architectural style of the region. The Proposal Department can also propose designs based on the theme of historical events or people of the region. Furthermore, the Proposal Department can also propose designs that incorporate art and crafts of the region. In this way, it is possible to realize a bridge design that respects the culture and history of the region.

[0096] The generation unit can estimate the user's emotions in designing a bridge and adjust the level of detail of the blueprint based on the emotions. For example, when the user is stressed, the generation unit can display a simple, highly visible blueprint. When the user is relaxed, the generation unit can display a detailed blueprint and provide additional information. Furthermore, when the user is in a hurry, the generation unit can display a concise blueprint that focuses on the main points. This provides a method for displaying blueprints according to the user's emotions, thereby deepening the user's understanding of the blueprint.

[0097] The suggestion unit can estimate the user's emotions in bridge design and determine the priority order of suggestions based on the emotions. For example, when the user is nervous, the suggestion unit can preferentially display the most important suggestions. Also, when the user is relaxed, the suggestion unit can sequentially display detailed suggestions. Furthermore, when the user is in a hurry, the suggestion unit can preferentially display suggestions that focus on the main points. In this way, important suggestions can be provided quickly by setting priorities according to the user's emotions.

[0098] The process flow of the second embodiment will be briefly described below.

[0099] Step 1: The analysis unit analyzes the photo and detects signs of deterioration. The analysis unit uses image recognition technology and deep learning to analyze the photo and detects abnormalities such as cracks and rust. For example, the analysis unit can use AI to detect the width and depth of cracks in the photo. It can also detect signs of deterioration such as the spread of rust and discoloration. Furthermore, the analysis unit can use image recognition technology to identify abnormal areas in the photo and obtain their location information. Step 2: The proposal unit proposes design and safety candidates based on the signs of deterioration detected by the analysis unit. The proposal unit proposes optimal designs and safety candidates based on past design data and safety standards. For example, the proposal unit can use AI to select the bridge structure and materials. It can also propose designs that take durability and safety into consideration. It can also refer to past project data to propose optimal design standards. Step 3: The generation unit automatically generates a CAD design based on the design proposed by the proposal unit, and calculates the necessary costs and materials. The generation unit automatically generates a CAD design based on the proposed design. For example, the generation unit can use AI to automatically generate a design for a bridge. It can also calculate the necessary materials and costs. It can calculate the necessary costs based on the unit price of materials and construction costs. It can also calculate the necessary materials and costs based on the database used.

[0100] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0101] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0102] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements including the above-mentioned analysis unit, suggestion 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 analysis unit takes a photo using the camera 42 of the smart device 14, analyzes the photo by the specific processing unit 290 of the data processing device 12, and detects signs of aging. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal designs and safe candidates based on past design data and safety standards. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a CAD design drawing based on the proposed design and calculates the necessary costs and materials. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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, the RAM 30, and the 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.

[0107] 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.

[0108] 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 the voice according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0110] 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 performed in a secure state.

[0111] Fig. 4 shows an example of 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.

[0112] 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.

[0113] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0116] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0117] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0118] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements including the above-mentioned analysis unit, suggestion 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 analysis unit takes a photo using the camera 42 of the smart glasses 214, analyzes the photo by the specific processing unit 290 of the data processing device 12, and detects signs of aging. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal design and a candidate with high safety based on past design data and safety standards. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a CAD design drawing based on the proposed design and calculates the necessary costs and materials. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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, the RAM 30, and the 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.

[0123] 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.

[0124] 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 the voice according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0126] 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 performed in a secure state.

[0127] 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.

[0128] 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.

[0129] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0131] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0132] 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 voice indicating a user input for 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.

[0133] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0134] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements including the above-mentioned analysis unit, proposal 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 analysis unit takes a photo using the camera 42 of the headset type terminal 314, analyzes the photo by the specific processing unit 290 of the data processing device 12, and detects signs of aging. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal design and a candidate with high safety based on past design data and safety standards. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a CAD design drawing based on the proposed design and calculates the necessary costs and materials. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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, the RAM 30, and the 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.

[0139] 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.

[0140] 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 the voice according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0142] 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 performed in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0144] 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.

[0145] 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.

[0146] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0148] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] 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 a voice indicating a user input for 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.

[0150] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0152] Each of the multiple elements including the above-mentioned analysis unit, proposal 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 analysis unit takes a photo using the camera 42 of the robot 414, analyzes the photo by the specific processing unit 290 of the data processing device 12, and detects signs of aging. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal design and a candidate with high safety based on past design data and safety standards. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a CAD design drawing based on the proposed design and calculates the necessary costs and materials. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0154] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0158] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] In addition, 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 upon request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0165] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0169] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

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

[0171] (Appendix 1) An analysis section that analyzes the photos and detects signs of deterioration; a suggestion unit that proposes candidates with high design and safety based on the signs of deterioration detected by the analysis unit; A generation unit is provided for automatically generating a CAD design drawing based on the design proposed by the proposal unit and calculating the necessary costs and materials. A system characterized by: (Appendix 2) The analysis unit is Analyze photos using image recognition technology or deep learning to detect abnormalities such as cracks or rust 2. The system of claim 1, (Appendix 3) The suggestion unit, Suggest optimal designs and safe candidates based on past design data or safety standards 2. The system of claim 1, (Appendix 4) The generation unit is Automatically generate CAD drawings based on proposed designs and calculate costs and materials required 2. The system of claim 1, (Appendix 5) The generation unit is Uses a specific algorithm to generate blueprints 2. The system of claim 1, (Appendix 6) The generation unit is Calculate materials and costs based on the database you use 2. The system of claim 1, (Appendix 7) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 8) The analysis unit is Compare photos taken under different weather conditions to more accurately detect signs of deterioration 2. The system of claim 1, (Appendix 9) The analysis unit is Predict the progression of deterioration by referencing the bridge's past maintenance history 2. The system of claim 1, (Appendix 10) The analysis unit is Estimate user emotions and determine analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The analysis unit is Identify area-specific deterioration factors based on bridge geolocation 2. The system of claim 1, (Appendix 12) The analysis unit is Compare with bridge blueprints to identify design weaknesses 2. The system of claim 1, (Appendix 13) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The suggestion unit, Select the optimal design based on the bridge's intended use and traffic volume 2. The system of claim 1, (Appendix 15) The suggestion unit, Compare the durability and cost of different materials to suggest the most suitable material 2. The system of claim 1, (Appendix 16) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 17) The suggestion unit, Proposing designs that take into consideration the surrounding environment and landscape of the bridge 2. The system of claim 1, (Appendix 18) The suggestion unit, Refer to past success stories of similar projects to improve the accuracy of your proposals 2. The system of claim 1, (Appendix 19) The generation unit is Inferring user emotion and adjusting the display method of the generated blueprint based on the estimated user emotion 2. The system of claim 1, (Appendix 20) The generation unit is Conduct detailed structural analysis required for bridge design and incorporate it into the design drawings 2. The system of claim 1, (Appendix 21) The generation unit is Compare the costs and benefits of different design options to select the optimal design 2. The system of claim 1, (Appendix 22) The generation unit is Estimate user emotions and prioritize generated designs based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The generation unit is Automatically check the laws and regulations required for bridge design and reflect them in the design drawings 2. The system of claim 1, (Appendix 24) The generation unit is Conducting environmental impact assessments required for bridge design and incorporating them into the design drawings 2. The system of claim 1, [Explanation of symbols]

[0172] 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. An analysis section that analyzes the photos and detects signs of deterioration; a suggestion unit that proposes candidates with high design and safety based on the signs of deterioration detected by the analysis unit; A generation unit is provided for automatically generating a CAD design drawing based on the design proposed by the proposal unit and calculating the necessary costs and materials. A system characterized by:

2. The analysis unit is Analyze photos using image recognition technology or deep learning to detect abnormalities such as cracks or rust 2. The system of claim 1 .

3. The suggestion unit, Suggest optimal designs and safe candidates based on past design data or safety standards 2. The system of claim 1 .

4. The generation unit is Calculate materials and costs based on the database you use 2. The system of claim 1 .

5. The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1 .

6. The analysis unit is Compare photos taken under different weather conditions to more accurately detect signs of deterioration 2. The system of claim 1 .

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