system

Abstract

The system according to this embodiment aims to quickly derive buildable plans based on land information and to evaluate the economic value of the land. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and an evaluation unit. The reception unit receives land information. The analysis unit analyzes the land information received by the reception unit and derives a buildable plan considering legal regulations. The evaluation unit evaluates the economic value of the land based on the building plan derived by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to quickly derive a buildable plan based on land information and evaluate the economic value of the land.

[0005] The system according to the embodiment aims to quickly derive a buildable plan based on land information and evaluate the economic value of the land.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and an evaluation unit. The reception unit receives land information. The analysis unit analyzes the land information received by the reception unit and derives a buildable plan considering legal regulations. The evaluation unit evaluates the economic value of the land based on the building plan derived by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly derive buildable plans based on land information and evaluate the economic value of the land. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI system according to an embodiment of the present invention is a system for instantly evaluating the buildability of land. This system allows for a rapid understanding of the land's economic value by having the AI ​​process land information, analyzing legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions, and deriving multiple buildable plans. First, the user inputs land information, such as the location, area, and shape of the land. This information is then input into the AI. Next, the AI ​​analyzes the input land information. The AI ​​considers legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions, and derives multiple buildable plans. For example, it can propose a plan that is not buildable in Chuo Ward but is buildable in Itabashi Ward. Furthermore, the AI ​​evaluates the economic value of the land based on the derived building plans. For example, it can calculate the size and number of rooms of a buildable building and evaluate the economic value of the land. This mechanism allows for a rapid understanding of the land's economic value. Users can instantly obtain buildable plans simply by inputting land information, without needing to consult an architect. This improves the speed of land transactions and enhances competitiveness in the real estate industry. For example, for a given plot of land, AI can propose multiple building plans, from which the optimal plan can be selected. This ensures the effective use of the land and maximizes its economic value. In addition to proposing building plans, AI can also perform simulations based on the land's zoning regulations and legal restrictions. This allows users to maximize the potential of their land. In this way, AI can be used to instantly evaluate the buildability of land and quickly grasp its economic value. This will streamline land transactions in the real estate industry and create a world where ordinary real estate investors can compete on equal footing with professional real estate agents. Thus, AI systems can instantly evaluate the buildability of land and quickly grasp its economic value.

[0029] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, and an evaluation unit. The reception unit receives land information. Land information includes, but is not limited to, geographical location, area, and geological information. For example, the reception unit allows users to input information such as the location, area, and shape of the land. The analysis unit analyzes the land information input by the reception unit and derives a buildable plan considering legal regulations. For example, the analysis unit can derive a buildable plan considering legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions. For example, the analysis unit can propose a plan that cannot be built in Chuo Ward but can be built in Itabashi Ward. The analysis unit can also propose multiple building plans. For example, the analysis unit can propose plans for different uses or plans with different structures. The evaluation unit evaluates the economic value of the land based on the building plans derived by the analysis unit. For example, the evaluation unit can calculate the size and number of rooms of a buildable building and evaluate the economic value of the land. For example, the evaluation unit can evaluate the economic value based on the size and number of rooms of a buildable building. Furthermore, the evaluation unit can also perform simulations based on the land's zoning regulations and legal regulations. For example, the evaluation unit can perform simulations based on the land's zoning regulations and legal regulations to maximize the land's potential. As a result, the AI ​​system according to the embodiment can instantly evaluate the buildability of the land and quickly grasp its economic value. Some or all of the above-described processes in the reception unit, analysis unit, and evaluation unit may be performed using AI, or not using AI. For example, the reception unit can input land information entered by the user into the AI, the analysis unit can have the AI ​​analyze the land information, and the evaluation unit can have the AI ​​evaluate the economic value.

[0030] The reception desk inputs land information. This land information includes, but is not limited to, geographical location, area, and geological information. For example, users can input information such as the location, area, and shape of the land. Specifically, through a dedicated interface, users can select the land's location on a map and input the area and shape numerically or graphically. Furthermore, geological information can be automatically retrieved from geological survey reports or existing databases. The reception desk centrally manages this information and stores it in a database. Information entered by users is reflected in the system in real time, allowing the analysis and evaluation departments to access it immediately. The reception desk also includes a checking function to verify the accuracy of the information entered by users. For example, it automatically verifies whether the entered geographical location matches the actual map and whether the area is within a realistic range. This reduces the risk of users entering incorrect information and improves the overall reliability of the system. Furthermore, the reception desk provides support functions to supplement the information entered by users. For example, when users enter geological information, it presents historical data and reference materials to help them enter appropriate information. This allows users to input more accurate and detailed land information, improving the overall accuracy of the system.

[0031] The analysis unit analyzes land information entered by the reception unit and derives buildable plans that take legal regulations into consideration. For example, the analysis unit can derive buildable plans that take into account legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions. Specifically, the analysis unit uses AI to analyze land information and retrieves and applies various legal regulations from a database. For example, if the land use zone is a commercial zone, it proposes building plans for commercial facilities or office buildings, and if it is a residential zone, it proposes plans for detached houses or condominiums. It also optimizes the height and placement of buildings, taking into account road width and height restrictions. The analysis unit generates multiple building plans after considering these legal regulations. For example, it proposes multiple plans with different uses and structures for the same plot of land, allowing the user to choose. Furthermore, the analysis unit uses AI to refer to past data and similar cases to derive the optimal plan. For example, it analyzes successful and unsuccessful plans based on data of buildings constructed in the same area in the past and proposes the optimal plan. The analysis unit can also generate customized plans that take into account the user's requests and conditions. For example, if a user desires a specific design or function, the system will propose a plan that reflects those requests. This allows the analysis unit to provide the optimal building plan that meets the user's needs while complying with legal regulations.

[0032] The evaluation unit assesses the economic value of land based on the building plans derived by the analysis unit. For example, the evaluation unit can calculate the size and number of rooms of a buildable building and assess the economic value of the land. Specifically, the evaluation unit uses AI to assess the economic value of building plans. For instance, it simulates rental income and sale prices based on the size and number of rooms of a buildable building to calculate the economic value of the land. The evaluation unit can also perform simulations based on the land's zoning and legal regulations. For example, in a commercial area, it evaluates the profitability of commercial facilities, and in a residential area, it evaluates the market value of residential properties. Furthermore, the evaluation unit considers market trends and economic conditions in the surrounding area to perform more accurate assessments. For example, it analyzes real estate prices and rental demand in the surrounding area to predict the future value of the land. The evaluation unit can also simulate multiple scenarios to identify the plan with the highest economic value. For example, it compares plans with different uses and structures to select the most profitable plan. This allows the evaluation unit to provide users with the optimal building plan and its economic value, maximizing the land's potential. Finally, the evaluation unit visualizes the evaluation results and presents them clearly to the user. For example, graphs and charts can be used to compare the economic value of each plan, making it easy for users to understand. This allows the evaluation unit to provide users with reliable information and support their decision-making.

[0033] The analysis unit can derive buildable plans considering legal regulations such as land use zones, road widths, height zones, and shadow restrictions. For example, the analysis unit can derive buildable plans considering land use zones. For example, the analysis unit can propose building plans based on land use zones such as commercial, residential, and industrial zones. The analysis unit can also derive buildable plans considering road widths. For example, the analysis unit can propose building plans based on legal widths and actual widths. Furthermore, the analysis unit can derive buildable plans considering height zones. For example, the analysis unit can propose building plans based on height zones such as Type 1 Height Zones and Type 2 Height Zones. The analysis unit can also derive buildable plans considering shadow restrictions. For example, the analysis unit can propose building plans based on the scope of shadow regulations and the length of shadows. This allows for the deriving of building plans that take legal regulations into account. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions into the AI, which can then derive a plan that allows for construction.

[0034] The evaluation unit can calculate the size and number of rooms of a building that can be constructed and assess the economic value of the land. For example, the evaluation unit can calculate the size of a building that can be constructed. For example, the evaluation unit can calculate the size of a building based on the total floor area or the building area. The evaluation unit can also calculate the number of rooms. For example, the evaluation unit can calculate the number of rooms based on the number of habitable rooms or the number of non-habitable rooms. Furthermore, the evaluation unit can assess the economic value based on the size and number of rooms of the building that can be constructed. For example, the evaluation unit can assess the market price and profitability based on the size and number of rooms of the building. This allows the evaluation unit to assess the economic value based on the size and number of rooms of the building that can be constructed. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the size and number of rooms of the building that can be constructed into AI, and the AI ​​can assess the economic value.

[0035] The analysis unit can propose multiple building plans. For example, the analysis unit can propose building plans for different uses. For example, the analysis unit can propose building plans for different uses such as residential, commercial, and industrial. The analysis unit can also propose building plans for different structures. For example, the analysis unit can propose building plans for different structures such as wooden, steel frame, and reinforced concrete. Furthermore, the analysis unit can propose building plans of different scales. For example, the analysis unit can propose building plans of different scales such as small, medium, and large. By proposing multiple building plans, the optimal plan can be selected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input building plans of different uses, structures, and scales into the AI, and the AI ​​can propose multiple building plans.

[0036] The evaluation unit can perform simulations based on land use zones and legal regulations. For example, the evaluation unit can perform simulations based on land use zones such as commercial, residential, and industrial zones. The evaluation unit can also perform simulations based on legal regulations such as the Building Standards Act and the City Planning Act. Furthermore, the evaluation unit can evaluate the potential of the land based on the simulation results. For example, the evaluation unit can evaluate the economic value of the land based on the simulation results. This allows for maximizing the potential of the land by performing simulations based on land use zones and legal regulations. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input land use zones and legal regulations into AI, and the AI ​​can perform simulations.

[0037] The reception desk can analyze the user's past land information input history and suggest the optimal input method. For example, the reception desk can automatically display land information that the user has frequently entered in the past as a candidate. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest land information to be used at a specific time of day based on the user's past input history. This streamlines the input process by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI, which can then suggest the optimal input method.

[0038] The reception desk can automatically adjust input fields based on the user's current projects and areas of interest when entering land information. For example, if the user is interested in residential projects, the reception desk will prioritize displaying residential-related input fields. Similarly, if the user is interested in commercial facility projects, the reception desk can prioritize displaying commercial facility-related input fields. Furthermore, if the user is interested in a specific region, the reception desk can automatically display input fields related to that region. This streamlines the input process by adjusting input fields based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk can input the user's areas of interest data into the AI, which can then automatically adjust the input fields.

[0039] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when inputting land information. For example, if the user is in a specific region, the reception unit can prioritize inputting land information related to that region. Furthermore, if the user is on the move, the reception unit can prioritize inputting relevant land information based on their current location. Additionally, if the user is in a specific city, the reception unit can prioritize inputting land information related to that city. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location information into the AI, which can then prioritize inputting highly relevant information.

[0040] The reception desk can analyze the user's social media activity when inputting land information and automatically input relevant information. For example, the reception desk can automatically input relevant land information based on location information shared by the user on social media. It can also automatically input land information related to areas the user has shown interest in on social media. Furthermore, the reception desk can automatically input relevant land information based on information obtained from real estate-related accounts that the user follows on social media. In this way, relevant information can be automatically input by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​can automatically input relevant information.

[0041] The analysis unit can select the optimal analysis method by referring to the land's past use history during the analysis. For example, if the land was previously used as residential land, the analysis unit will select an analysis method suitable for residential land. It can also select an analysis method suitable for commercial land if the land was previously used as commercial land. Furthermore, if the land was previously used as agricultural land, the analysis unit can select an analysis method suitable for agricultural land. This allows the optimal analysis method to be selected by referring to past use history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past land use history data into AI, which can then select the optimal analysis method.

[0042] The analysis unit can improve the accuracy of its analysis by considering the shape and geological information of the land. For example, if the shape of the land is irregular, the analysis unit can select an analysis method that suits that shape. Furthermore, if the soil of the land is soft, the analysis unit can select an analysis method that suits that soil type. In addition, the analysis unit can propose an optimal building plan based on the shape and geological information of the land. This improves the accuracy of the analysis by considering the shape and geological information of the land. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the shape and geological information of the land into the AI, which can then improve the accuracy of the analysis.

[0043] The analysis unit can perform its analysis while considering the surrounding environment information of the land. For example, if there are many commercial facilities around the land, the analysis unit will consider their impact in its analysis. Similarly, if there are many residential areas around the land, the analysis unit can also consider their impact in its analysis. Furthermore, if there are many public facilities around the land, the analysis unit can also consider their impact in its analysis. This allows for a more accurate analysis by considering the surrounding environment information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the surrounding environment information of the land into the AI, which can then perform the analysis.

[0044] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on land during the analysis process. For example, the analysis unit can perform its analysis by referring to past research papers on land. It can also perform its analysis by referring to past survey reports on land. Furthermore, the analysis unit can perform its analysis by referring to literature on past laws and regulations concerning land. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data on land into AI, which can then improve the accuracy of the analysis.

[0045] The valuation unit can select the optimal valuation method by referring to the land's past transaction history during the valuation process. For example, if the land was traded at a high price in the past, the valuation unit will perform the valuation based on that transaction history. The valuation unit can also perform the valuation based on if the land was traded at a low price in the past. Furthermore, the valuation unit can also assess the current market value based on the land's past transaction history. This allows the optimal valuation method to be selected by referring to past transaction history. Some or all of the above processing in the valuation unit may be performed using AI, for example, or without AI. For example, the valuation unit can input the land's past transaction history data into AI, which can then select the optimal valuation method.

[0046] The evaluation unit can improve the accuracy of its evaluation by considering surrounding market data for the land. For example, the evaluation unit can perform an evaluation based on market data of similar properties in the vicinity of the land. It can also perform an evaluation based on market data of commercial facilities in the vicinity of the land. Furthermore, it can perform an evaluation based on market data of public facilities in the vicinity of the land. This improves the accuracy of the evaluation by considering surrounding market data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input surrounding market data for the land into the AI, which can then improve the accuracy of the evaluation.

[0047] The evaluation unit can perform evaluations while considering the geographical location information of the land. For example, if the land is located in an urban area, the evaluation unit will perform the evaluation based on that geographical location information. The evaluation unit can also perform the evaluation based on the geographical location information of the land if it is located in a suburban area. Furthermore, if the land is located in a tourist area, the evaluation unit can perform the evaluation based on that geographical location information. This allows for a more accurate evaluation by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical location information of the land into the AI, and the AI ​​can perform the evaluation.

[0048] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on land during the evaluation process. For example, the evaluation unit can perform its evaluation by referring to past research papers on land. It can also perform its evaluation by referring to past survey reports on land. Furthermore, the evaluation unit can perform its evaluation by referring to literature on past laws and regulations concerning land. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input relevant literature data on land into AI, which can then improve the accuracy of the evaluation.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The reception desk can analyze the user's past land information input history and suggest the optimal input method. For example, the reception desk can automatically display land information that the user has frequently entered in the past as a candidate. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest land information to be used at a specific time of day based on the user's past input history. This streamlines the input process by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI, which can then suggest the optimal input method.

[0051] The reception desk can automatically adjust input fields based on the user's current projects and areas of interest when entering land information. For example, if the user is interested in residential projects, the reception desk will prioritize displaying residential-related input fields. Similarly, if the user is interested in commercial facility projects, the reception desk can prioritize displaying commercial facility-related input fields. Furthermore, if the user is interested in a specific region, the reception desk can automatically display input fields related to that region. This streamlines the input process by adjusting input fields based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk can input the user's areas of interest data into the AI, which can then automatically adjust the input fields.

[0052] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when inputting land information. For example, if the user is in a specific region, the reception unit can prioritize inputting land information related to that region. Furthermore, if the user is on the move, the reception unit can prioritize inputting relevant land information based on their current location. Additionally, if the user is in a specific city, the reception unit can prioritize inputting land information related to that city. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location information into the AI, which can then prioritize inputting highly relevant information.

[0053] The reception desk can analyze the user's social media activity when inputting land information and automatically input relevant information. For example, the reception desk can automatically input relevant land information based on location information shared by the user on social media. It can also automatically input land information related to areas the user has shown interest in on social media. Furthermore, the reception desk can automatically input relevant land information based on information obtained from real estate-related accounts that the user follows on social media. In this way, relevant information can be automatically input by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​can automatically input relevant information.

[0054] The analysis unit can select the optimal analysis method by referring to the land's past use history during the analysis. For example, if the land was previously used as residential land, the analysis unit will select an analysis method suitable for residential land. It can also select an analysis method suitable for commercial land if the land was previously used as commercial land. Furthermore, if the land was previously used as agricultural land, the analysis unit can select an analysis method suitable for agricultural land. This allows the optimal analysis method to be selected by referring to past use history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past land use history data into AI, which can then select the optimal analysis method.

[0055] The analysis unit can improve the accuracy of its analysis by considering the shape and geological information of the land. For example, if the shape of the land is irregular, the analysis unit can select an analysis method that suits that shape. Furthermore, if the soil of the land is soft, the analysis unit can select an analysis method that suits that soil type. In addition, the analysis unit can propose an optimal building plan based on the shape and geological information of the land. This improves the accuracy of the analysis by considering the shape and geological information of the land. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the shape and geological information of the land into the AI, which can then improve the accuracy of the analysis.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk enters the land information. This land information includes geographical location, area, and geological information. Users can enter information such as the location, area, and shape of the land. Step 2: The analysis unit analyzes the land information entered by the reception unit and derives a buildable plan considering legal regulations. The analysis unit can propose a buildable plan considering legal regulations such as land use zones, road width, height restrictions, and shadow restrictions. It can also propose multiple building plans. Step 3: The evaluation unit assesses the economic value of the land based on the building plan derived by the analysis unit. The evaluation unit can calculate the size and number of rooms of a building that can be constructed and assess the economic value of the land. It can also perform simulations based on the land's zoning regulations and legal restrictions to maximize the land's potential.

[0058] (Example of form 2) An AI system according to an embodiment of the present invention is a system for instantly evaluating the buildability of land. This system allows for a rapid understanding of the land's economic value by having the AI ​​process land information, analyzing legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions, and deriving multiple buildable plans. First, the user inputs land information, such as the location, area, and shape of the land. This information is then input into the AI. Next, the AI ​​analyzes the input land information. The AI ​​considers legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions, and derives multiple buildable plans. For example, it can propose a plan that is not buildable in Chuo Ward but is buildable in Itabashi Ward. Furthermore, the AI ​​evaluates the economic value of the land based on the derived building plans. For example, it can calculate the size and number of rooms of a buildable building and evaluate the economic value of the land. This mechanism allows for a rapid understanding of the land's economic value. Users can instantly obtain buildable plans simply by inputting land information, without needing to consult an architect. This improves the speed of land transactions and enhances competitiveness in the real estate industry. For example, for a given plot of land, AI can propose multiple building plans, from which the optimal plan can be selected. This ensures the effective use of the land and maximizes its economic value. In addition to proposing building plans, AI can also perform simulations based on the land's zoning regulations and legal restrictions. This allows users to maximize the potential of their land. In this way, AI can be used to instantly evaluate the buildability of land and quickly grasp its economic value. This will streamline land transactions in the real estate industry and create a world where ordinary real estate investors can compete on equal footing with professional real estate agents. Thus, AI systems can instantly evaluate the buildability of land and quickly grasp its economic value.

[0059] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, and an evaluation unit. The reception unit receives land information. Land information includes, but is not limited to, geographical location, area, and geological information. For example, the reception unit allows users to input information such as the location, area, and shape of the land. The analysis unit analyzes the land information input by the reception unit and derives a buildable plan considering legal regulations. For example, the analysis unit can derive a buildable plan considering legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions. For example, the analysis unit can propose a plan that cannot be built in Chuo Ward but can be built in Itabashi Ward. The analysis unit can also propose multiple building plans. For example, the analysis unit can propose plans for different uses or plans with different structures. The evaluation unit evaluates the economic value of the land based on the building plans derived by the analysis unit. For example, the evaluation unit can calculate the size and number of rooms of a buildable building and evaluate the economic value of the land. For example, the evaluation unit can evaluate the economic value based on the size and number of rooms of a buildable building. Furthermore, the evaluation unit can also perform simulations based on the land's zoning regulations and legal regulations. For example, the evaluation unit can perform simulations based on the land's zoning regulations and legal regulations to maximize the land's potential. As a result, the AI ​​system according to the embodiment can instantly evaluate the buildability of the land and quickly grasp its economic value. Some or all of the above-described processes in the reception unit, analysis unit, and evaluation unit may be performed using AI, or not using AI. For example, the reception unit can input land information entered by the user into the AI, the analysis unit can have the AI ​​analyze the land information, and the evaluation unit can have the AI ​​evaluate the economic value.

[0060] The reception desk inputs land information. This land information includes, but is not limited to, geographical location, area, and geological information. For example, users can input information such as the location, area, and shape of the land. Specifically, through a dedicated interface, users can select the land's location on a map and input the area and shape numerically or graphically. Furthermore, geological information can be automatically retrieved from geological survey reports or existing databases. The reception desk centrally manages this information and stores it in a database. Information entered by users is reflected in the system in real time, allowing the analysis and evaluation departments to access it immediately. The reception desk also includes a checking function to verify the accuracy of the information entered by users. For example, it automatically verifies whether the entered geographical location matches the actual map and whether the area is within a realistic range. This reduces the risk of users entering incorrect information and improves the overall reliability of the system. Furthermore, the reception desk provides support functions to supplement the information entered by users. For example, when users enter geological information, it presents historical data and reference materials to help them enter appropriate information. This allows users to input more accurate and detailed land information, improving the overall accuracy of the system.

[0061] The analysis unit analyzes land information entered by the reception unit and derives buildable plans that take legal regulations into consideration. For example, the analysis unit can derive buildable plans that take into account legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions. Specifically, the analysis unit uses AI to analyze land information and retrieves and applies various legal regulations from a database. For example, if the land use zone is a commercial zone, it proposes building plans for commercial facilities or office buildings, and if it is a residential zone, it proposes plans for detached houses or condominiums. It also optimizes the height and placement of buildings, taking into account road width and height restrictions. The analysis unit generates multiple building plans after considering these legal regulations. For example, it proposes multiple plans with different uses and structures for the same plot of land, allowing the user to choose. Furthermore, the analysis unit uses AI to refer to past data and similar cases to derive the optimal plan. For example, it analyzes successful and unsuccessful plans based on data of buildings constructed in the same area in the past and proposes the optimal plan. The analysis unit can also generate customized plans that take into account the user's requests and conditions. For example, if a user desires a specific design or function, the system will propose a plan that reflects those requests. This allows the analysis unit to provide the optimal building plan that meets the user's needs while complying with legal regulations.

[0062] The evaluation unit assesses the economic value of land based on the building plans derived by the analysis unit. For example, the evaluation unit can calculate the size and number of rooms of a buildable building and assess the economic value of the land. Specifically, the evaluation unit uses AI to assess the economic value of building plans. For instance, it simulates rental income and sale prices based on the size and number of rooms of a buildable building to calculate the economic value of the land. The evaluation unit can also perform simulations based on the land's zoning and legal regulations. For example, in a commercial area, it evaluates the profitability of commercial facilities, and in a residential area, it evaluates the market value of residential properties. Furthermore, the evaluation unit considers market trends and economic conditions in the surrounding area to perform more accurate assessments. For example, it analyzes real estate prices and rental demand in the surrounding area to predict the future value of the land. The evaluation unit can also simulate multiple scenarios to identify the plan with the highest economic value. For example, it compares plans with different uses and structures to select the most profitable plan. This allows the evaluation unit to provide users with the optimal building plan and its economic value, maximizing the land's potential. Finally, the evaluation unit visualizes the evaluation results and presents them clearly to the user. For example, graphs and charts can be used to compare the economic value of each plan, making it easy for users to understand. This allows the evaluation unit to provide users with reliable information and support their decision-making.

[0063] The analysis unit can derive buildable plans considering legal regulations such as land use zones, road widths, height zones, and shadow restrictions. For example, the analysis unit can derive buildable plans considering land use zones. For example, the analysis unit can propose building plans based on land use zones such as commercial, residential, and industrial zones. The analysis unit can also derive buildable plans considering road widths. For example, the analysis unit can propose building plans based on legal widths and actual widths. Furthermore, the analysis unit can derive buildable plans considering height zones. For example, the analysis unit can propose building plans based on height zones such as Type 1 Height Zones and Type 2 Height Zones. The analysis unit can also derive buildable plans considering shadow restrictions. For example, the analysis unit can propose building plans based on the scope of shadow regulations and the length of shadows. This allows for the deriving of building plans that take legal regulations into account. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input legal regulations such as land use zones, road widths, height restrictions, and shadow restrictions into the AI, which can then derive a plan that allows for construction.

[0064] The evaluation unit can calculate the size and number of rooms of a building that can be constructed and assess the economic value of the land. For example, the evaluation unit can calculate the size of a building that can be constructed. For example, the evaluation unit can calculate the size of a building based on the total floor area or the building area. The evaluation unit can also calculate the number of rooms. For example, the evaluation unit can calculate the number of rooms based on the number of habitable rooms or the number of non-habitable rooms. Furthermore, the evaluation unit can assess the economic value based on the size and number of rooms of the building that can be constructed. For example, the evaluation unit can assess the market price and profitability based on the size and number of rooms of the building. This allows the evaluation unit to assess the economic value based on the size and number of rooms of the building that can be constructed. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the size and number of rooms of the building that can be constructed into AI, and the AI ​​can assess the economic value.

[0065] The analysis unit can propose multiple building plans. For example, the analysis unit can propose building plans for different uses. For example, the analysis unit can propose building plans for different uses such as residential, commercial, and industrial. The analysis unit can also propose building plans for different structures. For example, the analysis unit can propose building plans for different structures such as wooden, steel frame, and reinforced concrete. Furthermore, the analysis unit can propose building plans of different scales. For example, the analysis unit can propose building plans of different scales such as small, medium, and large. By proposing multiple building plans, the optimal plan can be selected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input building plans of different uses, structures, and scales into the AI, and the AI ​​can propose multiple building plans.

[0066] The evaluation unit can perform simulations based on land use zones and legal regulations. For example, the evaluation unit can perform simulations based on land use zones such as commercial, residential, and industrial zones. The evaluation unit can also perform simulations based on legal regulations such as the Building Standards Act and the City Planning Act. Furthermore, the evaluation unit can evaluate the potential of the land based on the simulation results. For example, the evaluation unit can evaluate the economic value of the land based on the simulation results. This allows for maximizing the potential of the land by performing simulations based on land use zones and legal regulations. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input land use zones and legal regulations into AI, and the AI ​​can perform simulations.

[0067] The reception desk can estimate the user's emotions and customize the input interface for land information based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of land information. This improves user convenience by customizing the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate emotions and customize the input interface based on the result.

[0068] The reception desk can analyze the user's past land information input history and suggest the optimal input method. For example, the reception desk can automatically display land information that the user has frequently entered in the past as a candidate. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest land information to be used at a specific time of day based on the user's past input history. This streamlines the input process by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI, which can then suggest the optimal input method.

[0069] The reception desk can automatically adjust input fields based on the user's current projects and areas of interest when entering land information. For example, if the user is interested in residential projects, the reception desk will prioritize displaying residential-related input fields. Similarly, if the user is interested in commercial facility projects, the reception desk can prioritize displaying commercial facility-related input fields. Furthermore, if the user is interested in a specific region, the reception desk can automatically display input fields related to that region. This streamlines the input process by adjusting input fields based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk can input the user's areas of interest data into the AI, which can then automatically adjust the input fields.

[0070] The reception desk can estimate the user's emotions and determine the priority of the land information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize the input of important information and postpone the input of detailed information. Similarly, if the user is relaxed, the reception desk may also prioritize the input of detailed information. Furthermore, if the user is in a hurry, the reception desk may only input the most important information. This streamlines the input process by prioritizing the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate the emotions and determine the priority of the information to be entered based on the result.

[0071] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when inputting land information. For example, if the user is in a specific region, the reception unit can prioritize inputting land information related to that region. Furthermore, if the user is on the move, the reception unit can prioritize inputting relevant land information based on their current location. Additionally, if the user is in a specific city, the reception unit can prioritize inputting land information related to that city. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location information into the AI, which can then prioritize inputting highly relevant information.

[0072] The reception desk can analyze the user's social media activity when inputting land information and automatically input relevant information. For example, the reception desk can automatically input relevant land information based on location information shared by the user on social media. It can also automatically input land information related to areas the user has shown interest in on social media. Furthermore, the reception desk can automatically input relevant land information based on information obtained from real estate-related accounts that the user follows on social media. In this way, relevant information can be automatically input by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​can automatically input relevant information.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI, the generative AI can estimate the emotions, and the display method of the analysis results can be adjusted based on the result.

[0074] The analysis unit can select the optimal analysis method by referring to the land's past use history during the analysis. For example, if the land was previously used as residential land, the analysis unit will select an analysis method suitable for residential land. It can also select an analysis method suitable for commercial land if the land was previously used as commercial land. Furthermore, if the land was previously used as agricultural land, the analysis unit can select an analysis method suitable for agricultural land. This allows the optimal analysis method to be selected by referring to past use history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past land use history data into AI, which can then select the optimal analysis method.

[0075] The analysis unit can improve the accuracy of its analysis by considering the shape and geological information of the land. For example, if the shape of the land is irregular, the analysis unit can select an analysis method that suits that shape. Furthermore, if the soil of the land is soft, the analysis unit can select an analysis method that suits that soil type. In addition, the analysis unit can propose an optimal building plan based on the shape and geological information of the land. This improves the accuracy of the analysis by considering the shape and geological information of the land. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the shape and geological information of the land into the AI, which can then improve the accuracy of the analysis.

[0076] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying important analysis results. It can also prioritize displaying detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can display only the most important analysis results. This allows for the prioritization of important information by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of analysis results based on the results.

[0077] The analysis unit can perform its analysis while considering the surrounding environment information of the land. For example, if there are many commercial facilities around the land, the analysis unit will consider their impact in its analysis. Similarly, if there are many residential areas around the land, the analysis unit can also consider their impact in its analysis. Furthermore, if there are many public facilities around the land, the analysis unit can also consider their impact in its analysis. This allows for a more accurate analysis by considering the surrounding environment information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the surrounding environment information of the land into the AI, which can then perform the analysis.

[0078] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on land during the analysis process. For example, the analysis unit can perform its analysis by referring to past research papers on land. It can also perform its analysis by referring to past survey reports on land. Furthermore, the analysis unit can perform its analysis by referring to literature on past laws and regulations concerning land. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data on land into AI, which can then improve the accuracy of the analysis.

[0079] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. If the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a concise display method. By adjusting the display method of the evaluation results according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input the user's facial expression data into the generative AI, the generative AI can estimate the emotions, and the display method of the evaluation results can be adjusted based on the result.

[0080] The valuation unit can select the optimal valuation method by referring to the land's past transaction history during the valuation process. For example, if the land was traded at a high price in the past, the valuation unit will perform the valuation based on that transaction history. The valuation unit can also perform the valuation based on if the land was traded at a low price in the past. Furthermore, the valuation unit can also assess the current market value based on the land's past transaction history. This allows the optimal valuation method to be selected by referring to past transaction history. Some or all of the above processing in the valuation unit may be performed using AI, for example, or without AI. For example, the valuation unit can input the land's past transaction history data into AI, which can then select the optimal valuation method.

[0081] The evaluation unit can improve the accuracy of its evaluation by considering surrounding market data for the land. For example, the evaluation unit can perform an evaluation based on market data of similar properties in the vicinity of the land. It can also perform an evaluation based on market data of commercial facilities in the vicinity of the land. Furthermore, it can perform an evaluation based on market data of public facilities in the vicinity of the land. This improves the accuracy of the evaluation by considering surrounding market data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input surrounding market data for the land into the AI, which can then improve the accuracy of the evaluation.

[0082] The evaluation unit can estimate the user's emotions and determine the priority of evaluation results based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit can prioritize displaying important evaluation results. It can also prioritize displaying detailed evaluation results if the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can display only the most important evaluation results. This allows for the prioritization of important information by determining the priority of evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of evaluation results based on the result.

[0083] The evaluation unit can perform evaluations while considering the geographical location information of the land. For example, if the land is located in an urban area, the evaluation unit will perform the evaluation based on that geographical location information. The evaluation unit can also perform the evaluation based on the geographical location information of the land if it is located in a suburban area. Furthermore, if the land is located in a tourist area, the evaluation unit can perform the evaluation based on that geographical location information. This allows for a more accurate evaluation by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical location information of the land into the AI, and the AI ​​can perform the evaluation.

[0084] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on land during the evaluation process. For example, the evaluation unit can perform its evaluation by referring to past research papers on land. It can also perform its evaluation by referring to past survey reports on land. Furthermore, the evaluation unit can perform its evaluation by referring to literature on past laws and regulations concerning land. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input relevant literature data on land into AI, which can then improve the accuracy of the evaluation.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The reception desk can estimate the user's emotions and customize the input interface for land information based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of land information. This improves user convenience by customizing the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate emotions and customize the input interface based on the result.

[0087] The reception desk can analyze the user's past land information input history and suggest the optimal input method. For example, the reception desk can automatically display land information that the user has frequently entered in the past as a candidate. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest land information to be used at a specific time of day based on the user's past input history. This streamlines the input process by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI, which can then suggest the optimal input method.

[0088] The reception desk can automatically adjust input fields based on the user's current projects and areas of interest when entering land information. For example, if the user is interested in residential projects, the reception desk will prioritize displaying residential-related input fields. Similarly, if the user is interested in commercial facility projects, the reception desk can prioritize displaying commercial facility-related input fields. Furthermore, if the user is interested in a specific region, the reception desk can automatically display input fields related to that region. This streamlines the input process by adjusting input fields based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk can input the user's areas of interest data into the AI, which can then automatically adjust the input fields.

[0089] The reception desk can estimate the user's emotions and determine the priority of the land information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize the input of important information and postpone the input of detailed information. Similarly, if the user is relaxed, the reception desk may also prioritize the input of detailed information. Furthermore, if the user is in a hurry, the reception desk may only input the most important information. This streamlines the input process by prioritizing the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate the emotions and determine the priority of the information to be entered based on the result.

[0090] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when inputting land information. For example, if the user is in a specific region, the reception unit can prioritize inputting land information related to that region. Furthermore, if the user is on the move, the reception unit can prioritize inputting relevant land information based on their current location. Additionally, if the user is in a specific city, the reception unit can prioritize inputting land information related to that city. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location information into the AI, which can then prioritize inputting highly relevant information.

[0091] The reception desk can analyze the user's social media activity when inputting land information and automatically input relevant information. For example, the reception desk can automatically input relevant land information based on location information shared by the user on social media. It can also automatically input land information related to areas the user has shown interest in on social media. Furthermore, the reception desk can automatically input relevant land information based on information obtained from real estate-related accounts that the user follows on social media. In this way, relevant information can be automatically input by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​can automatically input relevant information.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI, the generative AI can estimate the emotions, and the display method of the analysis results can be adjusted based on the result.

[0093] The analysis unit can select the optimal analysis method by referring to the land's past use history during the analysis. For example, if the land was previously used as residential land, the analysis unit will select an analysis method suitable for residential land. It can also select an analysis method suitable for commercial land if the land was previously used as commercial land. Furthermore, if the land was previously used as agricultural land, the analysis unit can select an analysis method suitable for agricultural land. This allows the optimal analysis method to be selected by referring to past use history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past land use history data into AI, which can then select the optimal analysis method.

[0094] The analysis unit can improve the accuracy of its analysis by considering the shape and geological information of the land. For example, if the shape of the land is irregular, the analysis unit can select an analysis method that suits that shape. Furthermore, if the soil of the land is soft, the analysis unit can select an analysis method that suits that soil type. In addition, the analysis unit can propose an optimal building plan based on the shape and geological information of the land. This improves the accuracy of the analysis by considering the shape and geological information of the land. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the shape and geological information of the land into the AI, which can then improve the accuracy of the analysis.

[0095] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying important analysis results. It can also prioritize displaying detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can display only the most important analysis results. This allows for the prioritization of important information by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of analysis results based on the results.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The reception desk enters the land information. This land information includes geographical location, area, and geological information. Users can enter information such as the location, area, and shape of the land. Step 2: The analysis unit analyzes the land information entered by the reception unit and derives a buildable plan considering legal regulations. The analysis unit can propose a buildable plan considering legal regulations such as land use zones, road width, height restrictions, and shadow restrictions. It can also propose multiple building plans. Step 3: The evaluation unit assesses the economic value of the land based on the building plan derived by the analysis unit. The evaluation unit can calculate the size and number of rooms of a building that can be constructed and assess the economic value of the land. It can also perform simulations based on the land's zoning regulations and legal restrictions to maximize the land's potential.

[0098] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0101] Each of the multiple elements described above, including the reception unit, analysis unit, and evaluation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input land information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the land information and derives a buildable plan considering legal regulations. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the economic value of the land based on the building plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, analysis unit, and evaluation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input land information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the land information and derives a buildable plan considering legal regulations. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which evaluates the economic value of the land based on the building plan. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input land information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, analyzing the land information and deriving a buildable plan considering legal regulations. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, evaluating the economic value of the land based on the building plan. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, and evaluation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing the user to input land information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the land information and derives a buildable plan considering legal regulations. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the economic value of the land based on the building plan. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0151] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) The reception area for entering land information, The analysis unit analyzes the land information entered by the reception unit and derives a buildable plan considering legal regulations, The system includes an evaluation unit that evaluates the economic value of the land based on the building plan derived by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We derive a buildable plan by taking into account legal regulations such as land use zones, road widths, height restrictions, and sunlight restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, We calculate the size and number of rooms of a building that can be constructed and evaluate the economic value of the land. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We propose multiple architectural plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Conduct simulations based on land use zones and legal regulations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for land information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past land information input history and propose the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering land information, the input fields are automatically adjusted based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of land information to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering land information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering land information, the system analyzes the user's social media activity and automatically enters relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the optimal analysis method is selected by referring to the land's past use history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by taking into account the shape of the land and geological information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the surrounding environmental information of the land will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, we refer to relevant land-related literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During the valuation process, the optimal valuation method is selected by referring to the land's past transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During the evaluation process, consider surrounding market data for the land to improve the accuracy of the valuation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, The system estimates the user's emotions and prioritizes evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, When evaluating a property, the geographical location of the land should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation process, we refer to relevant land literature to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area for entering land information, The analysis unit analyzes the land information entered by the reception unit and derives a buildable plan considering legal regulations, The system includes an evaluation unit that evaluates the economic value of the land based on the building plan derived by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, We derive a buildable plan by taking into account legal regulations such as land use zones, road widths, height restrictions, and sunlight restrictions. The system according to feature 1.

3. The evaluation unit described above, We calculate the size and number of rooms of a building that can be constructed and evaluate the economic value of the land. The system according to feature 1.

4. The aforementioned analysis unit, We propose multiple architectural plans. The system according to feature 1.

5. The evaluation unit described above, Conduct simulations based on land use zones and legal regulations. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for land information based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past land information input history and propose the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering land information, the input fields are automatically adjusted based on the user's current projects and areas of interest. The system according to feature 1.

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