system

Abstract

The system according to the embodiment aims to efficiently match underutilized land with individual investors who want to invest in renewable energy. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a registration unit, a search unit, and a matching unit. The collection unit collects data on land location information, sunshine hours, wind power, and topography. The analysis unit determines power generation efficiency based on the data collected by the collection unit. The registration unit allows landowners to register land information. The search unit allows individual investors to search for investment targets. The matching unit matches landowners with individual investors based on the land information registered by the registration unit and the investment target information searched by the search 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] Conventional technology has faced the challenge of making it difficult to efficiently match underutilized land with individual investors who want to invest in renewable energy.

[0005] The system according to the embodiment aims to efficiently match underutilized land with individual investors who want to invest in renewable energy. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a registration unit, a search unit, and a matching unit. The collection unit collects data on land location information, sunshine hours, wind power, and topography. The analysis unit determines power generation efficiency based on the data collected by the collection unit. The registration unit allows landowners to register land information. The search unit allows individual investors to search for investment targets. The matching unit matches landowners with individual investors based on the land information registered by the registration unit and the investment target information searched by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match underutilized land with individual investors who want to invest in renewable energy. [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 matching system according to an embodiment of the present invention is a system aimed at landowners whose land is not being effectively utilized and individual investors who wish to contribute to renewable energy, etc. This matching system collects data such as the land's location, sunshine hours, wind power, and topography, determines the power generation efficiency, and matches landowners with individual investors. This allows the matching system to expect that land that is not being effectively utilized will be used for renewable energy generation. Individual investors can also find investment opportunities that contribute to renewable energy. For example, a landowner can register their land, and an individual investor can invest in that land, resulting in the realization of a renewable energy power generation project. In this way, the effective use of land and the spread of renewable energy are promoted.

[0029] A matching system according to an embodiment includes a collection unit, an analysis unit, a registration unit, a search unit, and a matching unit. The collection unit collects data on land location, sunshine hours, wind power, and topography. For example, the collection unit acquires land location information using GPS coordinates. The collection unit can also acquire annual average sunshine hours from a weather database. The collection unit can also collect wind power data using an anemometer. The collection unit can also collect topography data using a topographical map. The analysis unit determines power generation efficiency based on the data collected by the collection unit. For example, the analysis unit calculates power generation efficiency by combining sunshine hours and wind power data. The analysis unit can also evaluate power generation efficiency by taking topography data into account. The analysis unit can also analyze data using AI to determine optimal power generation efficiency. The registration unit allows landowners to register land information. For example, the registration unit allows landowners to enter land area and owner information through an online form. The registration unit also allows landowners to upload photos of their land. The registration unit also allows landowners to describe the purpose of their land use. The search unit allows individual investors to search for investment targets. For example, the search unit allows individual investors to search for land information by entering keywords. The search unit also provides a filtering function to narrow down land information based on specific conditions. The search unit also provides a map display function to visually confirm the location of land. The matching unit matches landowners and individual investors based on the land information registered by the registration unit and the investment target information searched by the search unit. For example, the matching unit compares the power generation efficiency of the land with the investment conditions of the individual investor to perform optimal matching. The matching unit can also optimize the matching algorithm using AI. The matching unit can also provide a function to notify the matching results. As a result, the matching system according to the embodiment can collect data such as land location information, sunshine hours, wind power, and topography, determine power generation efficiency, and match landowners and individual investors.

[0030] The collection unit can analyze past data collection history and select an appropriate collection method. The collection unit, for example, selects the most efficient collection method from the past data collection history. The collection unit can also analyze the past data collection history and identify areas for improvement in the collection method. The collection unit can also customize the collection method based on the past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0031] The collection unit can filter data based on the landowner's intentions or conditions when collecting data. For example, the collection unit collects only specific data based on the landowner's intentions. The collection unit can also limit the range of data to be collected based on the landowner's conditions. The collection unit can also adjust the data to be collected by reflecting the landowner's feedback. This makes it possible to collect only necessary data by filtering data based on the landowner's intentions or conditions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the landowner's intention data into the generation AI and have the generation AI perform data filtering.

[0032] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects voice data. Furthermore, when the user uses text input, the collection unit can also collect text data. Furthermore, when the user uses image input, the collection unit can also collect image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the land. The collection unit, for example, prioritizes collecting highly relevant data based on the geographical location information of the land. The collection unit can also limit the range of data to be collected by taking into account the geographical location information of the land. The collection unit can also determine the priority of data to be collected based on the geographical location information of the land. This allows for efficient data collection by preferentially collecting highly relevant data by taking into account the geographical location information of the land. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the land to the generation AI and cause the generation AI to collect highly relevant data.

[0034] The collection unit can analyze the landowner's social media activity and collect relevant data when collecting data. For example, the collection unit analyzes the landowner's social media activity and collects relevant data. The collection unit can also limit the scope of data to be collected based on the landowner's social media activity. The collection unit can also customize the data to be collected by referring to the landowner's social media activity. This allows for efficient collection of relevant data by analyzing the landowner's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the landowner's social media data into the generation AI and have the generation AI collect relevant data.

[0035] The collection unit can customize the collection method by reflecting the landowner's past opinions when collecting data. The collection unit customizes the collection method based on, for example, the landowner's past feedback. The collection unit can also adjust the range of data to be collected by reflecting the landowner's past feedback. The collection unit can also improve the collection method by referring to the landowner's past feedback. In this way, the collection method can be optimized by reflecting the landowner's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the landowner's past feedback data into the generation AI and have the generation AI customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described 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 data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit selects an optimal analysis algorithm depending on the data category. The analysis unit can also customize the analysis algorithm based on the data category. The analysis unit can also apply different analysis algorithms depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, improves the analysis algorithm based on past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results. The analysis unit can also analyze past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to past analysis results. 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 past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also determine the analysis priority based on the time when the data was collected. The analysis unit can also prioritize analyzing the most recent data, leaving older data for later. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. 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 when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. The analysis unit can also prioritize analysis of highly relevant data, leaving less relevant data for later analysis. In this way, adjusting the order of analysis based on the relevance of the data allows for efficient analysis. Some or all of the above-described 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 data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terminology according to the user's level of expertise. The analysis unit can also provide analysis results in simple language to users with little expertise. The analysis unit can also provide analysis results using detailed technical terminology to users with extensive expertise. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for users to understand 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 user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] At the time of registration, the registration unit can analyze the landowner's past registration history and select the optimal registration method. For example, the registration unit selects the optimal registration method based on the landowner's past registration history. The registration unit can also analyze the landowner's past registration history and identify areas for improvement in the registration method. The registration unit can also customize the registration method by referring to the landowner's past registration history. In this way, the optimal registration method can be selected by analyzing the landowner's past registration history. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the landowner's past registration history data into the generation AI and have the generation AI select the optimal registration method.

[0043] The registration unit can customize the registration content based on the landowner's current conditions and intentions at the time of registration. The registration unit customizes the registration content based on the landowner's current conditions, for example. The registration unit can also adjust the registration content based on the landowner's intentions. The registration unit can also improve the registration content by reflecting the landowner's feedback. In this way, optimal registration content can be provided by customizing the registration content based on the landowner's current conditions and intentions. Some or all of the above-mentioned processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the landowner's current conditions and intention data into the generation AI and have the generation AI customize the registration content.

[0044] The registration unit can improve the registration method by reflecting the landowner's feedback at the time of registration. The registration unit can improve the registration method based on, for example, the landowner's feedback. The registration unit can also simplify the registration procedure by reflecting the landowner's feedback. The registration unit can also improve the registration interface by referring to the landowner's feedback. In this way, the registration method can be optimized by reflecting the landowner's feedback. Some or all of the above-mentioned processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the landowner's feedback data into the generation AI and cause the generation AI to improve the registration method.

[0045] At the time of registration, the registration unit can select the optimal registration method by taking into account the geographical location information of the land. For example, the registration unit selects the optimal registration method based on the geographical location information of the land. The registration unit can also customize the registration content by taking into account the geographical location information of the land. The registration unit can also adjust the registration procedure by referring to the geographical location information of the land. In this way, the optimal registration method can be selected by taking into account the geographical location information of the land. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the geographical location information of the land to the generation AI and cause the generation AI to select the optimal registration method.

[0046] At the time of registration, the registration unit can analyze the landowner's social media activity and suggest registration content. For example, the registration unit can analyze the landowner's social media activity and suggest relevant registration content. The registration unit can also customize the registration content based on the landowner's social media activity. The registration unit can also adjust the registration content by referring to the landowner's social media activity. In this way, relevant registration content can be suggested by analyzing the landowner's social media activity. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the landowner's social media data into a generation AI and have the generation AI suggest registration content.

[0047] The registration unit can customize the registration method by reflecting the landowner's past feedback at the time of registration. The registration unit customizes the registration method based on, for example, the landowner's past feedback. The registration unit can also adjust the registration content by reflecting the landowner's past feedback. The registration unit can also improve the registration method by referring to the landowner's past feedback. In this way, the registration method can be optimized by reflecting the landowner's past feedback. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the landowner's past feedback data into the generation AI and cause the generation AI to customize the registration method.

[0048] When searching, the search unit can analyze the individual investor's past search history and select the optimal search method. The search unit selects the optimal search method based on, for example, the individual investor's past search history. The search unit can also analyze the individual investor's past search history and identify areas for improvement in the search method. The search unit can also customize the search method by referring to the individual investor's past search history. In this way, the optimal search method can be selected by analyzing the individual investor's past search history. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the individual investor's past search history data into the generation AI and have the generation AI select the optimal search method.

[0049] The search unit can customize the search content based on the individual investor's current investment conditions and intentions when searching. The search unit customizes the search content based on, for example, the individual investor's current investment conditions. The search unit can also adjust the search content based on the individual investor's intentions. The search unit can also improve the search content by reflecting the individual investor's feedback. This makes it possible to provide optimal search results by customizing the search content based on the individual investor's current investment conditions and intentions. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the individual investor's current investment conditions and intention data into the generation AI and have the generation AI customize the search content.

[0050] The search unit can improve the search method by reflecting feedback from individual investors during a search. The search unit improves the search method, for example, based on feedback from individual investors. The search unit can also simplify the search procedure by reflecting feedback from individual investors. The search unit can also improve the search interface by referring to feedback from individual investors. In this way, the search method can be optimized by reflecting feedback from individual investors. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input feedback data from individual investors into the generation AI and cause the generation AI to improve the search method.

[0051] During a search, the search unit can select the optimal search method by taking into account the geographical location information of the land. For example, the search unit selects the optimal search method based on the geographical location information of the land. The search unit can also customize the search content by taking into account the geographical location information of the land. The search unit can also adjust the search procedure by referring to the geographical location information of the land. In this way, the optimal search method can be selected by taking into account the geographical location information of the land. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the geographical location information of the land into the generation AI and cause the generation AI to select the optimal search method.

[0052] The search unit can analyze the social media activity of the individual investor and suggest search content when searching. For example, the search unit can analyze the social media activity of the individual investor and suggest related search content. The search unit can also customize the search content based on the social media activity of the individual investor. The search unit can also adjust the search content by referring to the social media activity of the individual investor. In this way, relevant search content can be suggested by analyzing the social media activity of the individual investor. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the social media data of the individual investor into the generation AI and have the generation AI execute search content suggestions.

[0053] The search unit can customize the search method by reflecting the individual investor's past feedback when searching. The search unit customizes the search method based on, for example, the individual investor's past feedback. The search unit can also adjust the search content by reflecting the individual investor's past feedback. The search unit can also improve the search method by referring to the individual investor's past feedback. In this way, the search method can be optimized by reflecting the individual investor's past feedback. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the individual investor's past feedback data into the generation AI and have the generation AI customize the search method.

[0054] The matching unit can select the optimal matching method by analyzing the past matching history of the landowner and the individual investor when matching. For example, the matching unit selects the optimal matching method based on the past matching history of the landowner and the individual investor. The matching unit can also analyze the past matching history of the landowner and the individual investor to identify areas for improvement in the matching method. The matching unit can also customize the matching method by referring to the past matching history of the landowner and the individual investor. In this way, the optimal matching method can be selected by analyzing the past matching history of the landowner and the individual investor. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input past matching history data of the landowner and the individual investor into the generation AI and have the generation AI select the optimal matching method.

[0055] The matching unit can customize the matching content based on the current conditions and intentions of the landowner and the individual investor when matching. The matching unit customizes the matching content based on, for example, the current conditions of the landowner and the individual investor. The matching unit can also adjust the matching content based on the intentions of the landowner and the individual investor. The matching unit can also improve the matching content by reflecting feedback from the landowner and the individual investor. This makes it possible to provide optimal matching results by customizing the matching content based on the current conditions and intentions of the landowner and the individual investor. Some or all of the above-mentioned processing in the matching unit may be performed, for example, using AI or without AI. For example, the matching unit can input the current conditions and intention data of the landowner and the individual investor into the generation AI and have the generation AI customize the matching content.

[0056] The matching unit can improve the matching method by reflecting feedback from landowners and individual investors during matching. For example, the matching unit improves the matching method based on feedback from landowners and individual investors. The matching unit can also simplify the matching procedure by reflecting feedback from landowners and individual investors. The matching unit can also improve the matching interface by referring to feedback from landowners and individual investors. In this way, the matching method can be optimized by reflecting feedback from landowners and individual investors. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input feedback data from landowners and individual investors into the generation AI and have the generation AI improve the matching method.

[0057] The matching unit can select the optimal matching method during matching, taking into account the geographical location information of the land. For example, the matching unit selects the optimal matching method based on the geographical location information of the land. The matching unit can also customize the matching content by taking into account the geographical location information of the land. The matching unit can also adjust the matching procedure by referring to the geographical location information of the land. In this way, the optimal matching method can be selected by taking into account the geographical location information of the land. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the geographical location information of the land into the generation AI and cause the generation AI to select the optimal matching method.

[0058] The matching unit can analyze the social media activities of the landowner and the individual investor when matching and propose matching content. For example, the matching unit analyzes the social media activities of the landowner and the individual investor and proposes relevant matching content. The matching unit can also customize the matching content based on the social media activities of the landowner and the individual investor. The matching unit can also adjust the matching content by referring to the social media activities of the landowner and the individual investor. In this way, relevant matching content can be proposed by analyzing the social media activities of the landowner and the individual investor. Some or all of the above-mentioned processing in the matching unit may be performed, for example, using AI or may be performed without using AI. For example, the matching unit can input social media data of the landowner and the individual investor into a generation AI and have the generation AI execute a matching content proposal.

[0059] The matching unit can customize the matching method by reflecting past feedback from the landowner and the individual investor when matching. The matching unit customizes the matching method based on, for example, past feedback from the landowner and the individual investor. The matching unit can also adjust the matching content by reflecting past feedback from the landowner and the individual investor. The matching unit can also improve the matching method by referring to past feedback from the landowner and the individual investor. In this way, the matching method can be optimized by reflecting past feedback from the landowner and the individual investor. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input past feedback data from the landowner and the individual investor into the generation AI and have the generation AI customize the matching method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The matching system may further include an evaluation unit. The evaluation unit may collect user feedback on the matching results and improve the matching algorithm based on the feedback. For example, if the user is satisfied with the matching results, the algorithm may be maintained. Alternatively, if the user is dissatisfied, the algorithm may be adjusted to improve the accuracy of the next match. This allows the matching accuracy to be continuously improved by reflecting user feedback.

[0062] The matching system may further include a prediction unit. The prediction unit can analyze past matching data and predict the future matching success rate. For example, the prediction unit may predict the matching success rate under specific conditions and present the prediction to the user. The prediction unit may also analyze the user's investment trends and suggest optimal investment destinations. This allows the user to make investment decisions based on the future matching success rate.

[0063] The matching system can further include a recommendation unit. The recommendation unit can analyze the user's past behavior history and recommend optimal land or investment destinations. For example, if the user has previously invested in a specific region, the recommendation unit can recommend new investment destinations related to that region. The matching system can also analyze the user's investment trends and recommend land that may be of interest to the user. This allows the user to find the optimal investment destination that matches their investment trends.

[0064] The matching system can further include an analysis unit. The analysis unit can perform detailed analysis of the land's environmental data and conduct environmental impact assessments. For example, the analysis unit can analyze the land's soil and water quality data to assess the environmental impact of a renewable energy project. The analysis unit can also collect ecosystem data on the land and assess the impact of the project on the ecosystem. This makes it possible to realize environmentally friendly projects.

[0065] The matching system may further include a reporting unit. The reporting unit has the function of periodically reporting the matching results and investment status. For example, it may create a monthly report and send it to the user. The reporting unit may also report the progress of the investment project in real time. Furthermore, the reporting unit may analyze the user's investment performance and suggest areas for improvement. This allows the user to constantly understand the investment status and make appropriate investment decisions.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data on land location, sunshine hours, wind power, and topography. For example, the collection unit obtains land location information using GPS coordinates and annual average sunshine hours from a weather database. It also collects wind data using an anemometer and topography data using a topographical map. Step 2: The analysis unit determines the power generation efficiency based on the data collected by the collection unit. For example, it calculates the power generation efficiency by combining sunshine hours and wind data, and evaluates the power generation efficiency by taking into account topographical data. It is also possible to analyze the data using AI and determine the optimal power generation efficiency. Step 3: The registration department allows landowners to register land information. For example, landowners can enter land area and owner information through an online form, upload photos of the land, and describe the land's intended use. Step 4: The search section allows individual investors to search for investment targets. For example, individual investors can enter keywords to search for land information and use the filtering function to narrow down the land information based on specific conditions. A map display function is also provided, allowing users to visually check the location of the land. Step 5: The matching unit matches landowners with individual investors based on the land information registered by the registration unit and the investment information searched by the search unit. For example, it compares the power generation efficiency of the land with the investment conditions of the individual investor to make the optimal match. It is also possible to use AI to optimize the matching algorithm and provide a function to notify the matching results.

[0068] (Example 2) A matching system according to an embodiment of the present invention is a system aimed at landowners whose land is not being effectively utilized and individual investors who wish to contribute to renewable energy, etc. This matching system collects data such as the land's location, sunshine hours, wind power, and topography, determines the power generation efficiency, and matches landowners with individual investors. This allows the matching system to expect that land that is not being effectively utilized will be used for renewable energy generation. Individual investors can also find investment opportunities that contribute to renewable energy. For example, a landowner can register their land, and an individual investor can invest in that land, resulting in the realization of a renewable energy power generation project. In this way, the effective use of land and the spread of renewable energy are promoted.

[0069] A matching system according to an embodiment includes a collection unit, an analysis unit, a registration unit, a search unit, and a matching unit. The collection unit collects data on land location, sunshine hours, wind power, and topography. For example, the collection unit acquires land location information using GPS coordinates. The collection unit can also acquire annual average sunshine hours from a weather database. The collection unit can also collect wind power data using an anemometer. The collection unit can also collect topography data using a topographical map. The analysis unit determines power generation efficiency based on the data collected by the collection unit. For example, the analysis unit calculates power generation efficiency by combining sunshine hours and wind power data. The analysis unit can also evaluate power generation efficiency by taking topography data into account. The analysis unit can also analyze data using AI to determine optimal power generation efficiency. The registration unit allows landowners to register land information. For example, the registration unit allows landowners to enter land area and owner information through an online form. The registration unit also allows landowners to upload photos of their land. The registration unit also allows landowners to describe the purpose of their land use. The search unit allows individual investors to search for investment targets. For example, the search unit allows individual investors to search for land information by entering keywords. The search unit also provides a filtering function to narrow down land information based on specific conditions. The search unit also provides a map display function to visually confirm the location of land. The matching unit matches landowners and individual investors based on the land information registered by the registration unit and the investment target information searched by the search unit. For example, the matching unit compares the power generation efficiency of the land with the investment conditions of the individual investor to perform optimal matching. The matching unit can also optimize the matching algorithm using AI. The matching unit can also provide a function to notify the matching results. As a result, the matching system according to the embodiment can collect data such as land location information, sunshine hours, wind power, and topography, determine power generation efficiency, and match landowners and individual investors.

[0070] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection and collect detailed data. Furthermore, if the user is in a hurry, the collection unit can adjust the timing of data collection to quickly collect necessary data. This reduces the user's burden by adjusting the timing of data collection 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 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0071] The collection unit can analyze past data collection history and select an appropriate collection method. The collection unit, for example, selects the most efficient collection method from the past data collection history. The collection unit can also analyze the past data collection history and identify areas for improvement in the collection method. The collection unit can also customize the collection method based on the past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0072] The collection unit can filter data based on the landowner's intentions or conditions when collecting data. For example, the collection unit collects only specific data based on the landowner's intentions. The collection unit can also limit the range of data to be collected based on the landowner's conditions. The collection unit can also adjust the data to be collected by reflecting the landowner's feedback. This makes it possible to collect only necessary data by filtering data based on the landowner's intentions or conditions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the landowner's intention data into the generation AI and have the generation AI perform data filtering.

[0073] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects voice data. Furthermore, when the user uses text input, the collection unit can also collect text data. Furthermore, when the user uses image input, the collection unit can also collect image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0074] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can postpone the collection of less important data. Furthermore, if the user is relaxed, the collection unit can prioritize the collection of detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize the collection of more important data. Thus, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. The 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 collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0075] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the land. The collection unit, for example, prioritizes collecting highly relevant data based on the geographical location information of the land. The collection unit can also limit the range of data to be collected by taking into account the geographical location information of the land. The collection unit can also determine the priority of data to be collected based on the geographical location information of the land. This allows for efficient data collection by preferentially collecting highly relevant data by taking into account the geographical location information of the land. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the land to the generation AI and cause the generation AI to collect highly relevant data.

[0076] The collection unit can analyze the landowner's social media activity and collect relevant data when collecting data. For example, the collection unit analyzes the landowner's social media activity and collects relevant data. The collection unit can also limit the scope of data to be collected based on the landowner's social media activity. The collection unit can also customize the data to be collected by referring to the landowner's social media activity. This allows for efficient collection of relevant data by analyzing the landowner's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the landowner's social media data into the generation AI and have the generation AI collect relevant data.

[0077] The collection unit can customize the collection method by reflecting the landowner's past opinions when collecting data. The collection unit customizes the collection method based on, for example, the landowner's past feedback. The collection unit can also adjust the range of data to be collected by reflecting the landowner's past feedback. The collection unit can also improve the collection method by referring to the landowner's past feedback. In this way, the collection method can be optimized by reflecting the landowner's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the landowner's past feedback data into the generation AI and have the generation AI customize the collection method.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described 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 data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit selects an optimal analysis algorithm depending on the data category. The analysis unit can also customize the analysis algorithm based on the data category. The analysis unit can also apply different analysis algorithms depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, improves the analysis algorithm based on past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results. The analysis unit can also analyze past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to past analysis results. 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 past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0082] 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. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0083] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also determine the analysis priority based on the time when the data was collected. The analysis unit can also prioritize analyzing the most recent data, leaving older data for later. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. 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 when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. The analysis unit can also prioritize analysis of highly relevant data, leaving less relevant data for later analysis. In this way, adjusting the order of analysis based on the relevance of the data allows for efficient analysis. Some or all of the above-described 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 data to the generation AI and cause the generation AI to adjust the order of analysis.

[0085] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terminology according to the user's level of expertise. The analysis unit can also provide analysis results in simple language to users with little expertise. The analysis unit can also provide analysis results using detailed technical terminology to users with extensive expertise. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for users to understand 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 user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0086] The registration unit can estimate the user's emotions and adjust the registration method based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can provide a simple interface and minimize the registration procedure. Furthermore, if the user is relaxed, the registration unit can provide detailed input options and suggest a customizable registration method. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input to enable quick registration. This allows the registration method to be adjusted according to the user's emotions, thereby providing the optimal registration method for the user. 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 registration unit can be performed using, for example, AI, or without AI. For example, the registration unit can input the user's emotion data into the generation AI and have the generation AI adjust the registration method.

[0087] At the time of registration, the registration unit can analyze the landowner's past registration history and select the optimal registration method. For example, the registration unit selects the optimal registration method based on the landowner's past registration history. The registration unit can also analyze the landowner's past registration history and identify areas for improvement in the registration method. The registration unit can also customize the registration method by referring to the landowner's past registration history. In this way, the optimal registration method can be selected by analyzing the landowner's past registration history. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the landowner's past registration history data into the generation AI and have the generation AI select the optimal registration method.

[0088] The registration unit can customize the registration content based on the landowner's current conditions and intentions at the time of registration. The registration unit customizes the registration content based on the landowner's current conditions, for example. The registration unit can also adjust the registration content based on the landowner's intentions. The registration unit can also improve the registration content by reflecting the landowner's feedback. In this way, optimal registration content can be provided by customizing the registration content based on the landowner's current conditions and intentions. Some or all of the above-mentioned processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the landowner's current conditions and intention data into the generation AI and have the generation AI customize the registration content.

[0089] The registration unit can improve the registration method by reflecting the landowner's feedback at the time of registration. The registration unit can improve the registration method based on, for example, the landowner's feedback. The registration unit can also simplify the registration procedure by reflecting the landowner's feedback. The registration unit can also improve the registration interface by referring to the landowner's feedback. In this way, the registration method can be optimized by reflecting the landowner's feedback. Some or all of the above-mentioned processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the landowner's feedback data into the generation AI and cause the generation AI to improve the registration method.

[0090] The registration unit can estimate the user's emotions and determine the registration priority based on the estimated user's emotions. For example, if the user is feeling stressed, the registration unit postpones registration of less important items. Furthermore, if the user is relaxed, the registration unit can prioritize detailed registration. Furthermore, if the user is in a hurry, the registration unit can prioritize registration of more important items. Thus, by determining the registration priority according to the user's emotions, important registrations can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, 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 registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the registration unit can input the user's emotion data into the generation AI and have the generation AI determine the registration priority.

[0091] At the time of registration, the registration unit can select the optimal registration method by taking into account the geographical location information of the land. For example, the registration unit selects the optimal registration method based on the geographical location information of the land. The registration unit can also customize the registration content by taking into account the geographical location information of the land. The registration unit can also adjust the registration procedure by referring to the geographical location information of the land. In this way, the optimal registration method can be selected by taking into account the geographical location information of the land. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the geographical location information of the land to the generation AI and cause the generation AI to select the optimal registration method.

[0092] At the time of registration, the registration unit can analyze the landowner's social media activity and suggest registration content. For example, the registration unit can analyze the landowner's social media activity and suggest relevant registration content. The registration unit can also customize the registration content based on the landowner's social media activity. The registration unit can also adjust the registration content by referring to the landowner's social media activity. In this way, relevant registration content can be suggested by analyzing the landowner's social media activity. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the landowner's social media data into a generation AI and have the generation AI suggest registration content.

[0093] The registration unit can customize the registration method by reflecting the landowner's past feedback at the time of registration. The registration unit customizes the registration method based on, for example, the landowner's past feedback. The registration unit can also adjust the registration content by reflecting the landowner's past feedback. The registration unit can also improve the registration method by referring to the landowner's past feedback. In this way, the registration method can be optimized by reflecting the landowner's past feedback. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the landowner's past feedback data into the generation AI and cause the generation AI to customize the registration method.

[0094] The search unit can estimate the user's emotions and adjust the search method based on the estimated user emotions. For example, the search unit can provide a simple search interface when the user is stressed. The search unit can also provide detailed search options when the user is relaxed. The search unit can also prioritize voice search and quickly display search results when the user is in a hurry. This allows the search method to be adjusted according to the user's emotions, thereby providing the optimal search method for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be 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-described processing in the search unit can be performed using AI, or without AI. For example, the search unit can input the user's emotion data into the generation AI and have the generation AI adjust the search method.

[0095] When searching, the search unit can analyze the individual investor's past search history and select the optimal search method. The search unit selects the optimal search method based on, for example, the individual investor's past search history. The search unit can also analyze the individual investor's past search history and identify areas for improvement in the search method. The search unit can also customize the search method by referring to the individual investor's past search history. In this way, the optimal search method can be selected by analyzing the individual investor's past search history. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the individual investor's past search history data into the generation AI and have the generation AI select the optimal search method.

[0096] The search unit can customize the search content based on the individual investor's current investment conditions and intentions when searching. The search unit customizes the search content based on, for example, the individual investor's current investment conditions. The search unit can also adjust the search content based on the individual investor's intentions. The search unit can also improve the search content by reflecting the individual investor's feedback. This makes it possible to provide optimal search results by customizing the search content based on the individual investor's current investment conditions and intentions. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the individual investor's current investment conditions and intention data into the generation AI and have the generation AI customize the search content.

[0097] The search unit can improve the search method by reflecting feedback from individual investors during a search. The search unit improves the search method, for example, based on feedback from individual investors. The search unit can also simplify the search procedure by reflecting feedback from individual investors. The search unit can also improve the search interface by referring to feedback from individual investors. In this way, the search method can be optimized by reflecting feedback from individual investors. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input feedback data from individual investors into the generation AI and cause the generation AI to improve the search method.

[0098] The search unit can estimate the user's emotions and determine search priorities based on the estimated user emotions. For example, if the user is feeling stressed, the search unit postpones searches of lower importance. The search unit can also prioritize detailed searches if the user is relaxed. The search unit can also prioritize searches of higher importance if the user is in a hurry. Thus, by determining search priorities according to the user's emotions, important searches can be prioritized. The 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 search unit can be performed using, for example, an AI, or without an AI. For example, the search unit can input the user's emotion data into the generation AI and have the generation AI determine the search priorities.

[0099] During a search, the search unit can select the optimal search method by taking into account the geographical location information of the land. For example, the search unit selects the optimal search method based on the geographical location information of the land. The search unit can also customize the search content by taking into account the geographical location information of the land. The search unit can also adjust the search procedure by referring to the geographical location information of the land. In this way, the optimal search method can be selected by taking into account the geographical location information of the land. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the geographical location information of the land into the generation AI and cause the generation AI to select the optimal search method.

[0100] The search unit can analyze the social media activity of the individual investor and suggest search content when searching. For example, the search unit can analyze the social media activity of the individual investor and suggest related search content. The search unit can also customize the search content based on the social media activity of the individual investor. The search unit can also adjust the search content by referring to the social media activity of the individual investor. In this way, relevant search content can be suggested by analyzing the social media activity of the individual investor. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the social media data of the individual investor into the generation AI and have the generation AI execute search content suggestions.

[0101] The search unit can customize the search method by reflecting the individual investor's past feedback when searching. The search unit customizes the search method based on, for example, the individual investor's past feedback. The search unit can also adjust the search content by reflecting the individual investor's past feedback. The search unit can also improve the search method by referring to the individual investor's past feedback. In this way, the search method can be optimized by reflecting the individual investor's past feedback. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the individual investor's past feedback data into the generation AI and have the generation AI customize the search method.

[0102] The matching unit can estimate the user's emotions and adjust the matching method based on the estimated user emotions. For example, the matching unit can provide a simple matching interface when the user is stressed. The matching unit can also provide detailed matching options when the user is relaxed. The matching unit can also quickly display matching results when the user is in a hurry. This allows the matching method to be adjusted according to the user's emotions, thereby providing the optimal matching method for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the matching unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the matching unit can input the user's emotion data into the generation AI and have the generation AI adjust the matching method.

[0103] The matching unit can select the optimal matching method by analyzing the past matching history of the landowner and the individual investor when matching. For example, the matching unit selects the optimal matching method based on the past matching history of the landowner and the individual investor. The matching unit can also analyze the past matching history of the landowner and the individual investor to identify areas for improvement in the matching method. The matching unit can also customize the matching method by referring to the past matching history of the landowner and the individual investor. In this way, the optimal matching method can be selected by analyzing the past matching history of the landowner and the individual investor. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input past matching history data of the landowner and the individual investor into the generation AI and have the generation AI select the optimal matching method.

[0104] The matching unit can customize the matching content based on the current conditions and intentions of the landowner and the individual investor when matching. The matching unit customizes the matching content based on, for example, the current conditions of the landowner and the individual investor. The matching unit can also adjust the matching content based on the intentions of the landowner and the individual investor. The matching unit can also improve the matching content by reflecting feedback from the landowner and the individual investor. This makes it possible to provide optimal matching results by customizing the matching content based on the current conditions and intentions of the landowner and the individual investor. Some or all of the above-mentioned processing in the matching unit may be performed, for example, using AI or without AI. For example, the matching unit can input the current conditions and intention data of the landowner and the individual investor into the generation AI and have the generation AI customize the matching content.

[0105] The matching unit can improve the matching method by reflecting feedback from landowners and individual investors during matching. For example, the matching unit improves the matching method based on feedback from landowners and individual investors. The matching unit can also simplify the matching procedure by reflecting feedback from landowners and individual investors. The matching unit can also improve the matching interface by referring to feedback from landowners and individual investors. In this way, the matching method can be optimized by reflecting feedback from landowners and individual investors. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input feedback data from landowners and individual investors into the generation AI and have the generation AI improve the matching method.

[0106] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated user emotions. For example, if the user is feeling stressed, the matching unit can postpone matching with lower importance. Furthermore, if the user is relaxed, the matching unit can prioritize detailed matching. Furthermore, if the user is in a hurry, the matching unit can prioritize matching with higher importance. Thus, by determining matching priorities according to the user's emotions, important matching can be prioritized. The 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 matching unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the matching unit can input the user's emotion data into the generation AI and have the generation AI determine the matching priorities.

[0107] The matching unit can select the optimal matching method during matching, taking into account the geographical location information of the land. For example, the matching unit selects the optimal matching method based on the geographical location information of the land. The matching unit can also customize the matching content by taking into account the geographical location information of the land. The matching unit can also adjust the matching procedure by referring to the geographical location information of the land. In this way, the optimal matching method can be selected by taking into account the geographical location information of the land. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the geographical location information of the land into the generation AI and cause the generation AI to select the optimal matching method.

[0108] The matching unit can analyze the social media activities of the landowner and the individual investor when matching and propose matching content. For example, the matching unit analyzes the social media activities of the landowner and the individual investor and proposes relevant matching content. The matching unit can also customize the matching content based on the social media activities of the landowner and the individual investor. The matching unit can also adjust the matching content by referring to the social media activities of the landowner and the individual investor. In this way, relevant matching content can be proposed by analyzing the social media activities of the landowner and the individual investor. Some or all of the above-mentioned processing in the matching unit may be performed, for example, using AI or may be performed without using AI. For example, the matching unit can input social media data of the landowner and the individual investor into a generation AI and have the generation AI execute a matching content proposal.

[0109] The matching unit can customize the matching method by reflecting past feedback from the landowner and the individual investor when matching. The matching unit customizes the matching method based on, for example, past feedback from the landowner and the individual investor. The matching unit can also adjust the matching content by reflecting past feedback from the landowner and the individual investor. The matching unit can also improve the matching method by referring to past feedback from the landowner and the individual investor. In this way, the matching method can be optimized by reflecting past feedback from the landowner and the individual investor. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input past feedback data from the landowner and the individual investor into the generation AI and have the generation AI customize the matching method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, registration unit, search unit, and matching 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 collection unit can collect land location information and topographical data using the camera 42 or GPS function of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and determines power generation efficiency based on the collected data. The registration unit allows landowners to register land information using the control unit 46A of the smart device 14. The search unit allows individual investors to search for investment targets using the control unit 46A of the smart device 14. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches landowners with individual investors. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, registration unit, search unit, and matching 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 collection unit can collect land location information and topographical data using the camera 42 and GPS function of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and determines power generation efficiency based on the collected data. The registration unit allows landowners to register land information using the control unit 46A of the smart glasses 214. The search unit allows individual investors to search for investment targets using the control unit 46A of the smart glasses 214. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches landowners with individual investors. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, registration unit, search unit, and matching 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 collection unit can collect land location information and topographical data using the camera 42 or GPS function of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and determines power generation efficiency based on the collected data. The registration unit allows landowners to register land information using the control unit 46A of the headset terminal 314. The search unit allows individual investors to search for investment targets using the control unit 46A of the headset terminal 314. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches landowners with individual investors. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, registration unit, search unit, and matching unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect land location information and topographical data using the camera 42 and GPS function of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and determines power generation efficiency based on the collected data. The registration unit allows landowners to register land information using the control unit 46A of the robot 414. The search unit allows individual investors to search for investment targets using the control unit 46A of the robot 414. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches landowners with individual investors.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The matching system may further include a notification unit. When notifying the user of the matching result, the notification unit may estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-understand notification may be sent. Alternatively, if the user is relaxed, a detailed notification may be sent. Furthermore, if the user is in a hurry, a quick notification may be sent. This makes it possible to provide the optimal notification method according to the user's emotions.

[0112] The matching system may further include an evaluation unit. The evaluation unit may collect user feedback on the matching results and improve the matching algorithm based on the feedback. For example, if the user is satisfied with the matching results, the algorithm may be maintained. Alternatively, if the user is dissatisfied, the algorithm may be adjusted to improve the accuracy of the next match. This allows the matching accuracy to be continuously improved by reflecting user feedback.

[0113] The matching system may further include a prediction unit. The prediction unit can analyze past matching data and predict the future matching success rate. For example, the prediction unit may predict the matching success rate under specific conditions and present the prediction to the user. The prediction unit may also analyze the user's investment trends and suggest optimal investment destinations. This allows the user to make investment decisions based on the future matching success rate.

[0114] The matching system may further include a learning unit. The learning unit may estimate the user's emotions and adjust the learning content based on the estimated emotions. For example, if the user is feeling stressed, simple learning content may be provided. If the user is relaxed, detailed learning content may be provided. Furthermore, if the user is in a hurry, learning content that focuses on the main points may be provided. In this way, it is possible to provide optimal learning content according to the user's emotions.

[0115] The matching system can further include a recommendation unit. The recommendation unit can analyze the user's past behavior history and recommend optimal land or investment destinations. For example, if the user has previously invested in a specific region, the recommendation unit can recommend new investment destinations related to that region. The matching system can also analyze the user's investment trends and recommend land that may be of interest to the user. This allows the user to find the optimal investment destination that matches their investment trends.

[0116] The matching system may further include an alert unit. The alert unit may estimate the user's emotions and adjust the content and timing of alerts based on the estimated emotions. For example, if the user is feeling stressed, only important alerts may be notified. If the user is relaxed, detailed alerts may be notified. Furthermore, if the user is in a hurry, quick alerts may be notified. This makes it possible to provide optimal alerts according to the user's emotions.

[0117] The matching system can further include an analysis unit. The analysis unit can perform detailed analysis of the land's environmental data and conduct environmental impact assessments. For example, the analysis unit can analyze the land's soil and water quality data to assess the environmental impact of a renewable energy project. The analysis unit can also collect ecosystem data on the land and assess the impact of the project on the ecosystem. This makes it possible to realize environmentally friendly projects.

[0118] The matching system may further include a customization unit. The customization unit may estimate the user's emotion and customize the system interface based on the estimated emotion. For example, if the user is feeling stressed, a simple and intuitive interface may be provided. If the user is feeling relaxed, detailed setting options may be provided. Furthermore, if the user is in a hurry, a quick-operation interface may be provided. In this way, the optimal interface may be provided according to the user's emotion.

[0119] The matching system may further include a reporting unit. The reporting unit has the function of periodically reporting the matching results and investment status. For example, it may create a monthly report and send it to the user. The reporting unit may also report the progress of the investment project in real time. Furthermore, the reporting unit may analyze the user's investment performance and suggest areas for improvement. This allows the user to constantly understand the investment status and make appropriate investment decisions.

[0120] The matching system may further include a support unit. The support unit may estimate the user's emotions and adjust the support content based on the estimated emotions. For example, if the user is feeling stressed, the support unit may provide quick and concise support. If the user is relaxed, the support unit may provide detailed support. Furthermore, if the user is in a hurry, the support unit may provide immediate support. In this way, optimal support can be provided according to the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects data on land location, sunshine hours, wind power, and topography. For example, the collection unit obtains land location information using GPS coordinates and annual average sunshine hours from a weather database. It also collects wind data using an anemometer and topography data using a topographical map. Step 2: The analysis unit determines the power generation efficiency based on the data collected by the collection unit. For example, it calculates the power generation efficiency by combining sunshine hours and wind data, and evaluates the power generation efficiency by taking into account topographical data. It is also possible to analyze the data using AI and determine the optimal power generation efficiency. Step 3: The registration department allows landowners to register land information. For example, landowners can enter land area and owner information through an online form, upload photos of the land, and describe the land's intended use. Step 4: The search section allows individual investors to search for investment targets. For example, individual investors can enter keywords to search for land information and use the filtering function to narrow down the land information based on specific conditions. A map display function is also provided, allowing users to visually check the location of the land. Step 5: The matching unit matches landowners with individual investors based on the land information registered by the registration unit and the investment information searched by the search unit. For example, it compares the power generation efficiency of the land with the investment conditions of the individual investor to make the optimal match. It is also possible to use AI to optimize the matching algorithm and provide a function to notify the matching results.

[0123] 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.

[0124] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0130] The 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.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data on land location, sunshine hours, wind power, and topography; an analysis unit that determines power generation efficiency based on the data collected by the collection unit; a registration unit where landowners register land information; A search section where individual investors search for investment targets; a matching unit that matches landowners with individual investors based on the land information registered by the registration unit and the investment destination information searched by the search unit; Equipped with A system characterized by:

2. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyze past data collection history and select appropriate collection methods 2. The system of claim 1.

4. The collecting unit When collecting data, filter it based on the landowner's wishes or conditions.

2. The system of claim 1.

5. The collecting unit When collecting data, select the optimal collection method depending on the user's input method 2. The system of claim 1.

6. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Prioritize the collection of relevant data based on the geographic location of the land during data collection 2. The system of claim 1.

8. The collecting unit During data collection, analyze landowners' online activities and collect relevant data 2. The system of claim 1.

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