System

Abstract

An object of a system according to an embodiment is to efficiently investigate the current status of vacant houses and wilderness and propose effective utilization.SOLUTION: A system according to an embodiment includes an fact-finding audit unit, a data collecting unit, an analyzing unit, and a suggestion generating unit. The fact-finding audit unit investigates a current state of an empty house or a wasteland using a drone or an automatic traveling vehicle. The data-collection unit collects the fact-finding audit collected by the data-collection unit. The analyzing unit analyzes the information collected by the information collecting unit by AI. The suggestion generation unit generates a maintenance plan and a suggestion for effective use in a generation AI.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 technologies have not been able to efficiently survey the current status of vacant houses and wasteland or propose effective utilization methods, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently investigate the current state of vacant houses and wasteland and propose effective utilization thereof. [Means for solving the problem]

[0006] The system according to the embodiment includes a current status survey unit, a data collection unit, an analysis unit, and a proposal generation unit. The current status survey unit surveys the current status of vacant houses and wasteland using drones or autonomous vehicles. The data collection unit collects the data collected by the current status survey unit. The analysis unit analyzes the data collected by the data collection unit using AI. The proposal generation unit generates maintenance plans and effective utilization proposals using AI based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently investigate the current state of vacant houses and wasteland and make proposals for effective use. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The vacant house and wasteland survey and analysis system according to an embodiment of the present invention is a system that uses AI technology to survey and analyze the current state of vacant houses and wasteland in a region, and based on that, a generation AI makes a maintenance plan and proposes effective use. As a result, the vacant house and wasteland survey and analysis system can efficiently solve the problem of vacant houses and wasteland in the region and provide optimal maintenance plans and proposals for effective use.

[0029] A vacant house and wasteland survey and analysis system according to an embodiment includes a current status survey unit, a data collection unit, an analysis unit, and a proposal generation unit. The current status survey unit surveys the current status of vacant houses and wastelands using drones or autonomous vehicles. For example, drones can collect high-precision video and data from the air. Furthermore, autonomous vehicles can collect detailed data from the ground. The data collection unit collects the data collected by the current status survey unit. For example, it centrally manages data transmitted from drones or autonomous vehicles. Furthermore, the data collection unit can convert the collected data into an appropriate format and store it. The analysis unit uses AI to analyze the data collected by the data collection unit. For example, the AI ​​analyzes video data to evaluate the degree of deterioration of vacant houses and the state of vegetation on wastelands. Furthermore, the AI ​​uses a geographic information system (GIS) to plot the location information of vacant houses and wastelands on a map. The proposal generation unit uses AI to generate maintenance plans and effective utilization proposals based on the data analyzed by the analysis unit. For example, the generative AI can propose optimal maintenance and utilization methods based on current status data for vacant houses and wasteland. This allows the vacant house and wasteland survey and analysis system to efficiently solve the problem of vacant houses and wasteland in the area and provide optimal maintenance plans and effective utilization proposals.

[0030] The current situation assessment department uses sensors mounted on the drone to collect environmental data such as temperature and humidity of vacant houses and wastelands, allowing it to conduct detailed current situation assessments based on environmental conditions. For example, the current situation assessment department may mount temperature and humidity sensors on a drone and fly it over the vacant houses and wastelands. This allows it to collect temperature and humidity data at each location and conduct detailed assessments of the environmental conditions. The current situation assessment department also uses a multispectral camera mounted on the drone to analyze the vegetation status and soil health of the vacant houses and wastelands. This allows it to conduct current situation assessments based on the environmental data. The current situation assessment department also uses gas sensors mounted on the drone to measure the concentration of harmful substances in the air of the vacant houses and wastelands and evaluate environmental risks. This allows it to conduct detailed current situation assessments based on environmental data, enabling it to develop more accurate maintenance plans and propose effective use.

[0031] The current situation assessment department uses a 3D scanner mounted on the autonomous vehicle to generate detailed 3D models of vacant houses and wasteland, allowing for a highly accurate understanding of the physical condition. For example, the current situation assessment department may install a 3D scanner on an autonomous vehicle and drive it around the vacant house or wasteland. This generates detailed 3D models of the building and terrain, allowing for a highly accurate understanding of the physical condition. The current situation assessment department also uses a laser scanner mounted on the autonomous vehicle to scan the internal structure of the vacant house and generate a detailed 3D model. This allows for the identification of building deterioration and damaged areas. The current situation assessment department also combines a camera mounted on the autonomous vehicle with a 3D scanner to generate detailed 3D models of the wasteland's terrain and vegetation, allowing for a highly accurate understanding of environmental changes. By generating detailed 3D models, the physical condition can be accurately understood, enabling more accurate maintenance plans and proposals for effective use.

[0032] In addition to drones and autonomous vehicles, the Current Status Survey Department can also use underwater drones to survey coastal wastelands and vacant waterfront properties. For example, the Current Status Survey Department uses underwater drones to survey the underwater portions of coastal wastelands and vacant waterfront properties and collect detailed data. For example, the department analyzes the condition of underwater structures and ecosystems. The Current Status Survey Department also uses cameras mounted on underwater drones to survey the foundations of vacant waterfront properties and the surrounding water quality to assess environmental risks. The Current Status Survey Department also uses underwater drones to scan the underwater portions of coastal wastelands and generate detailed 3D models. This allows the state of underwater topography and structures to be understood. This makes it possible to use underwater drones to conduct detailed surveys of coastal areas, vacant waterfront properties, and wastelands.

[0033] In addition to collecting data from drones and autonomous vehicles, the current status survey unit can use satellite images to grasp the current status of vacant houses and wastelands over a wide area. The current status survey unit, for example, uses satellite images to grasp the current status of vacant houses and wastelands over a wide area. For example, it analyzes satellite images to evaluate the distribution of vacant houses and the extent of wastelands. The current status survey unit also integrates satellite images with data from drones and autonomous vehicles to conduct a detailed assessment of the current status of vacant houses and wastelands. For example, it collects wide-area data using satellite images and supplements the detailed data with drones and autonomous vehicles. The current status survey unit also uses satellite images to analyze changes in vacant houses and wastelands and grasp the current status by comparing them with past data. For example, it compares past satellite images with current images to identify changes. In this way, the current status of vacant houses and wastelands over a wide area can be grasped by using satellite images.

[0034] The analysis unit uses AI to analyze the historical changes in vacant houses and wastelands from the collected data, making it possible to understand past usage and changes. For example, the analysis unit uses AI to analyze the past usage of vacant houses and wastelands from the collected data. For example, it analyzes past photographs and document data to understand usage history. The analysis unit also uses AI to analyze the changes in vacant houses and wastelands and compares past data with current data. For example, it compares past aerial photographs with current drone footage to identify changes. The analysis unit also uses AI to analyze the historical changes in vacant houses and wastelands and plots past usage and changes on a map. For example, it uses GIS to visualize the change data. In this way, by analyzing historical changes, it is possible to understand past usage and changes, making it possible to create more appropriate maintenance plans and propose more effective use.

[0035] The analysis unit can use AI to compare data on vacant houses and wasteland with data from other regions to identify problems and characteristics unique to the region. For example, the analysis unit uses AI to compare data on vacant houses and wasteland with data from other regions to identify problems unique to the region. For example, it compares the degree of deterioration of vacant houses and the extent of wasteland. The analysis unit also uses AI to integrate data on vacant houses and wasteland with data from other regions to identify characteristics unique to the region. For example, it analyzes the distribution of vacant houses and the usage status of wasteland by region. The analysis unit also uses AI to compare data on vacant houses and wasteland with data from other regions to identify issues unique to the region. For example, it identifies the causes of vacant houses and obstacles to the use of wasteland. By comparing data from other regions, problems and characteristics unique to the region can be identified, enabling more appropriate development plans and effective use proposals.

[0036] The analysis unit can use AI to update data on vacant houses and wastelands in real time, allowing the latest current status to be constantly grasped. For example, the analysis unit uses AI to analyze data collected from drones and self-driving cars in real time, constantly updating the current status of vacant houses and wastelands. The analysis unit also uses AI to collect data from satellite images and sensors in real time, building a system that constantly grasps the current status of vacant houses and wastelands. The analysis unit also uses AI to update data on vacant houses and wastelands in real time, plotting the latest current status on a map. For example, GIS is used to visualize the real-time data. By updating the data in real time, the latest current status can be constantly grasped, enabling more appropriate maintenance plans and effective use proposals.

[0037] The analysis unit can use AI to integrate data on vacant houses and wasteland with other urban planning data and analyze it as part of comprehensive urban planning. For example, the analysis unit uses AI to integrate data on vacant houses and wasteland with other urban planning data and analyze it as part of comprehensive urban planning. For example, it integrates it with traffic data and population data and analyzes it. The analysis unit also uses AI to integrate data on vacant houses and wasteland with other urban planning data to formulate regional development plans. For example, it optimizes the placement of commercial and public facilities. The analysis unit also uses AI to integrate data on vacant houses and wasteland with other urban planning data to formulate urban plans that take environmental protection and sustainable development into consideration. For example, it optimizes the placement of green spaces and energy efficiency. By integrating it with urban planning data, it can be analyzed as part of comprehensive urban planning, enabling more appropriate development plans and effective use proposals.

[0038] The proposal generation unit uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland, and can make the most economically effective proposal. The proposal generation unit, for example, uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland. For example, it analyzes the costs and expected profits required for development and makes the optimal proposal. The proposal generation unit also uses the generation AI to optimize the development plan based on economic data. For example, it makes proposals that take into account the local economic situation and market trends, maximizing the economic effect. The proposal generation unit also uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland in real time, and adjusts proposals based on that data. For example, it makes proposals that minimize economic risks. In this way, it is possible to contribute to regional economic development by simulating the economic impact and making the most economically effective proposal.

[0039] The proposal generation unit can use the generation AI to evaluate the environmental impact of development plans for vacant houses and wasteland and make environmentally friendly proposals. The proposal generation unit, for example, uses the generation AI to evaluate the environmental impact of development plans for vacant houses and wasteland. For example, it analyzes the environmental load and impact on the ecosystem caused by development and makes environmentally friendly proposals. The proposal generation unit also uses the generation AI to optimize development plans based on environmental data. For example, it proposes the use of renewable energy and greening plans to promote environmental protection. The proposal generation unit also uses the generation AI to evaluate the environmental impact of development plans for vacant houses and wasteland in real time and adjust proposals based on that data. For example, it makes proposals to minimize environmental risks. In this way, by evaluating environmental impacts and making environmentally friendly proposals, sustainable regional development becomes possible.

[0040] The proposal generation unit can use the generation AI to compare the development plans for vacant houses and wasteland with successful cases in other regions and propose the optimal development method. For example, the proposal generation unit uses the generation AI to compare the development plans for vacant houses and wasteland with successful cases in other regions and propose the optimal development method. For example, it refers to development methods that have been successful in similar regions. The proposal generation unit also uses the generation AI to learn from successful cases in other regions and optimize the development plan based on that knowledge. For example, it analyzes the factors behind the successful cases and reflects them in the proposal. The proposal generation unit also uses the generation AI to compare the development plans for vacant houses and wasteland with successful cases in other regions and propose the optimal development method in real time. For example, it adjusts the proposal based on data from successful cases. In this way, by comparing with successful cases in other regions, the optimal development method can be proposed and contribute to the development of the region.

[0041] The proposal generation unit can use the generation AI to simulate development plans for vacant houses and wasteland under different scenarios and present multiple options. The proposal generation unit, for example, uses the generation AI to simulate development plans for vacant houses and wasteland under different scenarios and present multiple options. For example, it compares scenarios such as renovation, redevelopment, and nature conservation. The proposal generation unit also uses the generation AI to simulate the economic, environmental, and social impacts of different scenarios and propose the optimal option. For example, it analyzes the costs and effects of each scenario. The proposal generation unit also uses the generation AI to simulate development plans for vacant houses and wasteland under different scenarios in real time and presents multiple options based on that data. For example, it evaluates the risks and benefits of each scenario. This makes it possible to select the optimal development plan by simulating under different scenarios and presenting multiple options.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] In addition to drones and autonomous vehicles, the Current Status Survey Department can also use ground-penetrating radar to investigate the underground structures of vacant houses and wastelands. For example, ground-penetrating radar is used to understand the condition of underground pipes and foundation structures. Ground-penetrating radar is also used to identify underground cavities and buried objects and evaluate the stability of the ground. Ground-penetrating radar is also used to analyze groundwater levels and soil moisture conditions to evaluate environmental risks. By collecting detailed data on underground structures, more accurate maintenance plans and effective utilization proposals can be made.

[0044] In addition to drones and autonomous vehicles, the Current Status Survey Department can use acoustic sensors to survey the sound environment of vacant houses and wastelands. For example, acoustic sensors can be used to measure noise levels around vacant houses and wastelands to conduct environmental assessments. Acoustic sensors can also be used to analyze the acoustic characteristics inside vacant houses and evaluate the state of deterioration of the buildings. Acoustic sensors can also be used to collect sounds from the wasteland ecosystem and evaluate biodiversity. By collecting detailed data on the sound environment, more accurate maintenance plans and effective use proposals can be made.

[0045] In addition to drones and autonomous vehicles, the current situation survey department can use weather sensors to collect weather data on vacant houses and wastelands to conduct detailed assessments of environmental conditions. For example, weather sensors are used to collect data such as temperature, humidity, wind speed, and precipitation around vacant houses and wastelands to assess weather conditions. Weather sensors are also used to collect weather data on vacant houses and wastelands over long periods of time to analyze the effects of seasonal fluctuations and climate change. Weather sensors are also used to integrate weather data on vacant houses and wastelands with other environmental data to conduct comprehensive environmental assessments. This allows for detailed environmental assessments based on weather data, enabling more accurate maintenance plans and effective utilization proposals.

[0046] In addition to drones and autonomous vehicles, the Current Status Survey Department can also use ground-penetrating radar to investigate the underground structures of vacant houses and wastelands. For example, ground-penetrating radar is used to understand the condition of underground pipes and foundation structures. Ground-penetrating radar is also used to identify underground cavities and buried objects and evaluate the stability of the ground. Ground-penetrating radar is also used to analyze groundwater levels and soil moisture conditions to evaluate environmental risks. By collecting detailed data on underground structures, more accurate maintenance plans and effective utilization proposals can be made.

[0047] The analysis unit uses AI to analyze the historical changes in vacant houses and wastelands from the collected data, making it possible to understand past usage and changes. For example, AI is used to analyze the past usage of vacant houses and wastelands from the collected data. For example, past photographs and document data are analyzed to understand usage history. The analysis unit also uses AI to analyze the changes in vacant houses and wastelands and compare past data with current data. For example, it compares past aerial photographs with current drone footage to identify changes. The analysis unit also uses AI to analyze the historical changes in vacant houses and wastelands and plot past usage and changes on a map. For example, it uses GIS to visualize the change data. In this way, by analyzing historical changes, past usage and changes can be understood, making it possible to create more appropriate maintenance plans and propose more effective use.

[0048] The analysis unit can use AI to compare data on vacant houses and wasteland with data from other regions to identify problems and characteristics specific to the region. For example, AI can be used to compare data on vacant houses and wasteland with data from other regions to identify problems specific to the region. For example, the degree of deterioration of vacant houses and the extent of wasteland can be compared. The analysis unit can also use AI to integrate data on vacant houses and wasteland with data from other regions to identify characteristics specific to the region. For example, it can analyze the distribution of vacant houses and the usage status of wasteland by region. The analysis unit can also use AI to compare data on vacant houses and wasteland with data from other regions to identify issues specific to the region. For example, it can identify the causes of vacant houses and obstacles to the use of wasteland. By comparing data from other regions, problems and characteristics specific to the region can be identified, enabling more appropriate development plans and effective use proposals.

[0049] The proposal generation unit uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland, and can make the most economically effective proposal. For example, the generation AI is used to simulate the economic impact of development plans for vacant houses and wasteland. For example, the cost of development and expected profits are analyzed, and the optimal proposal is made. The proposal generation unit also uses the generation AI to optimize the development plan based on economic data. For example, it makes proposals that take into account the local economic situation and market trends, maximizing the economic effect. The proposal generation unit also uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland in real time, and adjusts proposals based on that data. For example, it makes proposals that minimize economic risks. In this way, by simulating the economic impact and making the most economically effective proposal, it is possible to contribute to the economic development of the region.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The Current Status Survey Department will use drones and autonomous vehicles to survey the current status of vacant houses and wastelands. For example, drones will collect high-resolution images and data from the air, while autonomous vehicles will collect detailed data from the ground. Step 2: The Data Collection Department collects the data collected by the Current Status Survey Department. For example, data sent from drones and autonomous vehicles is centrally managed, converted into an appropriate format, and stored. Step 3: The analysis unit uses AI to analyze the data collected by the data collection unit. For example, AI can analyze video data to assess the degree of deterioration of vacant houses and the state of vegetation in wastelands, and then use a geographic information system (GIS) to plot the location information on a map. Step 4: The proposal generation unit uses AI to generate maintenance plans and effective utilization proposals based on the data analyzed by the analysis unit. For example, the AI ​​may propose optimal maintenance and utilization methods based on current data.

[0052] (Example 2) The vacant house and wasteland survey and analysis system according to an embodiment of the present invention is a system that uses AI technology to survey and analyze the current state of vacant houses and wasteland in a region, and based on that, a generation AI makes a maintenance plan and proposes effective use. As a result, the vacant house and wasteland survey and analysis system can efficiently solve the problem of vacant houses and wasteland in the region and provide optimal maintenance plans and proposals for effective use.

[0053] A vacant house and wasteland survey and analysis system according to an embodiment includes a current status survey unit, a data collection unit, an analysis unit, and a proposal generation unit. The current status survey unit surveys the current status of vacant houses and wastelands using drones or autonomous vehicles. For example, drones can collect high-precision video and data from the air. Furthermore, autonomous vehicles can collect detailed data from the ground. The data collection unit collects the data collected by the current status survey unit. For example, it centrally manages data transmitted from drones or autonomous vehicles. Furthermore, the data collection unit can convert the collected data into an appropriate format and store it. The analysis unit uses AI to analyze the data collected by the data collection unit. For example, the AI ​​analyzes video data to evaluate the degree of deterioration of vacant houses and the state of vegetation on wastelands. Furthermore, the AI ​​uses a geographic information system (GIS) to plot the location information of vacant houses and wastelands on a map. The proposal generation unit uses AI to generate maintenance plans and effective utilization proposals based on the data analyzed by the analysis unit. For example, the generative AI can propose optimal maintenance and utilization methods based on current status data for vacant houses and wasteland. This allows the vacant house and wasteland survey and analysis system to efficiently solve the problem of vacant houses and wasteland in the area and provide optimal maintenance plans and effective utilization proposals.

[0054] The current situation assessment department uses sensors mounted on the drone to collect environmental data such as temperature and humidity of vacant houses and wastelands, allowing it to conduct detailed current situation assessments based on environmental conditions. For example, the current situation assessment department may mount temperature and humidity sensors on a drone and fly it over the vacant houses and wastelands. This allows it to collect temperature and humidity data at each location and conduct detailed assessments of the environmental conditions. The current situation assessment department also uses a multispectral camera mounted on the drone to analyze the vegetation status and soil health of the vacant houses and wastelands. This allows it to conduct current situation assessments based on the environmental data. The current situation assessment department also uses gas sensors mounted on the drone to measure the concentration of harmful substances in the air of the vacant houses and wastelands and evaluate environmental risks. This allows it to conduct detailed current situation assessments based on environmental data, enabling it to develop more accurate maintenance plans and propose effective use.

[0055] The current situation assessment department uses a 3D scanner mounted on the autonomous vehicle to generate detailed 3D models of vacant houses and wasteland, allowing for a highly accurate understanding of the physical condition. For example, the current situation assessment department may install a 3D scanner on an autonomous vehicle and drive it around the vacant house or wasteland. This generates detailed 3D models of the building and terrain, allowing for a highly accurate understanding of the physical condition. The current situation assessment department also uses a laser scanner mounted on the autonomous vehicle to scan the internal structure of the vacant house and generate a detailed 3D model. This allows for the identification of building deterioration and damaged areas. The current situation assessment department also combines a camera mounted on the autonomous vehicle with a 3D scanner to generate detailed 3D models of the wasteland's terrain and vegetation, allowing for a highly accurate understanding of environmental changes. By generating detailed 3D models, the physical condition can be accurately understood, enabling more accurate maintenance plans and proposals for effective use.

[0056] The current situation survey unit can use the emotion estimation function to collect the emotions of local residents in real time and investigate their emotional reactions to vacant houses and wastelands. For example, the current situation survey unit uses a drone equipped with the emotion estimation function to analyze the facial expressions and voices of local residents and collect their emotional reactions to vacant houses and wastelands in real time. The current situation survey unit also equips an autonomous vehicle with the emotion estimation function to interview local residents and analyze their emotional reactions to vacant houses and wastelands. The current situation survey unit also uses the emotion estimation function to investigate how local residents feel about vacant houses and wastelands and evaluates the current situation based on that data. By investigating the emotional reactions of local residents, it becomes possible to create maintenance plans and propose effective use that reflect the opinions of residents.

[0057] In addition to drones and autonomous vehicles, the Current Status Survey Department can also use underwater drones to survey coastal wastelands and vacant waterfront properties. For example, the Current Status Survey Department uses underwater drones to survey the underwater portions of coastal wastelands and vacant waterfront properties and collect detailed data. For example, the department analyzes the condition of underwater structures and ecosystems. The Current Status Survey Department also uses cameras mounted on underwater drones to survey the foundations of vacant waterfront properties and the surrounding water quality to assess environmental risks. The Current Status Survey Department also uses underwater drones to scan the underwater portions of coastal wastelands and generate detailed 3D models. This allows the state of underwater topography and structures to be understood. This makes it possible to use underwater drones to conduct detailed surveys of coastal areas, vacant waterfront properties, and wastelands.

[0058] In addition to collecting data from drones and autonomous vehicles, the current status survey unit can use satellite images to grasp the current status of vacant houses and wastelands over a wide area. The current status survey unit, for example, uses satellite images to grasp the current status of vacant houses and wastelands over a wide area. For example, it analyzes satellite images to evaluate the distribution of vacant houses and the extent of wastelands. The current status survey unit also integrates satellite images with data from drones and autonomous vehicles to conduct a detailed assessment of the current status of vacant houses and wastelands. For example, it collects wide-area data using satellite images and supplements the detailed data with drones and autonomous vehicles. The current status survey unit also uses satellite images to analyze changes in vacant houses and wastelands and grasp the current status by comparing them with past data. For example, it compares past satellite images with current images to identify changes. In this way, the current status of vacant houses and wastelands over a wide area can be grasped by using satellite images.

[0059] The current situation survey department can use the emotion estimation function to collect the emotions of local residents while surveying vacant houses and wastelands, and develop a survey plan based on the emotions of the residents. For example, the current situation survey department uses the emotion estimation function to collect the emotions of local residents in real time while surveying vacant houses and wastelands, and develop a survey plan based on that data. For example, it creates a survey plan that reflects the concerns and expectations of residents. The current situation survey department also collects the emotions of local residents during the survey and determines survey priorities based on the emotion data. For example, it prioritizes surveys of areas where residents are highly anxious. The current situation survey department also uses the emotion estimation function to collect the emotions of local residents during the survey and adjusts the survey method based on that data. For example, it adopts a survey method that takes the emotions of residents into consideration. In this way, by developing a survey plan based on the emotions of residents, it becomes possible to conduct a survey that reflects the opinions of residents.

[0060] The analysis unit uses AI to analyze the historical changes in vacant houses and wastelands from the collected data, making it possible to understand past usage and changes. For example, the analysis unit uses AI to analyze the past usage of vacant houses and wastelands from the collected data. For example, it analyzes past photographs and document data to understand usage history. The analysis unit also uses AI to analyze the changes in vacant houses and wastelands and compares past data with current data. For example, it compares past aerial photographs with current drone footage to identify changes. The analysis unit also uses AI to analyze the historical changes in vacant houses and wastelands and plots past usage and changes on a map. For example, it uses GIS to visualize the change data. In this way, by analyzing historical changes, it is possible to understand past usage and changes, making it possible to create more appropriate maintenance plans and propose more effective use.

[0061] The analysis unit can use AI to compare data on vacant houses and wasteland with data from other regions to identify problems and characteristics unique to the region. For example, the analysis unit uses AI to compare data on vacant houses and wasteland with data from other regions to identify problems unique to the region. For example, it compares the degree of deterioration of vacant houses and the extent of wasteland. The analysis unit also uses AI to integrate data on vacant houses and wasteland with data from other regions to identify characteristics unique to the region. For example, it analyzes the distribution of vacant houses and the usage status of wasteland by region. The analysis unit also uses AI to compare data on vacant houses and wasteland with data from other regions to identify issues unique to the region. For example, it identifies the causes of vacant houses and obstacles to the use of wasteland. By comparing data from other regions, problems and characteristics unique to the region can be identified, enabling more appropriate development plans and effective use proposals.

[0062] The analysis unit can use AI to update data on vacant houses and wastelands in real time, allowing the latest current status to be constantly grasped. For example, the analysis unit uses AI to analyze data collected from drones and self-driving cars in real time, constantly updating the current status of vacant houses and wastelands. The analysis unit also uses AI to collect data from satellite images and sensors in real time, building a system that constantly grasps the current status of vacant houses and wastelands. The analysis unit also uses AI to update data on vacant houses and wastelands in real time, plotting the latest current status on a map. For example, GIS is used to visualize the real-time data. By updating the data in real time, the latest current status can be constantly grasped, enabling more appropriate maintenance plans and effective use proposals.

[0063] The analysis unit can use AI to integrate data on vacant houses and wasteland with other urban planning data and analyze it as part of comprehensive urban planning. For example, the analysis unit uses AI to integrate data on vacant houses and wasteland with other urban planning data and analyze it as part of comprehensive urban planning. For example, it integrates it with traffic data and population data and analyzes it. The analysis unit also uses AI to integrate data on vacant houses and wasteland with other urban planning data to formulate regional development plans. For example, it optimizes the placement of commercial and public facilities. The analysis unit also uses AI to integrate data on vacant houses and wasteland with other urban planning data to formulate urban plans that take environmental protection and sustainable development into consideration. For example, it optimizes the placement of green spaces and energy efficiency. By integrating it with urban planning data, it can be analyzed as part of comprehensive urban planning, enabling more appropriate development plans and effective use proposals.

[0064] The analysis unit can use the emotion estimation function to collect the emotional reactions of local residents to the results of the data analysis and provide feedback on the analysis results based on their emotions. For example, the analysis unit uses the emotion estimation function to collect the emotional reactions of local residents to the results of the data analysis in real time and provide feedback based on the data. For example, feedback reflecting the residents' anxieties and expectations is provided. The analysis unit also uses the emotion estimation function to analyze the emotional reactions of local residents to the results of the data analysis and adjust the analysis results based on the emotion data. For example, analysis results that take the residents' emotions into consideration are provided. The analysis unit also uses the emotion estimation function to collect the emotional reactions of local residents to the results of the data analysis and build a feedback system based on the data. For example, improvement suggestions are made based on the residents' emotions. In this way, by collecting the emotional reactions of local residents and providing feedback based on their emotions, analysis results that reflect the residents' opinions can be obtained.

[0065] The proposal generation unit uses the generation AI to predict the emotional reactions of local residents to development plans for vacant houses and wasteland, and can make optimal proposals based on their emotions. The proposal generation unit, for example, uses the generation AI to predict the emotional reactions of local residents to development plans for vacant houses and wasteland. For example, it learns residents' past reaction data and simulates their emotional reactions to proposals. The proposal generation unit also uses the generation AI to optimize the development plan based on the emotional data of local residents. For example, it makes proposals that take residents' emotions into consideration, thereby increasing resident satisfaction. The proposal generation unit also uses the generation AI to predict the emotional reactions of local residents to development plans for vacant houses and wasteland in real time, and adjusts proposals based on that data. For example, it makes flexible proposals based on residents' emotions. In this way, it is possible to predict the emotional reactions of local residents and make optimal proposals based on their emotions, thereby increasing resident satisfaction.

[0066] The proposal generation unit uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland, and can make the most economically effective proposal. The proposal generation unit, for example, uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland. For example, it analyzes the costs and expected profits required for development and makes the optimal proposal. The proposal generation unit also uses the generation AI to optimize the development plan based on economic data. For example, it makes proposals that take into account the local economic situation and market trends, maximizing the economic effect. The proposal generation unit also uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland in real time, and adjusts proposals based on that data. For example, it makes proposals that minimize economic risks. In this way, it is possible to contribute to regional economic development by simulating the economic impact and making the most economically effective proposal.

[0067] The proposal generation unit can use the generation AI to evaluate the environmental impact of development plans for vacant houses and wasteland and make environmentally friendly proposals. The proposal generation unit, for example, uses the generation AI to evaluate the environmental impact of development plans for vacant houses and wasteland. For example, it analyzes the environmental load and impact on the ecosystem caused by development and makes environmentally friendly proposals. The proposal generation unit also uses the generation AI to optimize development plans based on environmental data. For example, it proposes the use of renewable energy and greening plans to promote environmental protection. The proposal generation unit also uses the generation AI to evaluate the environmental impact of development plans for vacant houses and wasteland in real time and adjust proposals based on that data. For example, it makes proposals to minimize environmental risks. In this way, by evaluating environmental impacts and making environmentally friendly proposals, sustainable regional development becomes possible.

[0068] The proposal generation unit can use the generation AI to compare the development plans for vacant houses and wasteland with successful cases in other regions and propose the optimal development method. For example, the proposal generation unit uses the generation AI to compare the development plans for vacant houses and wasteland with successful cases in other regions and propose the optimal development method. For example, it refers to development methods that have been successful in similar regions. The proposal generation unit also uses the generation AI to learn from successful cases in other regions and optimize the development plan based on that knowledge. For example, it analyzes the factors behind the successful cases and reflects them in the proposal. The proposal generation unit also uses the generation AI to compare the development plans for vacant houses and wasteland with successful cases in other regions and propose the optimal development method in real time. For example, it adjusts the proposal based on data from successful cases. In this way, by comparing with successful cases in other regions, the optimal development method can be proposed and contribute to the development of the region.

[0069] The proposal generation unit can use the generation AI to simulate development plans for vacant houses and wasteland under different scenarios and present multiple options. The proposal generation unit, for example, uses the generation AI to simulate development plans for vacant houses and wasteland under different scenarios and present multiple options. For example, it compares scenarios such as renovation, redevelopment, and nature conservation. The proposal generation unit also uses the generation AI to simulate the economic, environmental, and social impacts of different scenarios and propose the optimal option. For example, it analyzes the costs and effects of each scenario. The proposal generation unit also uses the generation AI to simulate development plans for vacant houses and wasteland under different scenarios in real time and presents multiple options based on that data. For example, it evaluates the risks and benefits of each scenario. This makes it possible to select the optimal development plan by simulating under different scenarios and presenting multiple options.

[0070] The proposal generation unit can use the emotion estimation function to collect the emotional reactions of local residents to the development plan or effective use proposal in real time and modify the proposal based on the emotions. The proposal generation unit, for example, uses the emotion estimation function to collect the emotional reactions of local residents to the development plan or effective use proposal in real time and modify the proposal based on the data. For example, it makes a proposal that reflects the residents' anxieties and expectations. The proposal generation unit also uses the emotion estimation function to optimize the development plan based on the emotion data of local residents. For example, it makes a proposal that takes the residents' emotions into consideration and increases resident satisfaction. The proposal generation unit also uses the emotion estimation function to collect the emotional reactions of local residents to the development plan or effective use proposal in real time and adjust the proposal based on the data. For example, it makes a flexible proposal that corresponds to the residents' emotions. In this way, it is possible to increase resident satisfaction by collecting the emotional reactions of local residents in real time and modifying the proposal based on their emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] In addition to drones and autonomous vehicles, the Current Status Survey Department can also use ground-penetrating radar to investigate the underground structures of vacant houses and wastelands. For example, ground-penetrating radar is used to understand the condition of underground pipes and foundation structures. Ground-penetrating radar is also used to identify underground cavities and buried objects and evaluate the stability of the ground. Ground-penetrating radar is also used to analyze groundwater levels and soil moisture conditions to evaluate environmental risks. By collecting detailed data on underground structures, more accurate maintenance plans and effective utilization proposals can be made.

[0073] The current status survey department can use the emotion estimation function to collect the emotions of local residents while surveying vacant houses and wastelands, and develop a survey plan based on the residents' emotions. For example, the emotion estimation function can be used to collect the emotions of local residents in real time while surveying vacant houses and wastelands, and a survey plan can be developed based on that data. For example, a survey plan that reflects the residents' anxieties and expectations can be created. The current status survey department also collects the emotions of local residents during the survey and determines survey priorities based on the emotion data. For example, it can prioritize surveys of areas where residents are highly anxious. The current status survey department also uses the emotion estimation function to collect the emotions of local residents during the survey and adjust the survey method based on that data. For example, it can adopt a survey method that takes the residents' emotions into consideration. This allows for a survey plan to be developed based on the residents' emotions, making it possible to conduct a survey that reflects the residents' opinions.

[0074] In addition to drones and autonomous vehicles, the Current Status Survey Department can use acoustic sensors to survey the sound environment of vacant houses and wastelands. For example, acoustic sensors can be used to measure noise levels around vacant houses and wastelands to conduct environmental assessments. Acoustic sensors can also be used to analyze the acoustic characteristics inside vacant houses and evaluate the state of deterioration of the buildings. Acoustic sensors can also be used to collect sounds from the wasteland ecosystem and evaluate biodiversity. By collecting detailed data on the sound environment, more accurate maintenance plans and effective use proposals can be made.

[0075] The current situation survey department can use the emotion estimation function to collect the emotions of local residents in real time and investigate their emotional reactions to vacant houses and wastelands. For example, a drone equipped with the emotion estimation function can be used to analyze the facial expressions and voices of local residents to collect their emotional reactions to vacant houses and wastelands in real time. The current situation survey department can also equip an autonomous vehicle with the emotion estimation function to interview local residents and analyze their emotional reactions to vacant houses and wastelands. The current situation survey department can also use the emotion estimation function to investigate how local residents feel about vacant houses and wastelands and use that data to evaluate the current situation. By investigating the emotional reactions of local residents, it becomes possible to create maintenance plans and propose effective use that reflect the opinions of residents.

[0076] In addition to drones and autonomous vehicles, the current situation survey department can use weather sensors to collect weather data on vacant houses and wastelands to conduct detailed assessments of environmental conditions. For example, weather sensors are used to collect data such as temperature, humidity, wind speed, and precipitation around vacant houses and wastelands to assess weather conditions. Weather sensors are also used to collect weather data on vacant houses and wastelands over long periods of time to analyze the effects of seasonal fluctuations and climate change. Weather sensors are also used to integrate weather data on vacant houses and wastelands with other environmental data to conduct comprehensive environmental assessments. This allows for detailed environmental assessments based on weather data, enabling more accurate maintenance plans and effective utilization proposals.

[0077] The current status survey department can use the emotion estimation function to collect the emotions of local residents while surveying vacant houses and wastelands, and develop a survey plan based on the residents' emotions. For example, the emotion estimation function can be used to collect the emotions of local residents in real time while surveying vacant houses and wastelands, and a survey plan can be developed based on that data. For example, a survey plan that reflects the residents' anxieties and expectations can be created. The current status survey department also collects the emotions of local residents during the survey and determines survey priorities based on the emotion data. For example, it can prioritize surveys of areas where residents are highly anxious. The current status survey department also uses the emotion estimation function to collect the emotions of local residents during the survey and adjust the survey method based on that data. For example, it can adopt a survey method that takes the residents' emotions into consideration. This allows for a survey plan to be developed based on the residents' emotions, making it possible to conduct a survey that reflects the residents' opinions.

[0078] In addition to drones and autonomous vehicles, the Current Status Survey Department can also use ground-penetrating radar to investigate the underground structures of vacant houses and wastelands. For example, ground-penetrating radar is used to understand the condition of underground pipes and foundation structures. Ground-penetrating radar is also used to identify underground cavities and buried objects and evaluate the stability of the ground. Ground-penetrating radar is also used to analyze groundwater levels and soil moisture conditions to evaluate environmental risks. By collecting detailed data on underground structures, more accurate maintenance plans and effective utilization proposals can be made.

[0079] The analysis unit uses AI to analyze the historical changes in vacant houses and wastelands from the collected data, making it possible to understand past usage and changes. For example, AI is used to analyze the past usage of vacant houses and wastelands from the collected data. For example, past photographs and document data are analyzed to understand usage history. The analysis unit also uses AI to analyze the changes in vacant houses and wastelands and compare past data with current data. For example, it compares past aerial photographs with current drone footage to identify changes. The analysis unit also uses AI to analyze the historical changes in vacant houses and wastelands and plot past usage and changes on a map. For example, it uses GIS to visualize the change data. In this way, by analyzing historical changes, past usage and changes can be understood, making it possible to create more appropriate maintenance plans and propose more effective use.

[0080] The analysis unit can use AI to compare data on vacant houses and wasteland with data from other regions to identify problems and characteristics specific to the region. For example, AI can be used to compare data on vacant houses and wasteland with data from other regions to identify problems specific to the region. For example, the degree of deterioration of vacant houses and the extent of wasteland can be compared. The analysis unit can also use AI to integrate data on vacant houses and wasteland with data from other regions to identify characteristics specific to the region. For example, it can analyze the distribution of vacant houses and the usage status of wasteland by region. The analysis unit can also use AI to compare data on vacant houses and wasteland with data from other regions to identify issues specific to the region. For example, it can identify the causes of vacant houses and obstacles to the use of wasteland. By comparing data from other regions, problems and characteristics specific to the region can be identified, enabling more appropriate development plans and effective use proposals.

[0081] The proposal generation unit uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland, and can make the most economically effective proposal. For example, the generation AI is used to simulate the economic impact of development plans for vacant houses and wasteland. For example, the cost of development and expected profits are analyzed, and the optimal proposal is made. The proposal generation unit also uses the generation AI to optimize the development plan based on economic data. For example, it makes proposals that take into account the local economic situation and market trends, maximizing the economic effect. The proposal generation unit also uses the generation AI to simulate the economic impact of development plans for vacant houses and wasteland in real time, and adjusts proposals based on that data. For example, it makes proposals that minimize economic risks. In this way, by simulating the economic impact and making the most economically effective proposal, it is possible to contribute to the economic development of the region.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The Current Status Survey Department will use drones and autonomous vehicles to survey the current status of vacant houses and wastelands. For example, drones will collect high-resolution images and data from the air, while autonomous vehicles will collect detailed data from the ground. Step 2: The Data Collection Department collects the data collected by the Current Status Survey Department. For example, data sent from drones and autonomous vehicles is centrally managed, converted into an appropriate format, and stored. Step 3: The analysis unit uses AI to analyze the data collected by the data collection unit. For example, AI can analyze video data to assess the degree of deterioration of vacant houses and the state of vegetation in wastelands, and then use a geographic information system (GIS) to plot the location information on a map. Step 4: The proposal generation unit uses AI to generate maintenance plans and effective utilization proposals based on the data analyzed by the analysis unit. For example, the AI ​​may propose optimal maintenance and utilization methods based on current data.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. The Current Status Survey Department uses drones and self-driving cars to investigate the current status of vacant houses and wastelands, and a data collection unit that collects data collected by the current status survey unit; an analysis unit that analyzes the data collected by the data collection unit using AI; and a proposal generation unit that generates a maintenance plan and an effective utilization proposal using a generation AI based on the data analyzed by the analysis unit. A system characterized by:

2. The current situation investigation department Using sensors mounted on the drone, environmental data such as temperature and humidity of the vacant house and wasteland are collected, and a detailed current situation assessment is performed based on the environmental conditions.

2. The system of claim 1.

3. The current situation investigation department A 3D scanner mounted on the self-driving vehicle is used to generate detailed 3D models of the vacant house and wasteland, allowing for a highly accurate understanding of their physical condition.

2. The system of claim 1.

4. The current situation investigation department Collect real-time sentiment from local residents to explore their emotional reactions to the vacant house or wasteland.

2. The system of claim 1.

5. The current situation investigation department In addition to the drones and autonomous vehicles, underwater drones will be used to survey the wastelands along the coast and the vacant houses near the water.

2. The system of claim 1.

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