system

Abstract

The system according to the embodiment aims to propose an optimal method for using land and to formulate a specific plan. [Solution] A system according to an embodiment includes an information input unit, an analysis unit, and a planning unit. The information input unit inputs land information. The analysis unit analyzes the information input by the information input unit and proposes ways to use the land. The planning unit formulates a specific plan based on the ways to use the land proposed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to find the optimal way to use land, and there was a problem that land was not being used effectively enough.

[0005] The system according to the embodiment aims to propose an optimal method for using land and to formulate a specific plan. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, an analysis unit, and a planning unit. The information input unit inputs land information. The analysis unit analyzes the information input by the information input unit and proposes ways to use the land. The planning unit formulates a specific plan based on the ways to use the land proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable method for using land and create a specific plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A land use proposal system according to an embodiment of the present invention uses AI to propose optimal land use methods. In this land use proposal system, a landowner inputs land information, and an AI analyzes the information to propose optimal land use methods and develop specific plans. For example, a landowner inputs information such as the land's location, area, topography, surrounding environment, historical background, and legal restrictions. This information is then input into an AI, which considers the land's characteristics and surrounding environment and proposes optimal uses, such as agricultural use, construction of a commercial facility, or residential development. The AI ​​then develops specific plans based on the proposed uses. For example, if the land is to be used as agricultural land, the AI ​​will develop detailed plans for the crops to be cultivated and the necessary facilities. If a commercial facility is to be built, the AI ​​will develop plans for the appropriate facilities and the necessary design. This system allows landowners to effectively utilize their land and create new business opportunities. It is also believed to bring a breath of fresh air to social issues. This land use proposal system allows landowners to effectively utilize their land and create new business opportunities. It can also bring a breath of fresh air to social issues.

[0029] A land use proposal system according to an embodiment includes an information input unit, an analysis unit, and a planning unit. The information input unit inputs land information. The land information includes, but is not limited to, for example, location, area, topography, surrounding environment, historical background, and legal constraints. The information input unit inputs, for example, the location of the land. The location is expressed as an address or latitude and longitude. The area is input in units such as square meters or acres. The topography includes types such as flat land, hills, and mountains, and the surrounding environment includes transportation access, commercial facilities, and the natural environment. The historical background includes past land use and cultural heritage, and the legal constraints include the Building Standards Act and the City Planning Act. The analysis unit uses AI to analyze the information input by the information input unit and proposes an optimal use of the land. For example, the analysis unit considers the characteristics of the land and the surrounding environment and proposes options such as using the land as farmland, building a commercial facility, or developing it into a residential area. The analysis unit uses AI to analyze the characteristics of the land. For example, the system analyzes soil quality, drainage, sunlight conditions, etc. The system also analyzes the surrounding environment, taking into account factors such as transportation access, the presence or absence of commercial facilities, and the natural environment. The planning unit creates a specific plan based on the use method proposed by the analysis unit. For example, if the land is to be used as farmland, the planning unit creates a detailed plan that determines which crops to cultivate and what facilities are needed. If a commercial facility is to be built, the planning unit creates a plan that determines what facilities are appropriate and what design is required. The planning unit uses AI to perform detailed simulations and cost analyses to create a feasible plan. For example, the system uses 3D modeling to simulate the construction plan. The cost analysis takes into account material costs, labor costs, operating costs, etc. This enables the land use proposal system according to the embodiment to efficiently input, analyze, and create plans for land information.

[0030] The information input unit can input information on the location, area, topography, surrounding environment, historical background, and legal constraints of the land. The information input unit, for example, inputs the location of the land. The location is expressed by an address or latitude and longitude. The area is input in units such as square meters or acres. The topography includes types such as flat land, hills, and mountains, and the surrounding environment includes transportation access, commercial facilities, and the natural environment. The historical background includes past land use and cultural heritage, and the legal constraints include the Building Standards Act and the City Planning Act. This allows for more accurate analysis and planning by inputting detailed information about the land. Some or all of the above-mentioned processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, when inputting the location of the land, the information input unit can automatically complete the address using AI.

[0031] The analysis unit can propose a utilization method based on the land characteristics and surrounding environment. The analysis unit, for example, analyzes the land characteristics. For example, it analyzes soil quality, drainage, sunlight conditions, etc. The analysis unit also analyzes the surrounding environment. For example, it considers transportation access, the presence or absence of commercial facilities, the natural environment, etc. In this way, by taking the land characteristics and surrounding environment into consideration, it is possible to propose a more appropriate utilization method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can automatically evaluate soil quality using AI when analyzing the land characteristics.

[0032] The planning unit can perform detailed simulations and cost analyses based on the proposed usage methods and create specific plans. For example, if the land is to be used as farmland, the planning unit creates detailed plans such as which crops to cultivate and what facilities are needed. For example, if a commercial facility is to be built, the planning unit creates plans such as what facilities are appropriate and what design is needed. The planning unit uses AI to perform detailed simulations and cost analyses to create feasible plans. For example, the planning unit uses 3D modeling to simulate construction plans. The cost analysis takes into account material costs, labor costs, operating costs, etc. This allows for the creation of feasible plans through detailed simulations and cost analyses. Some or all of the above-described processing in the planning unit may be performed using AI, for example, or may be performed without using AI. For example, the planning unit can automatically generate a model using AI when performing 3D modeling.

[0033] The planning unit can create a plan based on environmental protection and contribution to the local community. For example, the planning unit creates a plan that takes environmental protection into consideration. For example, the planning unit creates a design to minimize the ecological footprint. The planning unit can also create a plan that recommends the use of renewable energy. Furthermore, the planning unit creates a plan that takes contribution to the local community into consideration. For example, the planning unit creates a plan that aims to create jobs and revitalize the local economy. This allows for the creation of a sustainable plan by taking environmental protection and contribution to the local community into consideration. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can automatically evaluate the ecological footprint using AI when creating a design for environmental protection.

[0034] The information input unit can analyze past land information input history and select the optimal input method. For example, the information input unit can automatically display the land information format that the user has frequently input in the past as a candidate. The information input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the information input unit can predict and suggest the input method to be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit can input the past input history into AI and have the AI ​​perform an analysis to select the optimal input method.

[0035] When inputting land information, the information input unit can filter the land information based on the user's current project or area of ​​interest. For example, the information input unit preferentially inputs land information related to a project currently underway by the user. The information input unit can also filter related land information based on the user's area of ​​interest (agriculture, commerce, residential, etc.). Furthermore, the information input unit can suggest related land information based on areas in which the user has previously shown interest. This allows highly relevant information to be preferentially input by filtering based on the user's project or area of ​​interest. Some or all of the above-described processing in the information input unit may be performed using, or without, AI. For example, the information input unit can input the user's area of ​​interest to AI and have the AI ​​perform an analysis to filter related land information.

[0036] When inputting land information, the information input unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, the information input unit can prioritize inputting land information close to the user's current location. The information input unit can also prioritize inputting land information in areas that the user frequently visits. Furthermore, the information input unit can also prioritize inputting land information in areas in which the user is interested. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the information input unit may be performed using AI, for example, or may be performed without using AI. For example, the information input unit can input the user's geographical location information to AI and cause the AI ​​to perform an analysis to prioritize inputting highly relevant information.

[0037] When inputting land information, the information input unit can analyze the user's social media activity and input related information. For example, the information input unit automatically inputs land information shared by the user on social media. The information input unit can also preferentially input land information in which the user has shown interest on social media. Furthermore, the information input unit can suggest related land information based on the user's social media activity. This allows related information to be input efficiently by analyzing the user's social media activity. Some or all of the above-described processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit can input the user's social media activity into AI and have the AI ​​perform an analysis to input related information.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the land. For example, the analysis unit performs a detailed analysis on land with high importance. The analysis unit can also perform a brief analysis on land with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the land. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the land into AI and have the AI ​​perform an analysis to adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the land category. For example, in the case of agricultural land, the analysis unit can apply an analysis algorithm specialized for agriculture. In addition, in the case of commercial land, the analysis unit can also apply an analysis algorithm specialized for commercial facilities. Furthermore, in the case of residential land, the analysis unit can also apply an analysis algorithm specialized for housing development. In this way, by applying an analysis algorithm according to the land category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the land category into AI and have the AI ​​perform an analysis to select an appropriate analysis algorithm.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the land information. For example, the analysis unit prioritizes analysis of recently submitted land information. The analysis unit can also postpone analysis of land information that was submitted earlier. Furthermore, the analysis unit can adjust the priority of analysis according to the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of the land information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the land information into AI and have the AI ​​perform an analysis to determine the priority of analysis.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the land. For example, the analysis unit prioritizes analysis of highly relevant land information. The analysis unit can also postpone analysis of less relevant land information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the land. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the land into AI and have the AI ​​perform an analysis to adjust the order of analysis.

[0042] When formulating a plan, the planning unit can analyze the user's past land use behavior and select the optimal planning method. For example, the planning unit formulates a plan by referring to land use methods that the user has used successfully in the past. The planning unit can also formulate a plan to avoid the user's past failures. Furthermore, the planning unit can analyze the user's past behavior patterns and propose an optimal plan. In this way, an optimal planning method can be provided by analyzing the user's past land use behavior. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's past land use behavior into AI and have the AI ​​perform an analysis to select the optimal planning method.

[0043] The planning unit can customize the planning means based on the user's current living situation when creating a plan. For example, if the user is busy, the planning unit can provide a concise and feasible plan. Also, if the user has time, the planning unit can provide a detailed plan. Furthermore, the planning unit can adjust the content of the plan according to the user's living situation. This allows the planning means to be customized according to the user's living situation, making it possible to provide a more appropriate plan. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's living situation into AI and have the AI ​​perform an analysis to customize the planning means.

[0044] The planning unit can select an optimal planning method by taking into account the user's geographical location information when planning. For example, the planning unit prioritizes plans for land close to the user's current location. The planning unit can also prioritize plans for land in areas the user frequently visits. Furthermore, the planning unit can also prioritize plans for land in areas in which the user is interested. This makes it possible to provide an optimal planning method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's geographical location information into AI and cause the AI ​​to perform an analysis to select an optimal planning method.

[0045] The planning unit can analyze the user's social media activity and suggest planning methods when formulating a plan. For example, the planning unit formulates a plan based on land use ideas shared by the user on social media. The planning unit can also prioritize land use methods in which the user has expressed interest on social media. Furthermore, the planning unit can suggest related planning methods based on the user's social media activity. In this way, related planning methods can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's social media activity into AI and have the AI ​​perform an analysis to suggest planning methods.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The information input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display input formats that the user has frequently used in the past as candidates. It can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the input method that will be used during a specific time period based on the user's past input history. In this way, it is possible to provide the optimal input method to the user by analyzing the past input history.

[0048] The analysis unit can take into account the historical background of the land when proposing uses based on the land's characteristics. For example, it can propose uses as a tourist destination, taking into account past land use and cultural heritage. It can also propose uses that protect cultural value based on the historical background. It can also propose uses that respect local traditions, taking into account the historical background. This makes it possible to propose more appropriate uses by taking into account the historical background of the land.

[0049] The planning department can perform detailed simulations and cost analyses based on the proposed use and create specific plans. For example, if the land is to be used as farmland, a detailed plan will be created, including which crops to cultivate and what facilities will be needed. If a commercial facility is to be built, a plan will be created, including what type of facility is appropriate and what design is required. The planning department uses AI to perform detailed simulations and cost analyses and create feasible plans. For example, 3D modeling is used to simulate construction plans. Cost analyses take into account material costs, labor costs, operating costs, etc. This allows for detailed simulations and cost analyses to create feasible plans.

[0050] The planning department can create plans based on environmental protection and contributions to the local community. For example, they can create designs to minimize ecological footprints. They can also create plans that encourage the use of renewable energy. They can also create plans that take into account contributions to the local community. For example, they can create plans that aim to create jobs and revitalize the local economy. This allows them to create sustainable plans by taking into account environmental protection and contributions to the local community.

[0051] During analysis, the analysis unit can apply different analysis algorithms depending on the land category. For example, in the case of agricultural land, an analysis algorithm specialized for agriculture can be applied. In addition, in the case of commercial land, an analysis algorithm specialized for commercial facilities can be applied. Furthermore, in the case of residential land, an analysis algorithm specialized for housing development can be applied. In this way, by applying an analysis algorithm according to the land category, more accurate analysis results can be provided.

[0052] When creating a plan, the planning unit can analyze the user's past land use behavior and select the optimal planning method. For example, the planning unit can create a plan by referring to land use methods that the user has used successfully in the past. It can also create a plan to avoid the user's past failures. Furthermore, it can analyze the user's past behavior patterns and propose the optimal plan. In this way, the optimal planning method can be provided by analyzing the user's past land use behavior.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The information input unit inputs land information. Land information includes location, area, topography, surrounding environment, historical background, legal restrictions, etc. For example, location is expressed as an address or latitude and longitude, and area is input in units such as square meters or acres. Topography includes types such as flat land, hills, and mountains, and surrounding environment includes transportation access, commercial facilities, and the natural environment. Historical background includes past land use and cultural heritage, and legal restrictions include the Building Standards Act and City Planning Act. Step 2: The analysis unit uses AI to analyze the information entered by the information input unit and proposes the optimal way to use the land. For example, taking into account the characteristics of the land and the surrounding environment, it will propose using it as farmland, building a commercial facility, or developing it into a residential area. The analysis unit analyzes soil quality, drainage, sunlight conditions, etc., and also analyzes the surrounding environment, taking into account factors such as transportation access, the presence or absence of commercial facilities, and the natural environment. Step 3: The planning department creates specific plans based on the usage methods proposed by the analysis department. For example, if the land is to be used as farmland, a detailed plan is created that determines which crops to cultivate and what facilities are needed. If a commercial facility is to be built, a plan is created that determines what type of facility is appropriate and what design is required. The planning department uses AI to perform detailed simulations and cost analyses and creates feasible plans. For example, 3D modeling is used to simulate construction plans, and cost analyses take into account material costs, labor costs, operating costs, and other expenses.

[0055] (Example 2) A land use proposal system according to an embodiment of the present invention uses AI to propose optimal land use methods. In this land use proposal system, a landowner inputs land information, and an AI analyzes the information to propose optimal land use methods and develop specific plans. For example, a landowner inputs information such as the land's location, area, topography, surrounding environment, historical background, and legal restrictions. This information is then input into an AI, which considers the land's characteristics and surrounding environment and proposes optimal uses, such as agricultural use, construction of a commercial facility, or residential development. The AI ​​then develops specific plans based on the proposed uses. For example, if the land is to be used as agricultural land, the AI ​​will develop detailed plans for the crops to be cultivated and the necessary facilities. If a commercial facility is to be built, the AI ​​will develop plans for the appropriate facilities and the necessary design. This system allows landowners to effectively utilize their land and create new business opportunities. It is also believed to bring a breath of fresh air to social issues. This land use proposal system allows landowners to effectively utilize their land and create new business opportunities. It can also bring a breath of fresh air to social issues.

[0056] A land use proposal system according to an embodiment includes an information input unit, an analysis unit, and a planning unit. The information input unit inputs land information. The land information includes, but is not limited to, for example, location, area, topography, surrounding environment, historical background, and legal constraints. The information input unit inputs, for example, the location of the land. The location is expressed as an address or latitude and longitude. The area is input in units such as square meters or acres. The topography includes types such as flat land, hills, and mountains, and the surrounding environment includes transportation access, commercial facilities, and the natural environment. The historical background includes past land use and cultural heritage, and the legal constraints include the Building Standards Act and the City Planning Act. The analysis unit uses AI to analyze the information input by the information input unit and proposes an optimal use of the land. For example, the analysis unit considers the characteristics of the land and the surrounding environment and proposes options such as using the land as farmland, building a commercial facility, or developing it into a residential area. The analysis unit uses AI to analyze the characteristics of the land. For example, the system analyzes soil quality, drainage, sunlight conditions, etc. The system also analyzes the surrounding environment, taking into account factors such as transportation access, the presence or absence of commercial facilities, and the natural environment. The planning unit creates a specific plan based on the use method proposed by the analysis unit. For example, if the land is to be used as farmland, the planning unit creates a detailed plan that determines which crops to cultivate and what facilities are needed. If a commercial facility is to be built, the planning unit creates a plan that determines what facilities are appropriate and what design is required. The planning unit uses AI to perform detailed simulations and cost analyses to create a feasible plan. For example, the system uses 3D modeling to simulate the construction plan. The cost analysis takes into account material costs, labor costs, operating costs, etc. This enables the land use proposal system according to the embodiment to efficiently input, analyze, and create plans for land information.

[0057] The information input unit can input information on the location, area, topography, surrounding environment, historical background, and legal constraints of the land. The information input unit, for example, inputs the location of the land. The location is expressed by an address or latitude and longitude. The area is input in units such as square meters or acres. The topography includes types such as flat land, hills, and mountains, and the surrounding environment includes transportation access, commercial facilities, and the natural environment. The historical background includes past land use and cultural heritage, and the legal constraints include the Building Standards Act and the City Planning Act. This allows for more accurate analysis and planning by inputting detailed information about the land. Some or all of the above-mentioned processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, when inputting the location of the land, the information input unit can automatically complete the address using AI.

[0058] The analysis unit can propose a utilization method based on the land characteristics and surrounding environment. The analysis unit, for example, analyzes the land characteristics. For example, it analyzes soil quality, drainage, sunlight conditions, etc. The analysis unit also analyzes the surrounding environment. For example, it considers transportation access, the presence or absence of commercial facilities, the natural environment, etc. In this way, by taking the land characteristics and surrounding environment into consideration, it is possible to propose a more appropriate utilization method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can automatically evaluate soil quality using AI when analyzing the land characteristics.

[0059] The planning unit can perform detailed simulations and cost analyses based on the proposed usage methods and create specific plans. For example, if the land is to be used as farmland, the planning unit creates detailed plans such as which crops to cultivate and what facilities are needed. For example, if a commercial facility is to be built, the planning unit creates plans such as what facilities are appropriate and what design is needed. The planning unit uses AI to perform detailed simulations and cost analyses to create feasible plans. For example, the planning unit uses 3D modeling to simulate construction plans. The cost analysis takes into account material costs, labor costs, operating costs, etc. This allows for the creation of feasible plans through detailed simulations and cost analyses. Some or all of the above-described processing in the planning unit may be performed using AI, for example, or may be performed without using AI. For example, the planning unit can automatically generate a model using AI when performing 3D modeling.

[0060] The planning unit can create a plan based on environmental protection and contribution to the local community. For example, the planning unit creates a plan that takes environmental protection into consideration. For example, the planning unit creates a design to minimize the ecological footprint. The planning unit can also create a plan that recommends the use of renewable energy. Furthermore, the planning unit creates a plan that takes contribution to the local community into consideration. For example, the planning unit creates a plan that aims to create jobs and revitalize the local economy. This allows for the creation of a sustainable plan by taking environmental protection and contribution to the local community into consideration. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can automatically evaluate the ecological footprint using AI when creating a design for environmental protection.

[0061] The information input unit can estimate the user's emotions and adjust the timing of inputting land information based on the estimated user emotions. For example, if the user is feeling stressed, the information input unit can pause input and provide a relaxing interface. Furthermore, if the user is relaxed, the information input unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the information input unit can prioritize voice input and enable quick input of land information. This allows for a more comfortable input experience by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information input unit can be performed using, for example, AI, or without AI. For example, the information input unit can capture the user's facial expression with a camera and input it into the generation AI to estimate emotions.

[0062] The information input unit can analyze past land information input history and select the optimal input method. For example, the information input unit can automatically display the land information format that the user has frequently input in the past as a candidate. The information input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the information input unit can predict and suggest the input method to be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit can input the past input history into AI and have the AI ​​perform an analysis to select the optimal input method.

[0063] When inputting land information, the information input unit can filter the land information based on the user's current project or area of ​​interest. For example, the information input unit preferentially inputs land information related to a project currently underway by the user. The information input unit can also filter related land information based on the user's area of ​​interest (agriculture, commerce, residential, etc.). Furthermore, the information input unit can suggest related land information based on areas in which the user has previously shown interest. This allows highly relevant information to be preferentially input by filtering based on the user's project or area of ​​interest. Some or all of the above-described processing in the information input unit may be performed using, or without, AI. For example, the information input unit can input the user's area of ​​interest to AI and have the AI ​​perform an analysis to filter related land information.

[0064] The information input unit can estimate the user's emotions and determine the priority of land information to be input based on the estimated user emotions. For example, when the user is feeling stressed, the information input unit postpones less important information and starts inputting simple information. The information input unit can also prioritize input of detailed information when the user is relaxed. Furthermore, when the user is in a hurry, the information input unit can prioritize input of the most important information. This enables efficient information input by determining the priority of information to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information input unit can be performed using, for example, AI, or without AI. For example, the information input unit can capture the user's facial expression with a camera and input it into the generation AI to estimate emotions.

[0065] When inputting land information, the information input unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, the information input unit can prioritize inputting land information close to the user's current location. The information input unit can also prioritize inputting land information in areas that the user frequently visits. Furthermore, the information input unit can also prioritize inputting land information in areas in which the user is interested. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the information input unit may be performed using AI, for example, or may be performed without using AI. For example, the information input unit can input the user's geographical location information to AI and cause the AI ​​to perform an analysis to prioritize inputting highly relevant information.

[0066] When inputting land information, the information input unit can analyze the user's social media activity and input related information. For example, the information input unit automatically inputs land information shared by the user on social media. The information input unit can also preferentially input land information in which the user has shown interest on social media. Furthermore, the information input unit can suggest related land information based on the user's social media activity. This allows related information to be input efficiently by analyzing the user's social media activity. Some or all of the above-described processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit can input the user's social media activity into AI and have the AI ​​perform an analysis to input related information.

[0067] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. This allows for adjusting the way the analysis is presented based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can capture the user's facial expression with a camera and input it into the generation AI to estimate emotions.

[0068] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the land. For example, the analysis unit performs a detailed analysis on land with high importance. The analysis unit can also perform a brief analysis on land with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the land. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the land into AI and have the AI ​​perform an analysis to adjust the level of detail of the analysis.

[0069] During analysis, the analysis unit can apply different analysis algorithms depending on the land category. For example, in the case of agricultural land, the analysis unit can apply an analysis algorithm specialized for agriculture. In addition, in the case of commercial land, the analysis unit can also apply an analysis algorithm specialized for commercial facilities. Furthermore, in the case of residential land, the analysis unit can also apply an analysis algorithm specialized for housing development. In this way, by applying an analysis algorithm according to the land category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the land category into AI and have the AI ​​perform an analysis to select an appropriate analysis algorithm.

[0070] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can capture the user's facial expression with a camera and input it into the generation AI to estimate emotions.

[0071] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the land information. For example, the analysis unit prioritizes analysis of recently submitted land information. The analysis unit can also postpone analysis of land information that was submitted earlier. Furthermore, the analysis unit can adjust the priority of analysis according to the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of the land information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the land information into AI and have the AI ​​perform an analysis to determine the priority of analysis.

[0072] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the land. For example, the analysis unit prioritizes analysis of highly relevant land information. The analysis unit can also postpone analysis of less relevant land information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the land. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the land into AI and have the AI ​​perform an analysis to adjust the order of analysis.

[0073] The planning unit can estimate the user's emotions and adjust the planning method based on the estimated user emotions. For example, if the user is relaxed, the planning unit can provide a detailed plan. If the user is in a hurry, the planning unit can also provide a concise plan that focuses on the main points. Furthermore, if the user is excited, the planning unit can also provide a plan with visually stimulating effects. This allows the planning method to be adjusted according to the user's emotions, thereby providing a more appropriate plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the planning unit can be performed using, for example, AI, or can be performed without using AI. For example, the planning unit can capture the user's facial expression with a camera and input it into the generation AI to estimate emotions.

[0074] When formulating a plan, the planning unit can analyze the user's past land use behavior and select the optimal planning method. For example, the planning unit formulates a plan by referring to land use methods that the user has used successfully in the past. The planning unit can also formulate a plan to avoid the user's past failures. Furthermore, the planning unit can analyze the user's past behavior patterns and propose an optimal plan. In this way, an optimal planning method can be provided by analyzing the user's past land use behavior. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's past land use behavior into AI and have the AI ​​perform an analysis to select the optimal planning method.

[0075] The planning unit can customize the planning means based on the user's current living situation when creating a plan. For example, if the user is busy, the planning unit can provide a concise and feasible plan. Also, if the user has time, the planning unit can provide a detailed plan. Furthermore, the planning unit can adjust the content of the plan according to the user's living situation. This allows the planning means to be customized according to the user's living situation, making it possible to provide a more appropriate plan. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's living situation into AI and have the AI ​​perform an analysis to customize the planning means.

[0076] The planning unit can estimate the user's emotions and determine the priority of planning based on the estimated user emotions. For example, when the user is feeling stressed, the planning unit postpones less important plans and starts with simple plans. Furthermore, when the user is relaxed, the planning unit can prioritize detailed plans. Furthermore, when the user is in a hurry, the planning unit can prioritize the most important plans. This enables efficient planning by determining the priority of planning according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the planning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the planning unit can capture the user's facial expression with a camera and input it into the generation AI to estimate emotions.

[0077] The planning unit can select an optimal planning method by taking into account the user's geographical location information when planning. For example, the planning unit prioritizes plans for land close to the user's current location. The planning unit can also prioritize plans for land in areas the user frequently visits. Furthermore, the planning unit can also prioritize plans for land in areas in which the user is interested. This makes it possible to provide an optimal planning method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's geographical location information into AI and cause the AI ​​to perform an analysis to select an optimal planning method.

[0078] The planning unit can analyze the user's social media activity and suggest planning methods when formulating a plan. For example, the planning unit formulates a plan based on land use ideas shared by the user on social media. The planning unit can also prioritize land use methods in which the user has expressed interest on social media. Furthermore, the planning unit can suggest related planning methods based on the user's social media activity. In this way, related planning methods can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's social media activity into AI and have the AI ​​perform an analysis to suggest planning methods. === Hard Collateral 1-1 === Each of the multiple elements including the information input unit, analysis unit, and planning unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information input unit is realized by the reception device 38 of the smart device 14, and inputs information such as the location and area of ​​land. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using AI and proposes an optimal usage method. The planning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and formulates a specific plan based on the proposed usage method. === Hard Collateral 1-2 === Each of the multiple elements including the information input unit, analysis unit, and planning unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information input unit is realized by the microphone 238 of the smart glasses 214, and land information is input by voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using AI and proposes an optimal usage method. The planning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a specific plan based on the proposed usage method. === Hard Collateral 1-3 === Each of the multiple elements including the information input unit, analysis unit, and planning unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the information input unit is realized by the microphone 238 of the headset terminal 314, and land information is input by voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using AI and proposes an optimal usage method. The planning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and formulates a specific plan based on the proposed usage method. === Hard Collateral 1-4 === Each of the multiple elements including the information input unit, analysis unit, and planning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information input unit is realized by the microphone 238 of the robot 414, and land information is input by voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using AI and proposes an optimal usage method. The planning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and formulates a specific plan based on the proposed usage method.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The information input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display input formats that the user has frequently used in the past as candidates. It can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the input method that will be used during a specific time period based on the user's past input history. In this way, it is possible to provide the optimal input method to the user by analyzing the past input history.

[0081] The analysis unit can take into account the historical background of the land when proposing uses based on the land's characteristics. For example, it can propose uses as a tourist destination, taking into account past land use and cultural heritage. It can also propose uses that protect cultural value based on the historical background. It can also propose uses that respect local traditions, taking into account the historical background. This makes it possible to propose more appropriate uses by taking into account the historical background of the land.

[0082] The planning department can perform detailed simulations and cost analyses based on the proposed use and create specific plans. For example, if the land is to be used as farmland, a detailed plan will be created, including which crops to cultivate and what facilities will be needed. If a commercial facility is to be built, a plan will be created, including what type of facility is appropriate and what design is required. The planning department uses AI to perform detailed simulations and cost analyses and create feasible plans. For example, 3D modeling is used to simulate construction plans. Cost analyses take into account material costs, labor costs, operating costs, etc. This allows for detailed simulations and cost analyses to create feasible plans.

[0083] The planning department can create plans based on environmental protection and contributions to the local community. For example, they can create designs to minimize ecological footprints. They can also create plans that encourage the use of renewable energy. They can also create plans that take into account contributions to the local community. For example, they can create plans that aim to create jobs and revitalize the local economy. This allows them to create sustainable plans by taking into account environmental protection and contributions to the local community.

[0084] The information input unit can estimate the user's emotions and adjust the timing of inputting land information based on the estimated user emotions. For example, if the user is feeling stressed, the information input unit can pause input and provide a relaxing interface. Also, if the user is relaxed, the information input unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the information input unit can prioritize voice input to allow the user to input land information quickly. This allows the input timing to be adjusted according to the user's emotions, providing a more comfortable input experience.

[0085] The information input unit can estimate the user's emotions and determine the priority of the land information to be input based on the estimated user's emotions. For example, if the user is feeling stressed, the less important information can be put off and the input can begin with simple information. Also, if the user is relaxed, detailed information can be input with priority. Furthermore, if the user is in a hurry, the most important information can be input with priority. In this way, the priority of the information to be input can be determined according to the user's emotions, enabling efficient information input.

[0086] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, analysis results with added visually stimulating effects can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0087] During analysis, the analysis unit can apply different analysis algorithms depending on the land category. For example, in the case of agricultural land, an analysis algorithm specialized for agriculture can be applied. In addition, in the case of commercial land, an analysis algorithm specialized for commercial facilities can be applied. Furthermore, in the case of residential land, an analysis algorithm specialized for housing development can be applied. In this way, by applying an analysis algorithm according to the land category, more accurate analysis results can be provided.

[0088] The planning unit can estimate the user's emotions and adjust the planning method based on the estimated user's emotions. For example, if the user is relaxed, a detailed plan can be provided. If the user is in a hurry, a concise plan that focuses on the main points can be provided. Furthermore, if the user is excited, a plan with visually stimulating effects can be provided. In this way, by adjusting the planning method according to the user's emotions, a more appropriate plan can be provided.

[0089] When creating a plan, the planning unit can analyze the user's past land use behavior and select the optimal planning method. For example, the planning unit can create a plan by referring to land use methods that the user has used successfully in the past. It can also create a plan to avoid the user's past failures. Furthermore, it can analyze the user's past behavior patterns and propose the optimal plan. In this way, the optimal planning method can be provided by analyzing the user's past land use behavior.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The information input unit inputs land information. Land information includes location, area, topography, surrounding environment, historical background, legal restrictions, etc. For example, location is expressed as an address or latitude and longitude, and area is input in units such as square meters or acres. Topography includes types such as flat land, hills, and mountains, and surrounding environment includes transportation access, commercial facilities, and the natural environment. Historical background includes past land use and cultural heritage, and legal restrictions include the Building Standards Act and City Planning Act. Step 2: The analysis unit uses AI to analyze the information entered by the information input unit and proposes the optimal way to use the land. For example, taking into account the characteristics of the land and the surrounding environment, it will propose using it as farmland, building a commercial facility, or developing it into a residential area. The analysis unit analyzes soil quality, drainage, sunlight conditions, etc., and also analyzes the surrounding environment, taking into account factors such as transportation access, the presence or absence of commercial facilities, and the natural environment. Step 3: The planning department creates specific plans based on the usage methods proposed by the analysis department. For example, if the land is to be used as farmland, a detailed plan is created that determines which crops to cultivate and what facilities are needed. If a commercial facility is to be built, a plan is created that determines what type of facility is appropriate and what design is required. The planning department uses AI to perform detailed simulations and cost analyses and creates feasible plans. For example, 3D modeling is used to simulate construction plans, and cost analyses take into account material costs, labor costs, operating costs, and other expenses.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

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

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0147] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0148] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0153] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0154] 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.

[0155] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0156] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0157] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0158] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0163] [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information input section for inputting land information; an analysis unit that analyzes the information input by the information input unit and proposes land use methods; a planning unit that formulates a specific plan based on the usage method proposed by the analysis unit. A system characterized by:

2. The information input unit Enter information about the location, area, topography, surrounding environment, historical background, and legal restrictions of the land.

2. The system of claim 1.

3. The analysis unit Propose ways to use the land based on its characteristics and surrounding environment 2. The system of claim 1.

4. The planning unit Based on the proposed usage, we will conduct detailed simulations and cost analyses to develop a concrete plan.

2. The system of claim 1.

5. The planning unit Develop a plan based on environmental protection and contributions to the local community 2. The system of claim 1.

6. The information input unit Estimate the user's emotions and adjust the timing of inputting land information based on the estimated user emotions.

2. The system of claim 1.

7. The information input unit Analyze past land information input history and select the optimal input method 2. The system of claim 1.

8. The information input unit Filter land information based on your current projects and areas of interest 2. The system of claim 1.

Images