System

Abstract

To provide a system capable of accurately presenting the present value of real estate and the prospect of future value improvement.SOLUTION: The system includes an input part, a map information acquisition part, an analysis part, a city development policy information acquisition part, a real estate transaction information acquisition part, a value calculation part, and a value improvement prospect presentation part. The input unit inputs a current photograph and address information of a real estate. The map information acquisition unit acquires map information based on the address information. The analysis unit analyzes the map information. The urban development policy information acquisition unit acquires urban development policy information of a local government. The analysis part analyzes city development policy information. The real estate transaction information acquisition unit acquires neighboring real estate transaction information. The analysis unit analyzes the real estate transaction information. The value calculation part calculates the present value of the real estate based on map information, city development policy information and real estate transaction information. The value improvement prospect presentation unit presents a prospect of a future value improvement based on the current value.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to accurately grasp the current value of real estate and the outlook for future value increases.

[0005] The system according to the embodiment aims to accurately present the current value of real estate and an outlook for future value increases. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a map information acquisition unit, an analysis unit, an urban development policy information acquisition unit, a real estate transaction information acquisition unit, a value calculation unit, and a value increase outlook presentation unit. The input unit inputs current photos and address information of the real estate. The map information acquisition unit acquires map information based on the address information input by the input unit. The analysis unit analyzes the map information acquired by the map information acquisition unit. The urban development policy information acquisition unit acquires urban development policy information of the local government. The analysis unit analyzes the urban development policy information acquired by the urban development policy information acquisition unit. The real estate transaction information acquisition unit acquires nearby real estate transaction information. The analysis unit analyzes the real estate transaction information acquired by the real estate transaction information acquisition unit. The value calculation unit calculates the current value of the real estate based on the map information, urban development policy information, and real estate transaction information analyzed by the analysis unit. The value increase outlook presentation unit presents an outlook for future value increase based on the current value calculated by the value calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately present the current value of real estate and a forecast of future value increases. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The AI ​​system according to an embodiment of the present invention is a system that, by inputting a current photograph and address information of a property, displays the current value of the property and an outlook for future value increases by combining map information, information on urban development policies of local governments, information on neighboring real estate transactions, etc. This enables the AI ​​system to make quick and accurate value judgments when purchasing or investing in real estate.

[0029] The AI ​​system according to the embodiment includes an input unit, a map information acquisition unit, an urban development policy information acquisition unit, a real estate transaction information acquisition unit, a value calculation unit, and a value increase forecast presentation unit. The input unit inputs a current photo of the property and address information. For example, the system begins analysis when a user inputs a photo taken with a smartphone and its address. The map information acquisition unit acquires map information based on the address information input by the input unit. For example, it identifies the location of the nearest train station, bus stop, park, commercial facility, etc. The urban development policy information acquisition unit acquires urban development policy information from a local government. For example, it collects information on construction plans for new roads and public facilities, redevelopment projects, etc. The real estate transaction information acquisition unit acquires information on nearby real estate transactions. For example, it identifies recent trends in sales and rental prices within the same area. The value calculation unit calculates the current value of the property based on the map information, urban development policy information, and real estate transaction information. For example, it evaluates the current market value based on the surrounding environment and transaction information. The value increase forecast presentation unit presents a forecast of future value increase based on the current value calculated by the value calculation unit. For example, if a redevelopment project is underway, the system will show a forecast that the value is likely to increase in the future. This allows the AI ​​system according to the embodiment to show the current value of real estate and the forecast of future value increases.

[0030] The input unit uses a generation AI to perform image recognition on photos entered by the user, and can automatically analyze the condition of the building and details of the surrounding environment. For example, when a user uploads a current photo of a property taken with a smartphone to the system, the generation AI performs image recognition and automatically analyzes the building's appearance and surrounding environment. For example, it analyzes in detail the degree of deterioration of the building and the presence or absence of green space in the surrounding area. This makes it possible to automatically analyze the condition of a building and details of the surrounding environment based on the photos entered by the user.

[0031] The input unit allows for voice input when entering address information, allowing the system to automatically recognize the address simply by the user speaking it. For example, when a user speaks an address, the system automatically converts the address information into text using voice recognition technology and begins analysis. For example, if a user speaks "1-1-1 Jinnan, Shibuya-ku, Tokyo," the system will accurately recognize the address. This allows the system to automatically recognize addresses simply by the user speaking them.

[0032] The input unit may have a function of automatically taking current state photos of real estate using a drone and uploading them directly to the system. The input unit provides a function of automatically taking current state photos of real estate using a drone and uploading them directly to the system. For example, a user operates a drone to take photos from above the real estate and uploads the photos to the system. This allows current state photos of real estate to be automatically taken using a drone and uploaded directly to the system.

[0033] The input unit can automatically acquire past seasonal photos for photos input by the user and perform analysis taking seasonal variations into account. For example, the input unit can automatically acquire past seasonal photos for current real estate photos input by the user and perform analysis taking seasonal variations into account. For example, photos from spring, summer, autumn, and winter are compared and seasonal changes in scenery are analyzed. This allows past seasonal photos to be automatically acquired based on photos input by the user and analysis taking seasonal variations into account.

[0034] The map information acquisition unit can use the generation AI to analyze past geographical changes and predict future changes. The map information acquisition unit, for example, uses the generation AI to analyze past geographical changes to map information and predict future changes. For example, it analyzes past land use changes and demographic trends and predicts future land use changes. This makes it possible to analyze past geographical changes and predict future changes.

[0035] The map information acquisition unit converts map information into a 3D model and provides it in a form that is visually easy for the user to understand. The map information acquisition unit, for example, converts map information into a 3D model and provides it in a form that is visually easy for the user to understand. For example, buildings and terrain may be displayed in 3D, allowing the user to freely change the viewpoint. This allows the map information to be converted into a 3D model and provided in a form that is visually easy for the user to understand.

[0036] The map information acquisition unit can integrate real-time traffic information and weather information with the map information and perform detailed analysis. The map information acquisition unit, for example, integrates real-time traffic information with the map information to enable the user to understand the current traffic situation. For example, congestion information and the operation status of public transportation are displayed on the map. This allows real-time traffic information and weather information to be integrated with the map information and perform detailed analysis.

[0037] The map information acquisition unit can display map information using augmented reality (AR) technology, allowing the user to visually confirm the location. The map information acquisition unit, for example, displays map information using augmented reality (AR) technology, allowing the user to visually confirm the location. For example, information about local buildings and facilities is displayed using AR via a smartphone camera. This allows the map information to be displayed using augmented reality (AR) technology, allowing the user to visually confirm the location.

[0038] The urban development policy information acquisition unit uses generative AI to analyze past policies and their results, and can predict the impact of future policies. For example, the urban development policy information acquisition unit uses generative AI to analyze a local government's past urban development policies and their results, and predict the impact of future policies. For example, it analyzes examples of past successes and failures of redevelopment projects, and predicts the probability of success for future projects. This makes it possible to analyze past policies and their results, and predict the impact of future policies.

[0039] The urban development policy information acquisition unit can visualize urban development policy information to enable users to intuitively understand it. The urban development policy information acquisition unit can visualize, for example, urban development policy information of a local government to enable users to intuitively understand it. For example, the progress of a policy and details of a plan can be displayed in graphs and charts. This makes it possible to visualize urban development policy information to enable users to intuitively understand it.

[0040] The urban development policy information acquisition unit can visualize urban development policy information to enable users to intuitively understand it. The urban development policy information acquisition unit can visualize, for example, urban development policy information of a local government to enable users to intuitively understand it. For example, the progress of a policy and details of a plan can be displayed in graphs and charts. This makes it possible to visualize urban development policy information to enable users to intuitively understand it.

[0041] The urban development policy information acquisition unit can integrate success stories from other municipalities and present best practices. For example, the urban development policy information acquisition unit can integrate success stories from other municipalities into the municipality's urban development policy information and present best practices. For example, it can refer to examples of successful redevelopment projects in other municipalities. This makes it possible to integrate success stories from other municipalities and present best practices.

[0042] The urban development policy information acquisition unit can customize information based on the user's interests and provide individually optimized information. The urban development policy information acquisition unit, for example, customizes urban development policy information of a local government based on the user's interests and provides individually optimized information. For example, information related to areas or projects in which the user is interested is preferentially displayed. This makes it possible to customize information based on the user's interests and provide individually optimized information.

[0043] The real estate transaction information acquisition unit can use the generation AI to analyze past transaction data and predict future transaction prices. The real estate transaction information acquisition unit, for example, uses the generation AI to analyze past transaction data for nearby real estate transaction information and predict future transaction prices. For example, it analyzes trends in past transaction prices and predicts future price fluctuations. This makes it possible to analyze past transaction data and predict future transaction prices.

[0044] The real estate transaction information acquisition unit can visualize real estate transaction information to enable a user to intuitively understand it. The real estate transaction information acquisition unit can visualize, for example, information on nearby real estate transactions to enable a user to intuitively understand it. For example, the unit can display fluctuations in transaction prices in graphs and charts. This allows the real estate transaction information to be visualized to enable a user to intuitively understand it.

[0045] The real estate transaction information acquisition unit can integrate transaction data from other regions and make comparisons between regions. The real estate transaction information acquisition unit can, for example, integrate transaction data from other regions with neighboring real estate transaction information and make comparisons between regions. For example, it can compare transaction prices in different regions within the same city. This allows the integration of transaction data from other regions and makes comparisons between regions.

[0046] The real estate transaction information acquisition unit can customize based on the user's interests and provide individually optimized information. The real estate transaction information acquisition unit, for example, customizes real estate transaction information based on the user's interests and provides individually optimized information. For example, transaction information related to a price range or property type that the user is interested in is preferentially displayed. This allows the information to be customized based on the user's interests and provided individually optimized information.

[0047] The value calculation unit uses the generation AI to analyze past market data and perform highly accurate evaluations. The value calculation unit, for example, uses the generation AI to analyze past market data and perform more accurate evaluations in calculating current value. For example, it analyzes past trading prices and market trends and evaluates current market value. This allows for analyzing past market data and performing highly accurate evaluations.

[0048] The value calculation unit can visualize the calculation result of the present value so that the user can intuitively understand it. The value calculation unit, for example, visualizes the calculation result of the present value so that the user can intuitively understand it. For example, the calculation result is displayed in a graph or chart. In this way, the calculation result of the present value can be visualized so that the user can intuitively understand it.

[0049] The value calculation unit can integrate other evaluation criteria (for example, environmental evaluation and social evaluation) and perform a comprehensive value evaluation. For example, in calculating the present value, the value calculation unit integrates other evaluation criteria (for example, environmental evaluation and social evaluation) and performs a comprehensive value evaluation. For example, a comprehensive evaluation is performed that takes into account the environmental evaluation and social evaluation. This makes it possible to integrate other evaluation criteria and perform a comprehensive value evaluation.

[0050] The value calculation unit can customize the calculation result of the present value based on the user's interests and provide individually optimized information. The value calculation unit, for example, customizes the calculation result of the present value based on the user's interests and provides individually optimized information. For example, calculation results related to a price range or property type in which the user is interested are preferentially displayed. This allows the calculation result of the present value to be customized based on the user's interests and provide individually optimized information.

[0051] The value increase forecast presentation unit can analyze past data using the generation AI and predict future value increases. The value increase forecast presentation unit, for example, analyzes past data using the generation AI and predicts future value increases in the future value increase forecast. For example, it analyzes past market trends and policy information and predicts future value increases. In this way, it is possible to analyze past data and predict future value increases.

[0052] The value increase forecast presentation unit can visualize the forecast of future value increase, allowing the user to intuitively understand. The value increase forecast presentation unit, for example, visualizes the forecast of future value increase, allowing the user to intuitively understand. For example, the forecast results are displayed in graphs or charts. This makes it possible to visualize the forecast of future value increase, allowing the user to intuitively understand.

[0053] The value increase forecast presentation unit can integrate data from other regions and markets and make comparisons between regions. For example, in the forecast of future value increase, the value increase forecast presentation unit can integrate data from other regions and markets and make comparisons between regions. For example, it can compare the forecast of future value increase in different regions within the same city. This allows data from other regions and markets to be integrated and comparisons between regions to be made.

[0054] The value increase forecast presentation unit can customize the future value increase forecast based on the user's interests and provide individually optimized information. The value increase forecast presentation unit, for example, customizes the future value increase forecast based on the user's interests and provides individually optimized information. For example, forecasts related to a price range or property type in which the user is interested are preferentially displayed. This makes it possible to customize the future value increase forecast based on the user's interests and provide individually optimized information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The input unit can automatically acquire past weather data for a photo input by a user and perform an analysis that takes weather fluctuations into account. For example, the system automatically acquires past weather data for a current photo of real estate input by a user and performs an analysis that takes weather fluctuations into account. This allows past weather data to be automatically acquired based on a photo input by a user and an analysis that takes weather fluctuations into account.

[0057] The map information acquisition unit can integrate real-time disaster information with map information and perform detailed analysis. For example, real-time disaster information can be integrated with map information to enable the user to understand the current disaster situation. For example, disaster information such as earthquakes and floods can be displayed on a map. This allows real-time disaster information to be integrated with map information and perform detailed analysis.

[0058] The urban development policy information acquisition unit can integrate the failure cases of other municipalities and provide information for risk avoidance. For example, it can integrate the failure cases of other municipalities into the municipality's urban development policy information and provide information for risk avoidance. For example, it can refer to examples of redevelopment projects that have failed in the past. This makes it possible to integrate the failure cases of other municipalities and provide information for risk avoidance.

[0059] The real estate transaction information acquisition unit can customize the real estate transaction information based on the user's interests and provide individually optimized information. For example, the real estate transaction information can be customized based on the user's interests and provide individually optimized information. For example, transaction information related to a price range or property type that the user is interested in can be preferentially displayed. This allows the information to be customized based on the user's interests and provide individually optimized information.

[0060] The value calculation unit can integrate other evaluation criteria (e.g., environmental evaluation and social evaluation) to perform a comprehensive value evaluation. For example, in calculating the present value, other evaluation criteria (e.g., environmental evaluation and social evaluation) are integrated to perform a comprehensive value evaluation. For example, a comprehensive evaluation is performed that takes into account the environmental evaluation and social evaluation. This allows the other evaluation criteria to be integrated to perform a comprehensive value evaluation.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The input section inputs a photo of the property's current condition and address information. For example, the user can input a photo taken with their smartphone and the address, and the system will begin its analysis. Step 2: The map information acquisition unit acquires map information based on the address information input by the input unit, such as the location of the nearest train station, bus stop, park, commercial facility, etc. Step 3: The Urban Development Policy Information Acquisition Department acquires information on urban development policies of local governments, such as construction plans for new roads and public facilities, and redevelopment projects. Step 4: The real estate transaction information acquisition unit acquires information on nearby real estate transactions, for example, by identifying recent trends in sales and rental prices within the same area. Step 5: The analysis unit analyzes the information acquired by the map information acquisition unit, urban development policy information acquisition unit, and real estate transaction information acquisition unit. This allows for a detailed analysis of each piece of information. Step 6: The value calculation unit calculates the current value of the property based on the map information, urban development policy information, and real estate transaction information analyzed by the analysis unit. For example, the current market value is evaluated based on the surrounding environment and transaction information. Step 7: The value increase forecast presentation unit presents a forecast of future value increase based on the present value calculated by the value calculation unit. For example, if a redevelopment project is underway, the forecast shows that the value is likely to increase in the future.

[0063] (Example 2) The AI ​​system according to an embodiment of the present invention is a system that, by inputting a current photograph and address information of a property, displays the current value of the property and an outlook for future value increases by combining map information, information on urban development policies of local governments, information on neighboring real estate transactions, etc. This enables the AI ​​system to make quick and accurate value judgments when purchasing or investing in real estate.

[0064] The AI ​​system according to the embodiment includes an input unit, a map information acquisition unit, an urban development policy information acquisition unit, a real estate transaction information acquisition unit, a value calculation unit, and a value increase forecast presentation unit. The input unit inputs a current photo of the property and address information. For example, the system begins analysis when a user inputs a photo taken with a smartphone and its address. The map information acquisition unit acquires map information based on the address information input by the input unit. For example, it identifies the location of the nearest train station, bus stop, park, commercial facility, etc. The urban development policy information acquisition unit acquires urban development policy information from a local government. For example, it collects information on construction plans for new roads and public facilities, redevelopment projects, etc. The real estate transaction information acquisition unit acquires information on nearby real estate transactions. For example, it identifies recent trends in sales and rental prices within the same area. The value calculation unit calculates the current value of the property based on the map information, urban development policy information, and real estate transaction information. For example, it evaluates the current market value based on the surrounding environment and transaction information. The value increase forecast presentation unit presents a forecast of future value increase based on the current value calculated by the value calculation unit. For example, if a redevelopment project is underway, the system will show a forecast that the value is likely to increase in the future. This allows the AI ​​system according to the embodiment to show the current value of real estate and the forecast of future value increases.

[0065] The input unit uses a generation AI to perform image recognition on photos entered by the user, and can automatically analyze the condition of the building and details of the surrounding environment. For example, when a user uploads a current photo of a property taken with a smartphone to the system, the generation AI performs image recognition and automatically analyzes the building's appearance and surrounding environment. For example, it analyzes in detail the degree of deterioration of the building and the presence or absence of green space in the surrounding area. This makes it possible to automatically analyze the condition of a building and details of the surrounding environment based on the photos entered by the user.

[0066] The input unit allows for voice input when entering address information, allowing the system to automatically recognize the address simply by the user speaking it. For example, when a user speaks an address, the system automatically converts the address information into text using voice recognition technology and begins analysis. For example, if a user speaks "1-1-1 Jinnan, Shibuya-ku, Tokyo," the system will accurately recognize the address. This allows the system to automatically recognize addresses simply by the user speaking them.

[0067] The input unit uses an emotion estimation function to analyze the emotion of the user when inputting information, and can make suggestions to simplify the input process if the user is feeling stressed. For example, when the user inputs address information, the input unit uses a camera or microphone to analyze the user's facial expression and tone of voice to determine whether the user is feeling stressed. For example, if the user is feeling impatient, the system simplifies the input process. This makes it possible to make suggestions to simplify the input process if the user is feeling stressed.

[0068] The input unit may have a function of automatically taking current state photos of real estate using a drone and uploading them directly to the system. The input unit provides a function of automatically taking current state photos of real estate using a drone and uploading them directly to the system. For example, a user operates a drone to take photos from above the real estate and uploads the photos to the system. This allows current state photos of real estate to be automatically taken using a drone and uploaded directly to the system.

[0069] The input unit can automatically acquire past seasonal photos for photos input by the user and perform analysis taking seasonal variations into account. For example, the input unit can automatically acquire past seasonal photos for current real estate photos input by the user and perform analysis taking seasonal variations into account. For example, photos from spring, summer, autumn, and winter are compared and seasonal changes in scenery are analyzed. This allows past seasonal photos to be automatically acquired based on photos input by the user and analysis taking seasonal variations into account.

[0070] The input unit can use the emotion estimation function to monitor the user's emotions in real time when they are inputting information, and provide an interface for eliciting positive emotions. For example, when a user inputs a current photo of a property or address information, the input unit uses a camera and microphone to monitor the user's emotions in real time, and provide an interface for eliciting positive emotions. For example, when the user inputs information with a smile, the system displays a compliment. This makes it possible to monitor the user's emotions in real time when they are inputting information, and provide an interface for eliciting positive emotions.

[0071] The map information acquisition unit can use the generation AI to analyze past geographical changes and predict future changes. The map information acquisition unit, for example, uses the generation AI to analyze past geographical changes to map information and predict future changes. For example, it analyzes past land use changes and demographic trends and predicts future land use changes. This makes it possible to analyze past geographical changes and predict future changes.

[0072] The map information acquisition unit converts map information into a 3D model and provides it in a form that is visually easy for the user to understand. The map information acquisition unit, for example, converts map information into a 3D model and provides it in a form that is visually easy for the user to understand. For example, buildings and terrain may be displayed in 3D, allowing the user to freely change the viewpoint. This allows the map information to be converted into a 3D model and provided in a form that is visually easy for the user to understand.

[0073] The map information acquisition unit uses the emotion estimation function to analyze the emotion of the user when viewing map information, and can prioritize displaying information that attracts the user's interest. For example, when the user views map information, the map information acquisition unit uses a camera or microphone to analyze the user's emotion in real time, and prioritize displaying information that attracts the user's interest. For example, the map information acquisition unit highlights locations in which the user has shown interest. This allows the emotion of the user when viewing map information to be analyzed, and information that attracts the user's interest to be prioritized.

[0074] The map information acquisition unit can integrate real-time traffic information and weather information with the map information and perform detailed analysis. The map information acquisition unit, for example, integrates real-time traffic information with the map information to enable the user to understand the current traffic situation. For example, congestion information and the operation status of public transportation are displayed on the map. This allows real-time traffic information and weather information to be integrated with the map information and perform detailed analysis.

[0075] The map information acquisition unit can display map information using augmented reality (AR) technology, allowing the user to visually confirm the location. The map information acquisition unit, for example, displays map information using augmented reality (AR) technology, allowing the user to visually confirm the location. For example, information about local buildings and facilities is displayed using AR via a smartphone camera. This allows the map information to be displayed using augmented reality (AR) technology, allowing the user to visually confirm the location.

[0076] The map information acquisition unit can use the emotion estimation function to monitor the user's emotions in real time when viewing map information and provide an interface for eliciting positive emotions. For example, when a user views map information, the map information acquisition unit uses a camera or microphone to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, if a user is smiling while viewing a map, the system displays popular spots in the area. This makes it possible to monitor the user's emotions in real time when viewing map information and provide an interface for eliciting positive emotions.

[0077] The urban development policy information acquisition unit uses generative AI to analyze past policies and their results, and can predict the impact of future policies. For example, the urban development policy information acquisition unit uses generative AI to analyze a local government's past urban development policies and their results, and predict the impact of future policies. For example, it analyzes examples of past successes and failures of redevelopment projects, and predicts the probability of success for future projects. This makes it possible to analyze past policies and their results, and predict the impact of future policies.

[0078] The urban development policy information acquisition unit can visualize urban development policy information to enable users to intuitively understand it. The urban development policy information acquisition unit can visualize, for example, urban development policy information of a local government to enable users to intuitively understand it. For example, the progress of a policy and details of a plan can be displayed in graphs and charts. This makes it possible to visualize urban development policy information to enable users to intuitively understand it.

[0079] The urban development policy information acquisition unit can visualize urban development policy information to enable users to intuitively understand it. The urban development policy information acquisition unit can visualize, for example, urban development policy information of a local government to enable users to intuitively understand it. For example, the progress of a policy and details of a plan can be displayed in graphs and charts. This makes it possible to visualize urban development policy information to enable users to intuitively understand it.

[0080] The urban development policy information acquisition unit uses the emotion estimation function to analyze the emotion of the user when viewing urban development policy information, and can prioritize displaying information that attracts the user's interest. For example, when the user views urban development policy information, the urban development policy information acquisition unit uses a camera or microphone to analyze the user's emotion in real time, and prioritize displaying information that attracts the user's interest. For example, policies that the user is interested in are highlighted. This makes it possible to analyze the emotion of the user when viewing urban development policy information, and prioritize displaying information that attracts the user's interest.

[0081] The urban development policy information acquisition unit can integrate success stories from other municipalities and present best practices. For example, the urban development policy information acquisition unit can integrate success stories from other municipalities into the municipality's urban development policy information and present best practices. For example, it can refer to examples of successful redevelopment projects in other municipalities. This makes it possible to integrate success stories from other municipalities and present best practices.

[0082] The urban development policy information acquisition unit can customize information based on the user's interests and provide individually optimized information. The urban development policy information acquisition unit, for example, customizes urban development policy information of a local government based on the user's interests and provides individually optimized information. For example, information related to areas or projects in which the user is interested is preferentially displayed. This makes it possible to customize information based on the user's interests and provide individually optimized information.

[0083] The urban development policy information acquisition unit can use the emotion estimation function to monitor the emotions of a user when viewing urban development policy information in real time, and provide an interface for eliciting positive emotions. For example, when a user views urban development policy information, the urban development policy information acquisition unit uses a camera or microphone to monitor the user's emotions in real time, and provides an interface for eliciting positive emotions. For example, if the user is smiling while viewing policy information, the system displays related projects in the vicinity. This makes it possible to monitor the emotions of a user when viewing urban development policy information in real time, and provide an interface for eliciting positive emotions.

[0084] The real estate transaction information acquisition unit can use the generation AI to analyze past transaction data and predict future transaction prices. The real estate transaction information acquisition unit, for example, uses the generation AI to analyze past transaction data for nearby real estate transaction information and predict future transaction prices. For example, it analyzes trends in past transaction prices and predicts future price fluctuations. This makes it possible to analyze past transaction data and predict future transaction prices.

[0085] The real estate transaction information acquisition unit can visualize real estate transaction information to enable a user to intuitively understand it. The real estate transaction information acquisition unit can visualize, for example, information on nearby real estate transactions to enable a user to intuitively understand it. For example, the unit can display fluctuations in transaction prices in graphs and charts. This allows the real estate transaction information to be visualized to enable a user to intuitively understand it.

[0086] The real estate transaction information acquisition unit uses the emotion estimation function to analyze the emotion of the user when viewing real estate transaction information, and can prioritize displaying information that attracts interest. For example, when a user views real estate transaction information, the real estate transaction information acquisition unit uses a camera or microphone to analyze the user's emotion in real time, and prioritize displaying information that attracts interest. For example, transaction information that the user is interested in is highlighted. This allows the emotion of the user when viewing real estate transaction information to be analyzed, and information that attracts interest to be prioritized.

[0087] The real estate transaction information acquisition unit can integrate transaction data from other regions and make comparisons between regions. The real estate transaction information acquisition unit can, for example, integrate transaction data from other regions with neighboring real estate transaction information and make comparisons between regions. For example, it can compare transaction prices in different regions within the same city. This allows the integration of transaction data from other regions and makes comparisons between regions.

[0088] The real estate transaction information acquisition unit can customize based on the user's interests and provide individually optimized information. The real estate transaction information acquisition unit, for example, customizes real estate transaction information based on the user's interests and provides individually optimized information. For example, transaction information related to a price range or property type that the user is interested in is preferentially displayed. This allows the information to be customized based on the user's interests and provided individually optimized information.

[0089] The real estate transaction information acquisition unit can use the emotion estimation function to monitor the emotions of a user when viewing real estate transaction information in real time, and provide an interface for eliciting positive emotions. For example, when a user views real estate transaction information, the real estate transaction information acquisition unit uses a camera or microphone to monitor the user's emotions in real time, and provides an interface for eliciting positive emotions. For example, if a user is smiling while viewing transaction information, the system displays related transaction information around the user. This makes it possible to monitor the emotions of a user when viewing real estate transaction information in real time, and provide an interface for eliciting positive emotions.

[0090] The value calculation unit uses the generation AI to analyze past market data and perform highly accurate evaluations. The value calculation unit, for example, uses the generation AI to analyze past market data and perform more accurate evaluations in calculating current value. For example, it analyzes past trading prices and market trends and evaluates current market value. This allows for analyzing past market data and performing highly accurate evaluations.

[0091] The value calculation unit can visualize the calculation result of the present value so that the user can intuitively understand it. The value calculation unit, for example, visualizes the calculation result of the present value so that the user can intuitively understand it. For example, the calculation result is displayed in a graph or chart. In this way, the calculation result of the present value can be visualized so that the user can intuitively understand it.

[0092] The value calculation unit uses the emotion estimation function to analyze the emotion of the user when viewing the calculation results of the present value, and can prioritize displaying information that attracts the user's interest. For example, when the user views the calculation results of the present value, the value calculation unit uses a camera or microphone to analyze the user's emotion in real time, and prioritize displaying information that attracts the user's interest. For example, calculation results that the user is interested in are highlighted. This allows the emotion of the user when viewing the calculation results of the present value to be analyzed, and information that attracts the user's interest to be prioritized.

[0093] The value calculation unit can integrate other evaluation criteria (for example, environmental evaluation and social evaluation) and perform a comprehensive value evaluation. For example, in calculating the present value, the value calculation unit integrates other evaluation criteria (for example, environmental evaluation and social evaluation) and performs a comprehensive value evaluation. For example, a comprehensive evaluation is performed that takes into account the environmental evaluation and social evaluation. This makes it possible to integrate other evaluation criteria and perform a comprehensive value evaluation.

[0094] The value calculation unit can customize the calculation result of the present value based on the user's interests and provide individually optimized information. The value calculation unit, for example, customizes the calculation result of the present value based on the user's interests and provides individually optimized information. For example, calculation results related to a price range or property type in which the user is interested are preferentially displayed. This allows the calculation result of the present value to be customized based on the user's interests and provide individually optimized information.

[0095] The value calculation unit can use the emotion estimation function to monitor the emotions of the user when viewing the calculation results of the present value in real time, and provide an interface for eliciting positive emotions. For example, when the user views the calculation results of the present value, the value calculation unit uses a camera or microphone to monitor the user's emotions in real time, and provides an interface for eliciting positive emotions. For example, if the user is smiling while viewing the calculation results, the system displays related information around them. This makes it possible to monitor the emotions of the user when viewing the calculation results of the present value in real time, and provide an interface for eliciting positive emotions.

[0096] The value increase forecast presentation unit can analyze past data using the generation AI and predict future value increases. The value increase forecast presentation unit, for example, analyzes past data using the generation AI and predicts future value increases in the future value increase forecast. For example, it analyzes past market trends and policy information and predicts future value increases. In this way, it is possible to analyze past data and predict future value increases.

[0097] The value increase forecast presentation unit can visualize the forecast of future value increase, allowing the user to intuitively understand. The value increase forecast presentation unit, for example, visualizes the forecast of future value increase, allowing the user to intuitively understand. For example, the forecast results are displayed in graphs or charts. This makes it possible to visualize the forecast of future value increase, allowing the user to intuitively understand.

[0098] The value increase forecast presentation unit uses an emotion estimation function to analyze the emotions of the user when viewing the forecast for future value increase, and can prioritize displaying information that attracts interest. For example, when a user views the forecast for future value increase, the value increase forecast presentation unit uses a camera or microphone to analyze the user's emotions in real time, and prioritize displaying information that attracts interest. For example, the system highlights forecast results that the user is interested in. This allows the user's emotions when viewing the forecast for future value increase to be analyzed, and information that attracts interest to be prioritized.

[0099] The value increase forecast presentation unit can integrate data from other regions and markets and make comparisons between regions. For example, in the forecast of future value increase, the value increase forecast presentation unit can integrate data from other regions and markets and make comparisons between regions. For example, it can compare the forecast of future value increase in different regions within the same city. This allows data from other regions and markets to be integrated and comparisons between regions to be made.

[0100] The value increase forecast presentation unit can customize the future value increase forecast based on the user's interests and provide individually optimized information. The value increase forecast presentation unit, for example, customizes the future value increase forecast based on the user's interests and provides individually optimized information. For example, forecasts related to a price range or property type in which the user is interested are preferentially displayed. This makes it possible to customize the future value increase forecast based on the user's interests and provide individually optimized information.

[0101] The value increase forecast presentation unit can use the emotion estimation function to monitor the user's emotions in real time when viewing the forecast for future value increase, and provide an interface for eliciting positive emotions. For example, when a user views the forecast for future value increase, the value increase forecast presentation unit uses a camera or microphone to monitor the user's emotions in real time, and provides an interface for eliciting positive emotions. For example, if the user is smiling while viewing the forecast results, the system displays related information around them. This makes it possible to monitor the user's emotions in real time when viewing the forecast for future value increase, and provide an interface for eliciting positive emotions.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The input unit can automatically acquire past weather data for a photo input by a user and perform an analysis that takes weather fluctuations into account. For example, the system automatically acquires past weather data for a current photo of real estate input by a user and performs an analysis that takes weather fluctuations into account. This allows past weather data to be automatically acquired based on a photo input by a user and an analysis that takes weather fluctuations into account.

[0104] The map information acquisition unit can integrate real-time disaster information with map information and perform detailed analysis. For example, real-time disaster information can be integrated with map information to enable the user to understand the current disaster situation. For example, disaster information such as earthquakes and floods can be displayed on a map. This allows real-time disaster information to be integrated with map information and perform detailed analysis.

[0105] The urban development policy information acquisition unit can integrate the failure cases of other municipalities and provide information for risk avoidance. For example, it can integrate the failure cases of other municipalities into the municipality's urban development policy information and provide information for risk avoidance. For example, it can refer to examples of redevelopment projects that have failed in the past. This makes it possible to integrate the failure cases of other municipalities and provide information for risk avoidance.

[0106] The real estate transaction information acquisition unit can customize the real estate transaction information based on the user's interests and provide individually optimized information. For example, the real estate transaction information can be customized based on the user's interests and provide individually optimized information. For example, transaction information related to a price range or property type that the user is interested in can be preferentially displayed. This allows the information to be customized based on the user's interests and provide individually optimized information.

[0107] The value calculation unit can integrate other evaluation criteria (e.g., environmental evaluation and social evaluation) to perform a comprehensive value evaluation. For example, in calculating the present value, other evaluation criteria (e.g., environmental evaluation and social evaluation) are integrated to perform a comprehensive value evaluation. For example, a comprehensive evaluation is performed that takes into account the environmental evaluation and social evaluation. This allows the other evaluation criteria to be integrated to perform a comprehensive value evaluation.

[0108] The input unit uses an emotion estimation function to analyze the user's emotions when entering information, and can make suggestions to simplify the input process if the user is feeling stressed. For example, when a user enters address information, the system uses a camera or microphone to analyze the user's facial expressions and tone of voice to determine whether the user is feeling stressed. For example, if the user is feeling impatient, the system simplifies the input process. This makes it possible to make suggestions to simplify the input process if the user is feeling stressed.

[0109] The map information acquisition unit uses the emotion estimation function to analyze the emotion of the user when viewing map information, and can prioritize displaying information that attracts the user's interest. For example, when the user views map information, the system uses a camera or microphone to analyze the user's emotion in real time, and prioritize displaying information that attracts the user's interest. For example, it highlights places that the user has shown interest in. This allows the emotion of the user when viewing map information to be analyzed, and prioritize displaying information that attracts the user's interest.

[0110] The urban development policy information acquisition unit can use the emotion estimation function to monitor the emotions of users when they view urban development policy information in real time, and provide an interface for eliciting positive emotions. For example, when a user views urban development policy information, the system can use a camera or microphone to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, if the user is smiling while viewing policy information, the system can display related projects in the area. This makes it possible to monitor the emotions of users when they view urban development policy information in real time and provide an interface for eliciting positive emotions.

[0111] The real estate transaction information acquisition unit uses the emotion estimation function to analyze the emotions of the user when viewing real estate transaction information, and can prioritize displaying information that attracts interest. For example, when a user views real estate transaction information, the system uses a camera or microphone to analyze the user's emotions in real time and prioritize displaying information that attracts interest. For example, transaction information that the user is interested in is highlighted. This allows the user's emotions when viewing real estate transaction information to be analyzed, and information that attracts interest to be prioritized.

[0112] The value calculation unit can use the emotion estimation function to monitor the emotions of the user when viewing the calculation results of the present value in real time, and provide an interface for eliciting positive emotions. For example, when the user views the calculation results of the present value, the system can monitor the user's emotions in real time using a camera or microphone, and provide an interface for eliciting positive emotions. For example, if the user is smiling while viewing the calculation results, the system can display related information around them. This makes it possible to monitor the emotions of the user when viewing the calculation results of the present value in real time, and provide an interface for eliciting positive emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The input section inputs a photo of the property's current condition and address information. For example, the user can input a photo taken with their smartphone and the address, and the system will begin its analysis. Step 2: The map information acquisition unit acquires map information based on the address information input by the input unit, such as the location of the nearest train station, bus stop, park, commercial facility, etc. Step 3: The Urban Development Policy Information Acquisition Department acquires information on urban development policies of local governments, such as construction plans for new roads and public facilities, and redevelopment projects. Step 4: The real estate transaction information acquisition unit acquires information on nearby real estate transactions, for example, by identifying recent trends in sales and rental prices within the same area. Step 5: The analysis unit analyzes the information acquired by the map information acquisition unit, urban development policy information acquisition unit, and real estate transaction information acquisition unit. This allows for a detailed analysis of each piece of information. Step 6: The value calculation unit calculates the current value of the property based on the map information, urban development policy information, and real estate transaction information analyzed by the analysis unit. For example, the current market value is evaluated based on the surrounding environment and transaction information. Step 7: The value increase forecast presentation unit presents a forecast of future value increase based on the present value calculated by the value calculation unit. For example, if a redevelopment project is underway, the forecast shows that the value is likely to increase in the future.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

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

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0159] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting current property photos and address information; a map information acquisition unit that acquires map information based on the address information input by the input unit; an analysis unit that analyzes the map information acquired by the map information acquisition unit; an urban development policy information acquisition department that acquires urban development policy information from local governments; an analysis unit that analyzes the urban development policy information acquired by the urban development policy information acquisition unit; a real estate transaction information acquisition unit that acquires real estate transaction information in the vicinity; an analysis unit that analyzes the real estate transaction information acquired by the real estate transaction information acquisition unit; a value calculation unit that calculates the current value of real estate based on the map information analyzed by the analysis unit, the urban development policy information, and the real estate transaction information; and a value increase forecast presentation unit that presents a forecast of future value increase based on the present value calculated by the value calculation unit. A system characterized by:

2. The input unit The system uses generative AI to perform image recognition on photos entered by users, automatically analyzing the building's condition and details of the surrounding environment.

2. The system of claim 1.

3. The map information acquisition unit Using generative AI to analyze past geographical changes and predict future changes 2. The system of claim 1.

4. The urban development policy information acquisition unit Using generative AI to analyze past policies and their outcomes and predict the impact of future policies 2. The system of claim 1.

5. The real estate transaction information acquisition unit Generative AI is used to analyze past transaction data and predict future transaction prices.

2. The system of claim 1.

6. The value calculation unit Generative AI is used to analyze past market data and provide highly accurate evaluations.

2. The system of claim 1.

7. The value improvement forecast presentation unit Generative AI is used to analyze past data and predict future value growth.

2. The system of claim 1.

8. The input unit Analyzes the user's emotions while typing and makes suggestions to simplify the input process if the user is feeling stressed.

2. The system of claim 1.

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