System

Abstract

An object of a system according to an embodiment is to efficiently grasp the deterioration status of the appearance of an apartment and take appropriate measures.SOLUTION: A system includes a recording unit, an analysis unit, an instruction unit, and a provision unit. The recording unit records the appearance of the apartment. The analysis unit analyzes the data recorded by the recording unit. The instruction unit instructs an additional imaging point on the basis of a result analyzed by the analysis unit. The provision unit provides the developer with the data obtained by additionally imaging the portion instructed by the instruction unit and the basic information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to efficiently grasp the deterioration state of an apartment building's exterior and take appropriate measures.

[0005] The system according to the embodiment aims to efficiently grasp the deterioration state of the exterior of an apartment building and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, an instruction unit, and a provision unit. The recording unit records the exterior of the apartment building. The analysis unit analyzes the data recorded by the recording unit. The instruction unit indicates additional locations to be photographed based on the results of the analysis by the analysis unit. The provision unit provides the developer with basic information and data of the additional footage of the locations indicated by the instruction unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently grasp the deterioration state of the exterior of an apartment building and take appropriate measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The apartment building reconstruction and seismic reinforcement system according to an embodiment of the present invention records the exterior of an apartment building, analyzes it using a generation AI, indicates additional shooting locations, and provides the collected information to developers. This allows the apartment building reconstruction and seismic reinforcement system to efficiently consider rebuilding or seismic reinforcement of aging apartment buildings.

[0029] An apartment reconstruction and seismic reinforcement system according to an embodiment includes a recording unit, an analysis unit, an instruction unit, and a provision unit. The recording unit records the exterior of the apartment building. For example, the recording unit can record the exterior of the building using a handheld camera or a drone. The recording unit can also capture an aerial image of the entire building. The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit uses a generation AI to analyze the recorded data and automatically identify the building's materials and degree of deterioration. The analysis unit can also evaluate the building's durability by referring to past earthquake data and weather data. The instruction unit instructs additional locations to be photographed based on the results of the analysis by the analysis unit. For example, the instruction unit uses 3D scanning technology to obtain detailed three-dimensional data for the additional locations instructed by the generation AI. The instruction unit can also visualize the interior deterioration using an infrared camera. The provision unit provides the developer with the additional photographed data and basic information for the locations instructed by the instruction unit. For example, the provision unit includes data on the building's energy efficiency and environmental impact in the information provided to the developer by the generation AI. The providing unit also allows the generation AI to automatically create presentation materials to make the proposals visually easier to understand. This allows the apartment building reconstruction and seismic reinforcement system according to the embodiment to efficiently consider rebuilding and seismic reinforcement of aging apartment buildings. For example, the output unit displays the grading results to students and teachers via a web application or mobile application. If students and teachers wish to receive feedback in paper form, the output unit prints the results using a printer. Sending the results via email allows for quick feedback by sending them directly to students and parents.

[0030] When analyzing the recorded data, the analysis unit can automatically identify the building's material and degree of deterioration, and make deterioration predictions for specific materials. For example, the generation AI analyzes the recorded data and automatically identifies the materials of the building's exterior walls and interior. For example, it identifies materials such as concrete, wood, and metal, and evaluates the degree of deterioration for each. The analysis unit also makes deterioration predictions for specific materials. For example, the generation AI predicts the progression of cracks in concrete and evaluates the need for repairs. This makes it possible to automatically identify the degree of deterioration for each building material and make deterioration predictions.

[0031] When analyzing the recorded data, the analysis unit can evaluate the durability of a building by referring to past earthquake data and weather data. For example, the generation AI analyzes the recorded data and evaluates the earthquake resistance of a building by referring to past earthquake data. For example, the analysis unit predicts the durability of a building based on the seismic intensity and frequency of past earthquakes. The analysis unit also evaluates the durability of a building by referring to weather data. For example, it predicts the progression of deterioration of a building based on past rainfall and wind speed. This makes it possible to evaluate the durability of a building by referring to past earthquake data and weather data.

[0032] The recording unit uses a drone to take aerial photographs of the entire building, and the generation AI can analyze the data. For example, the recording unit uses a drone to take aerial photographs of the entire building during initial recording and inputs the data into the generation AI. For example, it obtains detailed images of the building's roof and upper floors. The recording unit also uses a drone to record the building's exterior in detail. For example, the drone flies around the building and captures the entire image. This allows the drone to take aerial photographs of the entire building, and the generation AI to analyze the data.

[0033] The analysis unit can input the building's blueprints and past repair history into the generation AI in addition to the recorded data to perform a more detailed analysis. For example, the analysis unit can input the building's blueprints into the generation AI in addition to the recorded data to perform a more detailed analysis. For example, it can analyze the building's structure based on the blueprints and identify areas of deterioration. The analysis unit can also input past repair history into the generation AI to predict the progression of deterioration. For example, it can evaluate the progression of deterioration based on the areas of past repair. This allows the analysis unit to input the blueprints and repair history into the generation AI in addition to the recorded data to perform a more detailed analysis.

[0034] The instruction unit can use 3D scanning technology to obtain detailed three-dimensional data for additional shooting locations instructed by the generation AI. The instruction unit, for example, uses 3D scanning technology to obtain detailed three-dimensional data for additional shooting locations instructed by the generation AI. For example, cracks in walls and deterioration of ceilings are recorded in detail using 3D scanning. The instruction unit also uses 3D scanning technology to record the internal structure of a building in detail. For example, 3D scanning technology is used to record the deterioration of the interior of a building in detail. This makes it possible to obtain detailed three-dimensional data using 3D scanning technology.

[0035] The instruction unit can use an infrared camera to visualize the state of internal deterioration at additional photography locations instructed by the generation AI. The instruction unit, for example, uses an infrared camera to visualize the state of internal deterioration at additional photography locations instructed by the generation AI. For example, the infrared camera detects internal cracks in walls and deterioration of insulation. The instruction unit also uses an infrared camera to visualize the internal temperature distribution of a building. For example, the infrared camera is used to record the temperature distribution inside the building in detail. This makes it possible to visualize the state of internal deterioration using the infrared camera.

[0036] The instruction unit can provide specific instructions to the photographer in real time using audio guidance when taking additional photos. The instruction unit can provide specific instructions to the photographer in real time using audio guidance when taking additional photos, for example. For example, audio instructions such as "Take a detailed photo of this location" are provided. The instruction unit can also use audio guidance to explain the procedure for taking photos. For example, audio instructions such as "Next, take a photo from this angle" are provided. This makes it possible to provide specific instructions to the photographer in real time using audio guidance.

[0037] The instruction unit allows the generation AI to analyze the shooting data in real time when additional shooting is performed and provide instant feedback. The instruction unit allows the generation AI to analyze the shooting data in real time when additional shooting is performed and provide instant feedback. For example, it provides feedback such as, "This area was not photographed sufficiently." The instruction unit also allows the generation AI to analyze the shooting data in real time and provide instructions on how to improve the shooting. For example, it provides feedback such as, "Please get a little closer and take the photo." This allows the generation AI to analyze the shooting data in real time and provide instant feedback.

[0038] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0039] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0040] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0041] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0042] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0043] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0044] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0045] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0046] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0047] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] When analyzing the recorded data, the analysis unit can automatically identify the building's materials and degree of deterioration, and make deterioration predictions for specific materials. For example, the analysis unit uses the generation AI to analyze the recorded data and automatically identify the materials of the building's exterior walls and interior. For example, it identifies materials such as concrete, wood, and metal, and evaluates the degree of deterioration for each. The analysis unit also makes deterioration predictions for specific materials. For example, the generation AI predicts the progression of cracks in concrete and evaluates the need for repairs. This makes it possible to automatically identify the degree of deterioration for each building material and make deterioration predictions.

[0050] When analyzing the recorded data, the analysis unit can refer to past earthquake data and weather data to evaluate the durability of the building. For example, the analysis unit has the generation AI analyze the recorded data and refer to past earthquake data to evaluate the earthquake resistance of the building. For example, the analysis unit predicts the durability of the building based on the seismic intensity and frequency of past earthquakes. The analysis unit also evaluates the durability of the building by referring to weather data. For example, it predicts the progression of deterioration of the building based on past rainfall and wind speed. This makes it possible to evaluate the durability of a building by referring to past earthquake data and weather data.

[0051] The instruction unit can provide specific instructions to the photographer in real time using audio guidance when taking additional photos. For example, the instruction unit can provide specific instructions to the photographer in real time using audio guidance when taking additional photos. For example, audio instructions such as "Take a detailed photo of this location" are provided. The instruction unit can also use audio guidance to explain the procedure for taking photos. For example, audio instructions such as "Next, take a photo from this angle" are provided. This allows specific instructions to be provided to the photographer in real time using audio guidance.

[0052] The instruction unit allows the generation AI to analyze the shooting data in real time when additional shooting is performed and provide immediate feedback. For example, the instruction unit allows the generation AI to analyze the shooting data in real time when additional shooting is performed and provide immediate feedback. For example, it may provide feedback such as, "This area was not photographed sufficiently." The instruction unit also allows the generation AI to analyze the shooting data in real time and provide instructions on how to improve the shooting. For example, it may provide feedback such as, "Please get a little closer and take the photo." This allows the generation AI to analyze the shooting data in real time and provide immediate feedback.

[0053] The provision unit can include data on the energy efficiency and environmental impact of a building in the information that the generation AI provides to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information that the generation AI provides to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information that the generation AI provides to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. In this way, data on energy efficiency and environmental impact can be included in the information that the generation AI provides to the developer.

[0054] The provision unit can include data on the energy efficiency and environmental impact of a building in the information that the generation AI provides to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information that the generation AI provides to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information that the generation AI provides to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. In this way, data on energy efficiency and environmental impact can be included in the information that the generation AI provides to the developer.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The recording unit records the exterior of the apartment building. For example, the recording unit can record the exterior of the building using a handheld camera or a drone. The recording unit can also take aerial shots of the entire building. Step 2: The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit uses generative AI to analyze the recorded data and automatically identify the building's materials and degree of deterioration. The analysis unit can also refer to past earthquake and weather data to evaluate the building's durability. Step 3: The instruction unit instructs the areas to be photographed based on the results of the analysis by the analysis unit. For example, the instruction unit uses 3D scanning technology to obtain detailed three-dimensional data for the areas to be photographed instructed by the generation AI. The instruction unit can also use an infrared camera to visualize the state of internal deterioration. Step 4: The providing unit provides the developer with additional photographed data and basic information of the areas instructed by the instruction unit. For example, the providing unit can include data on the building's energy efficiency and environmental impact in the information provided to the developer by the generation AI. The providing unit can also have the generation AI automatically create presentation materials to make the proposal visually easier to understand.

[0057] (Example 2) The apartment building reconstruction and seismic reinforcement system according to an embodiment of the present invention records the exterior of an apartment building, analyzes it using a generation AI, indicates additional shooting locations, and provides the collected information to developers. This allows the apartment building reconstruction and seismic reinforcement system to efficiently consider rebuilding or seismic reinforcement of aging apartment buildings.

[0058] An apartment reconstruction and seismic reinforcement system according to an embodiment includes a recording unit, an analysis unit, an instruction unit, and a provision unit. The recording unit records the exterior of the apartment building. For example, the recording unit can record the exterior of the building using a handheld camera or a drone. The recording unit can also capture an aerial image of the entire building. The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit uses a generation AI to analyze the recorded data and automatically identify the building's materials and degree of deterioration. The analysis unit can also evaluate the building's durability by referring to past earthquake data and weather data. The instruction unit instructs additional locations to be photographed based on the results of the analysis by the analysis unit. For example, the instruction unit uses 3D scanning technology to obtain detailed three-dimensional data for the additional locations instructed by the generation AI. The instruction unit can also visualize the interior deterioration using an infrared camera. The provision unit provides the developer with the additional photographed data and basic information for the locations instructed by the instruction unit. For example, the provision unit includes data on the building's energy efficiency and environmental impact in the information provided to the developer by the generation AI. The providing unit also allows the generation AI to automatically create presentation materials to make the proposals visually easier to understand. This allows the apartment building reconstruction and seismic reinforcement system according to the embodiment to efficiently consider rebuilding and seismic reinforcement of aging apartment buildings. For example, the output unit displays the grading results to students and teachers via a web application or mobile application. If students and teachers wish to receive feedback in paper form, the output unit prints the results using a printer. Sending the results via email allows for quick feedback by sending them directly to students and parents.

[0059] When analyzing the recorded data, the analysis unit can automatically identify the building's material and degree of deterioration, and make deterioration predictions for specific materials. For example, the generation AI analyzes the recorded data and automatically identifies the materials of the building's exterior walls and interior. For example, it identifies materials such as concrete, wood, and metal, and evaluates the degree of deterioration for each. The analysis unit also makes deterioration predictions for specific materials. For example, the generation AI predicts the progression of cracks in concrete and evaluates the need for repairs. This makes it possible to automatically identify the degree of deterioration for each building material and make deterioration predictions.

[0060] When analyzing the recorded data, the analysis unit can evaluate the durability of a building by referring to past earthquake data and weather data. For example, the generation AI analyzes the recorded data and evaluates the earthquake resistance of a building by referring to past earthquake data. For example, the analysis unit predicts the durability of a building based on the seismic intensity and frequency of past earthquakes. The analysis unit also evaluates the durability of a building by referring to weather data. For example, it predicts the progression of deterioration of a building based on past rainfall and wind speed. This makes it possible to evaluate the durability of a building by referring to past earthquake data and weather data.

[0061] The analysis unit can use the emotion estimation function to analyze the emotions of residents, identify areas where residents feel anxious, and prioritize analysis of those areas. The analysis unit, for example, uses the emotion estimation function to analyze the emotions of anxiety and worry felt by residents during recording. For example, it identifies areas where residents feel anxious and prioritizes analysis of those areas. The analysis unit also determines the priority of analysis based on the emotions of residents. For example, it prioritizes analysis of areas where residents feel anxious and evaluates the deterioration status in detail. This makes it possible to analyze residents' emotions and prioritize analysis of areas where residents feel anxious.

[0062] The recording unit uses a drone to take aerial photographs of the entire building, and the generation AI can analyze the data. For example, the recording unit uses a drone to take aerial photographs of the entire building during initial recording and inputs the data into the generation AI. For example, it obtains detailed images of the building's roof and upper floors. The recording unit also uses a drone to record the building's exterior in detail. For example, the drone flies around the building and captures the entire image. This allows the drone to take aerial photographs of the entire building, and the generation AI to analyze the data.

[0063] The analysis unit can input the building's blueprints and past repair history into the generation AI in addition to the recorded data to perform a more detailed analysis. For example, the analysis unit can input the building's blueprints into the generation AI in addition to the recorded data to perform a more detailed analysis. For example, it can analyze the building's structure based on the blueprints and identify areas of deterioration. The analysis unit can also input past repair history into the generation AI to predict the progression of deterioration. For example, it can evaluate the progression of deterioration based on the areas of past repair. This allows the analysis unit to input the blueprints and repair history into the generation AI in addition to the recorded data to perform a more detailed analysis.

[0064] The instruction unit can use 3D scanning technology to obtain detailed three-dimensional data for additional shooting locations instructed by the generation AI. The instruction unit, for example, uses 3D scanning technology to obtain detailed three-dimensional data for additional shooting locations instructed by the generation AI. For example, cracks in walls and deterioration of ceilings are recorded in detail using 3D scanning. The instruction unit also uses 3D scanning technology to record the internal structure of a building in detail. For example, 3D scanning technology is used to record the deterioration of the interior of a building in detail. This makes it possible to obtain detailed three-dimensional data using 3D scanning technology.

[0065] The instruction unit can use an infrared camera to visualize the state of internal deterioration at additional photography locations instructed by the generation AI. The instruction unit, for example, uses an infrared camera to visualize the state of internal deterioration at additional photography locations instructed by the generation AI. For example, the infrared camera detects internal cracks in walls and deterioration of insulation. The instruction unit also uses an infrared camera to visualize the internal temperature distribution of a building. For example, the infrared camera is used to record the temperature distribution inside the building in detail. This makes it possible to visualize the state of internal deterioration using the infrared camera.

[0066] The instruction unit uses the emotion estimation function to analyze the emotions felt by the resident when taking the additional photos, and can prioritize photographing areas where the resident feels anxious. The instruction unit, for example, uses the emotion estimation function to analyze the emotions felt by the resident when taking the additional photos, and identifies areas where the resident feels anxious. For example, it prioritizes photographing areas where the resident feels anxious. The instruction unit also determines the priority of photographing based on the resident's emotions. For example, it prioritizes photographing areas where the resident feels anxious, and records the deterioration status in detail. This makes it possible to analyze the resident's emotions and prioritize photographing areas where the resident feels anxious.

[0067] The instruction unit can provide specific instructions to the photographer in real time using audio guidance when taking additional photos. The instruction unit can provide specific instructions to the photographer in real time using audio guidance when taking additional photos, for example. For example, audio instructions such as "Take a detailed photo of this location" are provided. The instruction unit can also use audio guidance to explain the procedure for taking photos. For example, audio instructions such as "Next, take a photo from this angle" are provided. This makes it possible to provide specific instructions to the photographer in real time using audio guidance.

[0068] The instruction unit allows the generation AI to analyze the shooting data in real time when additional shooting is performed and provide instant feedback. The instruction unit allows the generation AI to analyze the shooting data in real time when additional shooting is performed and provide instant feedback. For example, it provides feedback such as, "This area was not photographed sufficiently." The instruction unit also allows the generation AI to analyze the shooting data in real time and provide instructions on how to improve the shooting. For example, it provides feedback such as, "Please get a little closer and take the photo." This allows the generation AI to analyze the shooting data in real time and provide instant feedback.

[0069] The instruction unit can use the emotion estimation function to analyze the emotions felt by the resident when taking the additional photos and suggest a shooting method that will put the resident at ease. The instruction unit can, for example, use the emotion estimation function to analyze the emotions felt by the resident when taking the additional photos and suggest a shooting method that will put the resident at ease. For example, it can suggest shooting from an angle or distance that will put the resident at ease. The instruction unit can also adjust the shooting method based on the resident's emotions. For example, it can suggest a shooting procedure that will put the resident at ease. In this way, it is possible to analyze the resident's emotions and suggest a shooting method that will put the resident at ease.

[0070] The provision unit can use the emotion estimation function to analyze the emotions of residents and provide the developer with a proposal that will reassure residents the most. The provision unit, for example, uses the emotion estimation function to analyze the emotions of residents and provide the developer with a proposal that will reassure residents the most. For example, it proposes repair methods and construction plans that will reassure residents. The provision unit also adjusts the proposal content based on the emotions of residents. For example, it provides a construction schedule and explanation that will reassure residents. In this way, it is possible to analyze the emotions of residents and provide the developer with a proposal that will reassure residents the most.

[0071] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0072] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0073] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0074] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0075] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0076] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0077] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0078] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0079] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0080] The provision unit can include data on the energy efficiency and environmental impact of a building in the information provided by the generation AI to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information provided by the generation AI to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information provided by the generation AI to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. This allows data on energy efficiency and environmental impact to be included in the information provided to the developer.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] When analyzing the recorded data, the analysis unit can automatically identify the building's materials and degree of deterioration, and make deterioration predictions for specific materials. For example, the analysis unit uses the generation AI to analyze the recorded data and automatically identify the materials of the building's exterior walls and interior. For example, it identifies materials such as concrete, wood, and metal, and evaluates the degree of deterioration for each. The analysis unit also makes deterioration predictions for specific materials. For example, the generation AI predicts the progression of cracks in concrete and evaluates the need for repairs. This makes it possible to automatically identify the degree of deterioration for each building material and make deterioration predictions.

[0083] When analyzing the recorded data, the analysis unit can refer to past earthquake data and weather data to evaluate the durability of the building. For example, the analysis unit has the generation AI analyze the recorded data and refer to past earthquake data to evaluate the earthquake resistance of the building. For example, the analysis unit predicts the durability of the building based on the seismic intensity and frequency of past earthquakes. The analysis unit also evaluates the durability of the building by referring to weather data. For example, it predicts the progression of deterioration of the building based on past rainfall and wind speed. This makes it possible to evaluate the durability of a building by referring to past earthquake data and weather data.

[0084] The analysis unit can use the emotion estimation function to analyze the emotions of residents, identify areas where residents feel anxious, and prioritize analysis of those areas. For example, the analysis unit uses the emotion estimation function to analyze the emotions of anxiety and worry felt by residents during recording. For example, it can identify areas where residents feel anxious and prioritize analysis of those areas. The analysis unit also determines the priority of analysis based on the emotions of residents. For example, it can prioritize analysis of areas where residents feel anxious and evaluate the deterioration status in detail. This makes it possible to analyze residents' emotions and prioritize analysis of areas where residents feel anxious.

[0085] The instruction unit uses the emotion estimation function to analyze the emotions felt by residents when taking additional photos, and can prioritize photographing areas where residents feel anxious. For example, the instruction unit uses the emotion estimation function to analyze the emotions felt by residents when taking additional photos, and identify areas where residents feel anxious. For example, the instruction unit prioritizes photographing areas where residents feel anxious. The instruction unit also determines the priority of photographing based on the residents' emotions. For example, the instruction unit prioritizes photographing areas where residents feel anxious, and records the deterioration status in detail. This makes it possible to analyze residents' emotions and prioritize photographing areas where residents feel anxious.

[0086] The instruction unit can provide specific instructions to the photographer in real time using audio guidance when taking additional photos. For example, the instruction unit can provide specific instructions to the photographer in real time using audio guidance when taking additional photos. For example, audio instructions such as "Take a detailed photo of this location" are provided. The instruction unit can also use audio guidance to explain the procedure for taking photos. For example, audio instructions such as "Next, take a photo from this angle" are provided. This allows specific instructions to be provided to the photographer in real time using audio guidance.

[0087] The instruction unit allows the generation AI to analyze the shooting data in real time when additional shooting is performed and provide immediate feedback. For example, the instruction unit allows the generation AI to analyze the shooting data in real time when additional shooting is performed and provide immediate feedback. For example, it may provide feedback such as, "This area was not photographed sufficiently." The instruction unit also allows the generation AI to analyze the shooting data in real time and provide instructions on how to improve the shooting. For example, it may provide feedback such as, "Please get a little closer and take the photo." This allows the generation AI to analyze the shooting data in real time and provide immediate feedback.

[0088] The instruction unit can use the emotion estimation function to analyze the emotions felt by the resident when taking the additional photos and suggest a shooting method that will put the resident at ease. For example, the instruction unit can use the emotion estimation function to analyze the emotions felt by the resident when taking the additional photos and suggest a shooting method that will put the resident at ease. For example, the instruction unit can suggest shooting from an angle or distance that will put the resident at ease. The instruction unit can also adjust the shooting method based on the resident's emotions. For example, the instruction unit can suggest a shooting procedure that will put the resident at ease. In this way, the resident's emotions can be analyzed and a shooting method that will put the resident at ease can be suggested.

[0089] The provision unit can use the emotion estimation function to analyze the emotions of residents and provide the developer with proposals that will reassure residents the most. For example, the provision unit can use the emotion estimation function to analyze the emotions of residents and provide the developer with proposals that will reassure residents the most. For example, the provision unit can propose repair methods and construction plans that will reassure residents. The provision unit can also adjust the content of the proposal based on the emotions of residents. For example, the provision unit can provide a construction schedule and explanations that will reassure residents. In this way, it is possible to analyze the emotions of residents and provide the developer with proposals that will reassure residents the most.

[0090] The provision unit can include data on the energy efficiency and environmental impact of a building in the information that the generation AI provides to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information that the generation AI provides to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information that the generation AI provides to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. In this way, data on energy efficiency and environmental impact can be included in the information that the generation AI provides to the developer.

[0091] The provision unit can include data on the energy efficiency and environmental impact of a building in the information that the generation AI provides to the developer. For example, the provision unit includes data on the energy efficiency of a building in the information that the generation AI provides to the developer. For example, the provision unit evaluates the insulation performance and energy consumption of a building and makes suggestions for improving energy efficiency. The provision unit also includes data on the environmental impact in the information that the generation AI provides to the developer. For example, the provision unit evaluates the CO2 emissions and amount of waste of a building and makes suggestions for reducing the environmental impact. In this way, data on energy efficiency and environmental impact can be included in the information that the generation AI provides to the developer.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The recording unit records the exterior of the apartment building. For example, the recording unit can record the exterior of the building using a handheld camera or a drone. The recording unit can also take aerial shots of the entire building. Step 2: The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit uses generative AI to analyze the recorded data and automatically identify the building's materials and degree of deterioration. The analysis unit can also refer to past earthquake and weather data to evaluate the building's durability. Step 3: The instruction unit instructs the areas to be photographed based on the results of the analysis by the analysis unit. For example, the instruction unit uses 3D scanning technology to obtain detailed three-dimensional data for the areas to be photographed instructed by the generation AI. The instruction unit can also use an infrared camera to visualize the state of internal deterioration. Step 4: The providing unit provides the developer with additional photographed data and basic information of the areas instructed by the instruction unit. For example, the providing unit can include data on the building's energy efficiency and environmental impact in the information provided to the developer by the generation AI. The providing unit can also have the generation AI automatically create presentation materials to make the proposal visually easier to understand.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] 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.

[0120] 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.

[0121] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] 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.

[0136] 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.

[0137] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] 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.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] 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.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A recording unit that records the exterior of the apartment building, an analysis unit that analyzes the data recorded by the recording unit; an instruction unit that instructs an additional imaging location based on the analysis result by the analysis unit; a providing unit that provides the developer with additionally photographed data and basic information of the location instructed by the instructing unit. A system characterized by:

2. The analysis unit When analyzing recorded data, the system automatically identifies the building's materials and the level of deterioration, and predicts deterioration of specific materials.

2. The system of claim 1.

3. The recording unit A drone is used to take aerial photographs of the entire building, and the generative AI analyzes the data.

2. The system of claim 1.

4. The instruction unit The 3D scanning technology is used to obtain detailed three-dimensional data for the additional photography locations indicated by the generation AI.

2. The system of claim 1.

5. The providing unit The information provided by the generating AI to the developer includes the data on the energy efficiency and environmental impact of the building.

2. The system of claim 1.

6. The analysis unit Analyze residents' emotions, identify areas where they feel anxious, and prioritize analysis of those areas.

2. The system of claim 1.

7. The instruction unit Analyze the emotions felt by residents when taking additional photos, and prioritize taking photos of areas that cause anxiety to the residents.

2. The system of claim 1.

8. The providing unit Analyze residents' sentiments and provide the developer with the proposal that is most convincing to the residents.

2. The system of claim 1.

Images