system

Abstract

We provide the system. [Solution] A computer device used to create contract-related provisions and a means of managing contracts, A means of integrating geographic information and meteorological information to generate a dataset, A method for evaluating the power generation potential of land using a machine learning algorithm with this dataset, A means for identifying the most efficient energy generation location based on the aforementioned evaluation results, A means of matching users with candidate locations based on identified location information, A system that includes this.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the spread of renewable energy, especially solar power generation, it is important to select land with high power generation efficiency. However, there is a lack of means to appropriately analyze land information and meteorological information and efficiently identify optimal land. Also, there is no platform to realize a quick and appropriate matching between landowners and investors, so there is a problem that unused land cannot be effectively utilized.

Means for Solving the Problems

[0005] This invention provides a means for generating a dataset integrating geographic and meteorological information using a computer device that creates contract-related provisions, and for evaluating the power generation potential of land using a machine learning algorithm. This identifies the location where energy can be generated most efficiently, and based on this information, the system matches users with candidate locations. This matching process automatically generates combinations based on user information and provides solutions by notifying the relevant users.

[0006] A "computer device" is a device that includes hardware and software for processing information and performing specific functions.

[0007] "Geographic information" refers to location information and topographic data about specific points or regions, and includes datasets and their elements based on maps and coordinate systems.

[0008] "Weather information" refers to data related to the climate and weather in a specific region, and includes information such as sunshine amount, temperature, precipitation, and wind speed.

[0009] A "dataset" refers to a collection of data that has been gathered, integrated, and organized for a specific purpose, and is a set of information used for analysis and predictive models.

[0010] A "machine learning algorithm" is a general term for mathematical models and computational methods that learn patterns from data and automatically perform predictions and analyses.

[0011] "Power generation potential" is an indicator used to evaluate the efficiency or possibility of power generation in a particular area or location, and is usually expressed as predicted power generation or efficiency.

[0012] The term "energy-generating location" refers to a geographical location where energy is expected to be efficiently generated using renewable energy sources.

[0013] "Matching" refers to the process of combining multiple elements, such as landowners and investors, based on conditions and attributes, to build the optimal relationship. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that uses a computer device to effectively select the optimal land for renewable energy, particularly solar power generation, and to match users associated with that identified land.

[0036] In this system, the server first collects geographic and meteorological information from external databases and public APIs. Since the collected information exists in various formats, the server integrates this data into a unified dataset. Here, the data is prepared through preprocessing, such as imputing missing values ​​and removing unwanted noise.

[0037] After data integration and preprocessing are complete, the server runs a machine learning algorithm using the prepared dataset. This algorithm identifies land areas predicted to have high power generation efficiency based on land location, sunshine hours, topographic data, temperature, and other relevant weather data. The analysis results are scored as power generation potential and listed in descending order of efficiency.

[0038] The highly efficient land information identified by the server is cross-referenced with information on landowners and investors registered in the system. In this process, the server scores individual investors based on information such as their budget and desired yield to ensure appropriate matching. When a highly suitable match is found, the server automatically generates a matching proposal and notifies the user.

[0039] Users (investors and landowners) who receive a notification can use their device to view the details of the proposed match. Here, users can view predicted power generation efficiency data and investment simulation results, and accept the proposal if the conditions are met. If a contract is reached, the electronic contract procedure will be carried out through the system.

[0040] As a concrete example, suppose a user who owns unused land in Japan registers that land in the system. At the same time, an investor interested in renewable energy projects registers assets of 5 million yen and a desired return of 5% in the system. The server evaluates the power generation potential of the land based on domestic geographical and weather information, and if it is predicted to be highly efficient, it is added to a list of recommended properties for investors. Investors can check this list via their terminals, and if the conditions are met, a contract is made, the unused land of the landowner is put to effective use, and the investor has the opportunity to obtain a stable return. In this way, the system of the present invention supports the effective use of land resources and the promotion of investment.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects geographic and meteorological information from external databases and public APIs. This includes satellite imagery and time-of-day weather data, and is done by calling the appropriate APIs.

[0044] Step 2:

[0045] The server organizes the collected data by format and integrates it into a unified dataset. Here, data preprocessing is performed, such as integrating map information based on coordinate data and arranging weather data in chronological order.

[0046] Step 3:

[0047] The server runs a machine learning algorithm using pre-processed data. This algorithm evaluates power generation potential from sunlight, temperature, and topographic data, and calculates an efficiency score for each location.

[0048] Step 4:

[0049] The server lists land areas that are predicted to have high power generation efficiency based on the calculated efficiency score. This narrows down the list of high-priority land candidates.

[0050] Step 5:

[0051] Users register detailed information about the land they own and their desired conditions in the system. Investors similarly register details about their budget and desired return on investment.

[0052] Step 6:

[0053] The server calculates a matching score based on registered user information and previously identified land information, and automatically generates matching pairs that are deemed appropriate.

[0054] Step 7:

[0055] The terminal notifies the user of matching proposals received from the server. The user reviews the details and decides whether to accept the proposal based on the conditions.

[0056] Step 8:

[0057] If the user agrees to the proposal, they can proceed with the electronic contract procedure through their device. The system provides a contract signing process using digital signatures, and the contract is concluded.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] There is a need to select optimal land for renewable energy, particularly solar power generation, and to efficiently match that land with prospective investors and landowners. However, methods for accurately evaluating power generation potential using geographical and meteorological information and automatically matching land to individual investment conditions are not yet fully established. This invention solves these problems and provides the identification of land that can be expected to achieve maximum power generation efficiency and an appropriate investment matching process.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for using an information processing device to create contract-related provisions for managing contracts, means for integrating geographic and meteorological information to generate an information set, and means for using this information set to evaluate the power generation efficiency of land using a learning algorithm. This makes it possible to identify locations where efficient energy can be generated and to perform optimal investment matching.

[0063] An "information processing device" is a part of a computer system that has the function of managing contract-related regulations and data.

[0064] An "information set" is an integrated dataset containing geographic and meteorological information collected from various sources.

[0065] A "learning algorithm" is a mathematical method for recognizing patterns from data and making predictions; it is an algorithm that provides a specific calculation method.

[0066] "Power generation efficiency" is a numerical indicator used to evaluate the renewable energy production capacity of a particular area of ​​land.

[0067] An "energy-generating location" is a geographical area where renewable energy sources such as solar power are predicted to be able to be efficiently generated.

[0068] "Match" refers to a situation where the conditions and preferences of the user are compatible with those of a specified energy-generating location, thereby enabling the use or investment of that location.

[0069] A "predictive model" is a mathematical or statistical system used to predict future outcomes or characteristics based on input data.

[0070] This invention is a system for selecting optimal land for renewable energy, particularly solar power generation, and matching that land with prospective investors and landowners.

[0071] The server first collects geographic and meteorological information from external sources. This process utilizes common APIs and databases; specific examples include map APIs for geographic information and meteorological data APIs for meteorological information. Once this information is aggregated, the server uses data processing libraries such as Pandas and NumPy to integrate and preprocess the data. This includes imputing missing values ​​and removing noise. As a result, an integrated data set is generated.

[0072] Next, the server runs a learning algorithm using this prepared data. Specifically, it uses machine learning frameworks such as Scikit-learn and TENSORFLOW® to build a model that predicts power generation efficiency based on land location information, sunshine duration, topographic data, temperature, etc. Based on this prediction, highly efficient energy generation locations are scored and listed in descending order of efficiency.

[0073] Meanwhile, users use their devices to receive highly efficient land information suggested by the server based on their registered information. Here, users can check predicted power generation efficiency data and investment simulation results for each plot of land. If the conditions are met, they proceed with the contract procedures through their devices. Electronic contract platforms such as the DocuSign API are utilized to streamline this process.

[0074] As a concrete example, the server collects and analyzes data from 5,000 plots of land, narrowing down the top 100 locations with high power generation efficiency based on the investor's criteria. An example of a prompt that utilizes the generated AI model is, "Find the best unused land in Japan for an investor looking for a renewable energy investment project with a budget of 5 million yen and a desired return of 5%."

[0075] In this way, the system of the present invention realizes the effective utilization of land resources and the promotion of investment.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server collects geographic and meteorological information. It obtains data from geographic information APIs and meteorological data APIs as input. This provides raw data on regional climate conditions and geographical characteristics. Specifically, the server sends requests to the APIs and receives data including geographic coordinates, average sunshine hours, and annual precipitation. Based on this data, it compiles the basic information necessary for the initial evaluation of regional power generation efficiency.

[0079] Step 2:

[0080] The server converts the collected raw data into a unified format and performs preprocessing. The raw data obtained in step 1 is used as input. Data processing libraries (e.g., Pandas, NumPy) are used to impute missing values ​​with the mean and remove outliers. The output is a unified dataset with data consistency maintained. Specifically, the server generates a data frame, performs statistical analysis to detect outliers, and prepares cleaned data.

[0081] Step 3:

[0082] The server builds a predictive model for power generation efficiency using preprocessed data and performs the analysis. The integrated dataset obtained in step 2 is used as input. A machine learning framework (e.g., Scikit-learn, TensorFlow) is used to train the data-driven predictive model. The output is an analysis result that scores the power generation potential of each plot of land. Specifically, the server applies a random forest method to score the power generation efficiency of each plot of land based on location information and weather data, and lists them in descending order of efficiency.

[0083] Step 4:

[0084] The server uses a list of highly efficient land plots for power generation, cross-references it with user information, and performs optimal matching. The inputs used are the power generation potential score list generated in step 3 and the investment condition data registered by the user. A scoring algorithm is applied to generate the most suitable user-land combination. The output provides a proposal for the optimal match. Specifically, the server compares each user's desired yield and budget with the land's power generation efficiency to identify the combination that best meets the criteria.

[0085] Step 5:

[0086] Using the terminal, the user reviews the matching proposals notified by the server and examines the details. The matching proposals obtained in step 4 are used as input. The output is the proposal result selected by the user. Specifically, the user views power generation efficiency forecast data and investment simulations through the terminal interface and evaluates whether the conditions are met.

[0087] Step 6:

[0088] If the user agrees to the proposal, the contract process is initiated through the device. The user's accepted proposal data is used as input. An electronic contract API (e.g., DocuSign) is used to create and sign the contract. The output is data confirming the completion of the contract. Specifically, the user reviews the contract, electronically signs it, and receives notification that the contract has been officially completed.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] The challenge lies in effectively selecting land suitable for solar power generation as a renewable energy source and facilitating the swift and smooth conclusion of contracts between landowners and investors. Conventional technologies have resulted in a fragmented process, from land evaluation to matching and contract conclusion, involving many manual procedures and being inefficient. This has led to problems such as lost investment opportunities and insufficient promotion of the effective utilization of land resources.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means used to manage an information processing device that creates contract-related provisions, means for integrating geographic and meteorological information to generate a set of information, and means for using this set of information to evaluate the power generation capacity of a location using a predictive algorithm. This enables the identification of the most efficient energy-generating areas, matching users with candidate areas, and rapid contract conclusion through e-commerce.

[0094] An "information processing device" is a device designed for data input, processing, and output, and primarily has the function of creating and managing contract-related provisions.

[0095] "Means used for control" refers to methods or devices for controlling or operating a system or process according to a specific purpose.

[0096] "Geographic information" refers to information that provides physical and spatial data about a specific location or region.

[0097] "Meteorological information" refers to data on atmospheric conditions such as temperature, precipitation, and solar radiation, and is used to create prediction models.

[0098] "Means for generating information sets" refers to a method or apparatus for integrating data from various sources to create a single dataset tailored to a specific purpose.

[0099] A "predictive algorithm" is a computational method that uses collected data to calculate and predict future events and states.

[0100] "Power generation capacity" is an indicator that shows how much electricity a particular location or device can sustainably generate.

[0101] An "energy-generating area" refers to a geographical area that has been identified as being capable of efficiently generating energy using specific energy sources, such as solar power.

[0102] "Match" means that different elements or conditions are mutually compatible, and in this context, it refers to the user and the candidate region meeting each other's conditions.

[0103] "Electronic commerce" refers to the buying, selling, and contracting of goods and services through online networks such as the internet.

[0104] The system implementing this invention is built around an information processing device. The server first acquires geographic and meteorological information by utilizing external databases and APIs. Since this data exists in various formats, the server aggregates the various data and generates a unified set of information. In this process, data preprocessing tools are used as software to fill in missing information and remove unnecessary noise.

[0105] Next, the server uses the compiled information to run a prediction algorithm. This algorithm incorporates location data, sunshine duration, temperature, and topographic information to evaluate the power generation capacity of a specific area. This evaluation is scored numerically and listed in descending order of efficiency. This series of analyses often utilizes common machine learning algorithms.

[0106] The server uses identified high-efficiency areas to match them with user-registered criteria and generate potential matches. This process is automated by a scoring system that considers factors such as the investor's assets and desired returns. If a highly suitable match is found, the server automatically notifies the user of the suggested match.

[0107] Users with a device can receive notifications and view detailed information about suggested matches. They can review power generation forecast data and investment simulation results, and if the conditions are met, they can enter into a contract through e-commerce procedures. As an example of this process, a prompt such as "Which plots of land have high power generation potential?" can be used to provide support from a generative AI model.

[0108] Users can complete these procedures seamlessly on their smartphones or computers, enabling investment contracts for solar power generation to proceed quickly and efficiently. A concrete example would be considering investments in unused land within Japan. This system is expected to promote the more effective use of land resources and accelerate investment in renewable energy.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The server collects geographical and meteorological information from external databases and APIs. The input is a specified API endpoint or database query. The output is raw data in various formats. The server stores this data for later processing.

[0112] Step 2:

[0113] The server preprocesses the collected data. The input is the raw data collected in step 1. Data cleaning, such as noise reduction and missing value imputation, is performed to obtain a formatted set of information as output. As a result, the data is in a unified format and processed to be suitable for prediction algorithms.

[0114] Step 3:

[0115] The server applies a prediction algorithm using a formatted set of information. The input is prepared data. The server uses a machine learning model to predict and score the power generation capacity for each region. The output is a score list based on the power generation potential of each region. This score list is used to identify regions capable of effective energy generation.

[0116] Step 4:

[0117] The server generates user information and matching candidates based on scored data. Inputs are a score list and registered user criteria. The server automatically scores the best match, taking into account the user's budget and desired return on investment. The output is a list of suggested matches, which is then notified to the user.

[0118] Step 5:

[0119] The user receives notifications using their device and reviews the details of the provided matches. The input is a list of match suggestions from the server. The user reviews the power generation forecast and investment simulation data on the system and selects to proceed to the next step if the conditions are met. The output is the decision of whether or not to proceed with the contract.

[0120] Step 6:

[0121] The user completes the contract procedure through e-commerce via the terminal. The input consists of the user's final decision and contract information. The server executes the contract signing through the electronic contract system, and as a result, a formal contract document is generated. In this process, a "generative AI model" is used, and prompts such as "Which land has high power generation potential?" can be used to assist in verifying the information.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention is a system that utilizes a computer device to select the optimal land for renewable energy, matches it with users associated with that land, and incorporates an emotion engine to recognize the user's emotions.

[0124] In this system, the server first collects geographic and meteorological information from external databases and public APIs. Because the collected data is in different formats, the server integrates it to generate a dataset suitable for analysis. This dataset is based on factors such as sunshine duration, temperature, and topography, making it possible to evaluate power generation potential.

[0125] Next, the server uses machine learning algorithms to analyze the prepared dataset. Through this analysis, it evaluates the power generation efficiency of each plot of land and identifies the optimal plot based on the scored results. The identified plots of land are then matched with information on landowners and investors registered in the system.

[0126] When a user registers land details and investment conditions using a terminal, the server matches the registered information with identified land information. Furthermore, this invention incorporates an emotion engine, which includes a mechanism for recognizing the user's emotions. The emotion engine analyzes the user's responses and provides data for determining the appropriateness of the proposed content.

[0127] As a concrete example, if a user (investor) wishes to invest with a budget of 5 million yen and inputs their desired return on investment into the system, the server will list land with optimal power generation potential based on collected geographical and weather information. At this point, the emotion engine analyzes the user's facial expressions and tone of voice, and customizes the suggestions to reflect the predicted level of the user's interest and satisfaction. In this way, it is possible to provide more optimized suggestions to the user.

[0128] The user reviews the proposal, accepts it if the conditions are met, and then proceeds to the next step of electronic contract procedures. The terminal displays information about the contract on the screen and completes the process of concluding the contract using a digital signature. In this way, the system based on the present invention realizes the efficient utilization of land resources and the optimization of investment, and provides the user with a highly personalized experience.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The server collects geographical and meteorological information from external databases and public APIs. This includes satellite imagery, sunshine duration, and temperature for each region, and this information is efficiently retrieved using data acquisition scripts.

[0132] Step 2:

[0133] The server integrates the collected data to generate a dataset. This process standardizes data in different formats, unifies geographical coordinates, and organizes it as time-series data. Any missing or outlier data is imputed or corrected at this stage.

[0134] Step 3:

[0135] The server feeds the integrated dataset into a machine learning algorithm for analysis. The algorithm evaluates the power generation potential of each plot of land and performs scoring that comprehensively considers factors such as sunlight, topography, and temperature.

[0136] Step 4:

[0137] The server identifies land predicted to have high power generation efficiency based on the analysis results and creates a ranking from highest to lowest score. This information is stored in a database and made available for subsequent processes.

[0138] Step 5:

[0139] Users enter their land information or investment conditions into a terminal and register them in the system. This sends the location information, area, and details of their desired conditions to the server.

[0140] Step 6:

[0141] The server uses a matching algorithm based on user information and land efficiency data to generate the optimal combination. This takes into account factors such as funding budget, desired return on investment, and land location.

[0142] Step 7:

[0143] The emotion engine analyzes emotional data obtained through user interaction. The device inputs facial expressions and voice tone from the user's camera and microphone, and evaluates the user's response in real time.

[0144] Step 8:

[0145] The server adjusts the suggestions based on the analysis results of the emotion engine. It customizes the suggestions according to the user's level of interest and satisfaction, improving the accuracy of the matching.

[0146] Step 9:

[0147] The device notifies the user of the final matching proposal. The user reviews the proposal, and if the conditions are met, agrees and proceeds to the electronic contract process. The contract is finalized using a digital signature.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] In selecting land for optimal renewable energy utilization, there is a need to effectively integrate geographic and meteorological information to quickly and accurately identify land with high energy generation efficiency. Furthermore, there is a challenge in the process of appropriately matching users with land, as it has not yet been possible to provide individually optimized proposals that take into account the user's feelings.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for using an information processing device that generates contract-related provisions to manage contracts, means for generating an information set by combining geographic information and meteorological information, and means for evaluating the energy generation capacity of land using an information processing algorithm with the information set. This enables the rapid identification of land with high energy generation efficiency and the individual optimization of land that takes into account the feelings of users.

[0153] An "information processing device for generating contract-related provisions" is a dedicated information processing system for managing contract content and generating and editing agreed-upon terms and conditions in a digital format.

[0154] "Geographic information" refers to a dataset that includes location information, topography, land use, and other data about a specific place.

[0155] "Weather information" refers to information about weather conditions such as temperature, precipitation, and wind speed in a specific region, based on past data and forecasts.

[0156] An "information set" is a collection of data that has been integrated and organized from different formats and converted into a format suitable for analysis.

[0157] An "information processing algorithm" is a computational method that defines the procedures and rules for analyzing data.

[0158] "Land's energy generation capacity" refers to the potential of a particular piece of land to efficiently generate renewable energy.

[0159] An "emotion analysis device" is an information processing system that can detect and analyze a user's emotional state.

[0160] "Matching" is the process of finding relationships between different data based on registered information and identifying the optimal combination.

[0161] "Notification" refers to a method or means used to convey specific information or results to a user.

[0162] This system involves multiple steps to select the optimal land for renewable energy and match users with that land. The server collects geographic and meteorological information from external databases and public APIs. Specifically, the software uses geographic information systems (GIS) and meteorological data analysis tools to collect and analyze data related to location and weather conditions.

[0163] Because the collected data is in different formats, the server uses a data integration tool to standardize the format and generate an information set. This information set includes information such as sunshine duration, temperature, and topography, and by applying machine learning algorithms, the energy generation capacity of the land can be evaluated. This utilizes machine learning frameworks such as the Python library Scikit-learn and TensorFlow.

[0164] The server uses the evaluated land information to identify the most suitable land and provides information to the user's terminal based on this. When the user enters investment conditions using the terminal, the server receives the input information in encryption, matches it with the identified land information, and presents the optimal investment options. This process also utilizes an emotion analysis device to detect and analyze the user's emotions, and customizes the suggestions to the user.

[0165] As a concrete example, consider a user, an investor, who wishes to invest with a budget of 5 million yen. In this case, the user inputs their desired rate of return into the system. The server uses data such as sunshine hours and temperature to identify land with high power generation potential and makes investment suggestions based on that. At this time, an emotion analysis device can evaluate the user's response and improve the suggestions.

[0166] An example of a prompt to input into the generating AI model is: "Please propose the optimal renewable energy land investment with a budget of 5 million yen. Also, please evaluate whether the proposal resonates with the user's emotions."

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server collects geographic and meteorological information using external databases and public APIs. Specifically, it authenticates using an API key and retrieves data for a specified region. Inputs include regional codes and API keys, and outputs include geographic information and meteorological data for each region. Geographic Information Systems (GIS) and meteorological analysis tools are used at this stage.

[0170] Step 2:

[0171] The server integrates the collected geographic and meteorological information. This process uses data integration tools to standardize different data formats and generate a consistent data set. Inputs include geographic and meteorological information in raw data formats, and output is a data set in a format suitable for analysis. Data cleansing is also performed at this stage, including the imputation of missing values ​​and the removal of outliers.

[0172] Step 3:

[0173] The server evaluates the energy generation capacity of land using a data set. This process applies machine learning algorithms to operate a model that predicts power generation efficiency from the dataset. The input is an integrated data set, and the output is a list of power generation efficiency scores for each candidate site. Specifically, the model is trained and evaluated using libraries such as Scikit-learn and TensorFlow.

[0174] Step 4:

[0175] The server identifies the optimal land based on the scored results. This process sorts the land by score, selecting the location with the highest power generation efficiency. The input is a list of scored power generation efficiencies, and the output is information about the optimal land. The server stores this information in a management database.

[0176] Step 5:

[0177] Users input their investment conditions and preferences using a terminal. This information includes budget, desired return on investment, and regional preferences. The terminal encrypts this data and sends it to the server. This allows the server to share additional criteria necessary for selecting the most suitable land.

[0178] Step 6:

[0179] The server matches the user with appropriate land information based on their investment criteria. Inputs include the user's investment criteria and identified land information, and output is an investment proposal optimized for the user. At this stage, an emotion analysis device is also used to analyze the user's emotional state and adjust the proposal accordingly.

[0180] Step 7:

[0181] The user reviews the proposal through their device. Specifically, the device displays detailed information and conditions of the proposed land on the screen for the user to review. If the user accepts the investment, the electronic contract process proceeds, and the contract is concluded through digital signature. At this stage, the contractual provisions are applied, and the transaction is formally completed.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0184] To promote the widespread adoption of renewable energy, the challenge lies in efficiently selecting the optimal land and matching it with users. Furthermore, for the installation of charging and maintenance facilities for autonomous vehicles, it is necessary to quickly and effectively propose the most suitable land. In addition, it is essential to provide higher satisfaction by considering user sentiment and offering individually optimized proposals.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for integrating geographic and meteorological information to generate an information set, means for evaluating the power generation potential of land using machine learning techniques, and means for identifying the user's emotions and customizing suggestions. This enables efficient selection of energy-generating land and optimal suggestions tailored to the user's needs.

[0187] An "information processing device" is a device that receives data, processes it, and generates a specific output.

[0188] An "information set" is a collection of data that integrates different forms of data, such as geographical information and meteorological information.

[0189] "Machine learning techniques" are algorithms and processes used to make predictions and classifications based on data.

[0190] "Power generation potential" is an indicator of a particular piece of land's ability to generate energy.

[0191] "Geographic information" is a general term for information about specific land areas that are capable of generating energy.

[0192] A "user" is an individual or organization that uses this system to select land or perform matching.

[0193] A "candidate site" is land that has been identified by the system as suitable for energy generation.

[0194] "Identifying emotions" means determining a user's emotional state from their voice and facial expressions.

[0195] "Customizing a proposal" means individually adjusting the content of the proposal according to the user's response and needs.

[0196] To implement this invention, a server first functions as an information processing device, collecting geographic and meteorological information from external databases and public APIs. This data is then integrated into an information set. The server processes this information set and uses machine learning techniques to evaluate the power generation potential of the land. Based on the evaluation results, it identifies areas where efficient energy generation is possible.

[0197] Next, the terminal functions as an interface with the user. The user inputs their land selection needs and conditions through the terminal. This data is sent to the server, where the user is matched with identified candidate sites. The server uses emotion recognition software to identify the user's emotions and customizes the suggestions according to the user's individual needs. Specifically, OpenCV models are used for facial expression recognition, and TensorFlow models are used for speech emotion analysis.

[0198] As a concrete example, consider a case where a company wants to select a location to install a charging station for new autonomous vehicles. The company's representative inputs the requirements through an application. The server analyzes the collected data and proposes the optimal installation location. In this process, the system adjusts the proposal based on the representative's facial expressions and tone of voice, ensuring a high level of satisfaction.

[0199] An example of a prompt message would be: "We are looking for land to optimally locate a charging station for our new autonomous vehicles. When selecting a location, please consider power generation potential, accessibility, and future expandability."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The server collects geographic and meteorological information from external databases and public APIs. Since the collected data exists in various formats, it is integrated into a unified information set. This process standardizes the data format, enabling analysis in the next step. The input consists of geographic and meteorological information, as well as raw data; the output is the integrated information set.

[0203] Step 2:

[0204] The server evaluates the power generation potential of land using machine learning techniques based on the information set. Here, an analysis model is built using libraries such as Scikit-Learn, and the data is used to train the model, thereby performing evaluations for each plot of land. The input is an integrated information set, and the output is numerical data representing the evaluation result.

[0205] Step 3:

[0206] The server identifies candidate sites with efficient energy generation based on the evaluation results. It creates a list of candidate sites based on land information with high scores. The input is the evaluated numerical data, and the output is the list of candidate sites.

[0207] Step 4:

[0208] The terminal receives land selection requirements from the user. The user enters their desired conditions using a smart device. This information is sent to the server and used in the subsequent matching process. The input is the user's requirements data, and the output is the data sent to the server.

[0209] Step 5:

[0210] The server matches the user's requirements with a list of identified candidate locations and performs a matching process to propose the most suitable location. Here, filtering is performed based on the user's conditions to select the optimal proposed location. The input consists of the transmitted data and the candidate location list, and the output is the proposed optimal location information.

[0211] Step 6:

[0212] The terminal presents the user with the proposed optimal location information. The process proceeds only if the user makes a selection. The input is the proposed information from the server, and the output is the information presented to the user.

[0213] Step 7:

[0214] The server uses emotion recognition software to analyze the user's responses and identify the user's emotional state. This analysis is used to customize suggestions and generate more appropriate recommendations. The input is the user's facial expressions and voice data, and the output is emotional state judgment data and customized suggestions.

[0215] Step 8:

[0216] Once the user formally accepts the proposal, the contract requirements are entered from the terminal, and the electronic contract is concluded with a digital signature. This completes the entire process. The input is the user's consent data, and the output is the concluded contract information.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention is a system that uses a computer device to effectively select the optimal land for renewable energy, particularly solar power generation, and to match users associated with that identified land.

[0234] In this system, the server first collects geographic and meteorological information from external databases and public APIs. Since the collected information exists in various formats, the server integrates this data into a unified dataset. Here, the data is prepared through preprocessing, such as imputing missing values ​​and removing unwanted noise.

[0235] After data integration and preprocessing are complete, the server runs a machine learning algorithm using the prepared dataset. This algorithm identifies land areas predicted to have high power generation efficiency based on land location, sunshine hours, topographic data, temperature, and other relevant weather data. The analysis results are scored as power generation potential and listed in descending order of efficiency.

[0236] The highly efficient land information identified by the server is cross-referenced with information on landowners and investors registered in the system. In this process, the server scores individual investors based on information such as their budget and desired yield to ensure appropriate matching. When a highly suitable match is found, the server automatically generates a matching proposal and notifies the user.

[0237] Users (investors and landowners) who receive a notification can use their device to view the details of the proposed match. Here, users can view predicted power generation efficiency data and investment simulation results, and accept the proposal if the conditions are met. If a contract is reached, the electronic contract procedure will be carried out through the system.

[0238] As a concrete example, suppose a user who owns unused land in Japan registers that land in the system. At the same time, an investor interested in renewable energy projects registers assets of 5 million yen and a desired return of 5% in the system. The server evaluates the power generation potential of the land based on domestic geographical and weather information, and if it is predicted to be highly efficient, it is added to a list of recommended properties for investors. Investors can check this list via their terminals, and if the conditions are met, a contract is made, the unused land of the landowner is put to effective use, and the investor has the opportunity to obtain a stable return. In this way, the system of the present invention supports the effective use of land resources and the promotion of investment.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The server collects geographic and meteorological information from external databases and public APIs. This includes satellite imagery and time-of-day weather data, and is done by calling the appropriate APIs.

[0242] Step 2:

[0243] The server organizes the collected data by format and integrates it into a unified dataset. Here, data preprocessing is performed, such as integrating map information based on coordinate data and arranging weather data in chronological order.

[0244] Step 3:

[0245] The server runs a machine learning algorithm using pre-processed data. This algorithm evaluates power generation potential from sunlight, temperature, and topographic data, and calculates an efficiency score for each location.

[0246] Step 4:

[0247] The server lists land areas that are predicted to have high power generation efficiency based on the calculated efficiency score. This narrows down the list of high-priority land candidates.

[0248] Step 5:

[0249] Users register detailed information about the land they own and their desired conditions in the system. Investors similarly register details about their budget and desired return on investment.

[0250] Step 6:

[0251] The server calculates a matching score based on registered user information and previously identified land information, and automatically generates matching pairs that are deemed appropriate.

[0252] Step 7:

[0253] The terminal notifies the user of matching proposals received from the server. The user reviews the details and decides whether to accept the proposal based on the conditions.

[0254] Step 8:

[0255] If the user agrees to the proposal, they can proceed with the electronic contract procedure through their device. The system provides a contract signing process using digital signatures, and the contract is concluded.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] There is a need to select optimal land for renewable energy, particularly solar power generation, and to efficiently match that land with prospective investors and landowners. However, methods for accurately evaluating power generation potential using geographical and meteorological information and automatically matching land to individual investment conditions are not yet fully established. This invention solves these problems and provides the identification of land that can be expected to achieve maximum power generation efficiency and an appropriate investment matching process.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes means for using an information processing device to create contract-related provisions for managing contracts, means for integrating geographic and meteorological information to generate an information set, and means for using this information set to evaluate the power generation efficiency of land using a learning algorithm. This makes it possible to identify locations where efficient energy can be generated and to perform optimal investment matching.

[0261] An "information processing device" is a part of a computer system that has the function of managing contract-related regulations and data.

[0262] An "information set" is an integrated dataset containing geographic and meteorological information collected from various sources.

[0263] A "learning algorithm" is a mathematical method for recognizing patterns from data and making predictions; it is an algorithm that provides a specific calculation method.

[0264] "Power generation efficiency" is a numerical indicator used to evaluate the renewable energy production capacity of a particular area of ​​land.

[0265] An "energy-generating location" is a geographical area where renewable energy sources such as solar power are predicted to be able to be efficiently generated.

[0266] "Match" refers to a situation where the conditions and preferences of the user are compatible with those of a specified energy-generating location, thereby enabling the use or investment of that location.

[0267] A "predictive model" is a mathematical or statistical system used to predict future outcomes or characteristics based on input data.

[0268] This invention is a system for selecting optimal land for renewable energy, particularly solar power generation, and matching that land with prospective investors and landowners.

[0269] The server first collects geographic and meteorological information from external sources. This process utilizes common APIs and databases; specific examples include map APIs for geographic information and meteorological data APIs for meteorological information. Once this information is aggregated, the server uses data processing libraries such as Pandas and NumPy to integrate and preprocess the data. This includes imputing missing values ​​and removing noise. As a result, an integrated data set is generated.

[0270] Next, the server runs a learning algorithm using this prepared data. Specifically, it uses machine learning frameworks such as Scikit-learn and TensorFlow to build a model that predicts power generation efficiency based on land location information, sunshine duration, topographic data, temperature, etc. Based on this prediction, highly efficient energy generation locations are scored and listed in descending order of efficiency.

[0271] Meanwhile, users use their devices to receive highly efficient land information suggested by the server based on their registered information. Here, users can check predicted power generation efficiency data and investment simulation results for each plot of land. If the conditions are met, they proceed with the contract procedures through their devices. Electronic contract platforms such as the DocuSign API are utilized to streamline this process.

[0272] As a concrete example, the server collects and analyzes data from 5,000 plots of land, narrowing down the top 100 locations with high power generation efficiency based on the investor's criteria. An example of a prompt that utilizes the generated AI model is, "Find the best unused land in Japan for an investor looking for a renewable energy investment project with a budget of 5 million yen and a desired return of 5%."

[0273] In this way, the system of the present invention realizes the effective utilization of land resources and the promotion of investment.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The server collects geographic and meteorological information. It obtains data from geographic information APIs and meteorological data APIs as input. This provides raw data on regional climate conditions and geographical characteristics. Specifically, the server sends requests to the APIs and receives data including geographic coordinates, average sunshine hours, and annual precipitation. Based on this data, it compiles the basic information necessary for the initial evaluation of regional power generation efficiency.

[0277] Step 2:

[0278] The server converts the collected raw data into a unified format and performs preprocessing. The raw data obtained in step 1 is used as input. Data processing libraries (e.g., Pandas, NumPy) are used to impute missing values ​​with the mean and remove outliers. The output is a unified dataset with data consistency maintained. Specifically, the server generates a data frame, performs statistical analysis to detect outliers, and prepares cleaned data.

[0279] Step 3:

[0280] The server builds a predictive model for power generation efficiency using preprocessed data and performs the analysis. The integrated dataset obtained in step 2 is used as input. A machine learning framework (e.g., Scikit-learn, TensorFlow) is used to train the data-driven predictive model. The output is an analysis result that scores the power generation potential of each plot of land. Specifically, the server applies a random forest method to score the power generation efficiency of each plot of land based on location information and weather data, and lists them in descending order of efficiency.

[0281] Step 4:

[0282] The server matches the user information based on the list of lands with high power generation efficiency to perform optimal matching. As input, it uses the score list of power generation potential generated in Step 3 and the investment condition data registered by the user. It applies a scoring algorithm to generate the combination of the user and land with the highest degree of fit. As output, a proposal for optimal matching is obtained. Specifically, the server compares the expected return and budget of each user with the power generation efficiency of the land to identify the most suitable combination.

[0283] Step 5:

[0284] Using the terminal, the user checks the matching proposal notified by the server and examines the details. As input, it uses the matching proposal obtained in Step 4. As output, the proposal result selected by the user is obtained. Specifically, the user browses the predicted data of power generation efficiency and investment simulation through the interface of the terminal to evaluate whether the conditions are met.

[0285] Step 6:

[0286] If the user agrees to the proposal, the contract process is started through the terminal. As input, it uses the proposal data approved by the user. It utilizes an electronic contract API (e.g., DocuSign) to create and sign the contract. As output, confirmation data of contract establishment is obtained. Specifically, the user checks the contract and signs electronically, and receives a notice that the contract has been formally completed.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] The challenge lies in effectively selecting land suitable for solar power generation as a renewable energy source and facilitating the swift and smooth conclusion of contracts between landowners and investors. Conventional technologies have resulted in a fragmented process, from land evaluation to matching and contract conclusion, involving many manual procedures and being inefficient. This has led to problems such as lost investment opportunities and insufficient promotion of the effective utilization of land resources.

[0290] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes means used to manage an information processing device that creates contract-related provisions, means for integrating geographic and meteorological information to generate a set of information, and means for using this set of information to evaluate the power generation capacity of a location using a predictive algorithm. This enables the identification of the most efficient energy-generating areas, matching users with candidate areas, and rapid contract conclusion through e-commerce.

[0292] An "information processing device" is a device designed for data input, processing, and output, and primarily has the function of creating and managing contract-related provisions.

[0293] "Means used for control" refers to methods or devices for controlling or operating a system or process according to a specific purpose.

[0294] "Geographic information" refers to information that provides physical and spatial data about a specific location or region.

[0295] "Meteorological information" refers to data on atmospheric conditions such as temperature, precipitation, and solar radiation, and is used to create prediction models.

[0296] "Means for generating information sets" refers to a method or apparatus for integrating data from various sources to create a single dataset tailored to a specific purpose.

[0297] A "predictive algorithm" is a computational method that uses collected data to calculate and predict future events and states.

[0298] "Power generation capacity" is an indicator that shows how much electricity a particular location or device can sustainably generate.

[0299] An "energy-generating area" refers to a geographical area that has been identified as being capable of efficiently generating energy using specific energy sources, such as solar power.

[0300] "Match" means that different elements or conditions are mutually compatible, and in this context, it refers to the user and the candidate region meeting each other's conditions.

[0301] "Electronic commerce" refers to the buying, selling, and contracting of goods and services through online networks such as the internet.

[0302] The system implementing this invention is built around an information processing device. The server first acquires geographic and meteorological information by utilizing external databases and APIs. Since this data exists in various formats, the server aggregates the various data and generates a unified set of information. In this process, data preprocessing tools are used as software to fill in missing information and remove unnecessary noise.

[0303] Next, the server uses the compiled information to run a prediction algorithm. This algorithm incorporates location data, sunshine duration, temperature, and topographic information to evaluate the power generation capacity of a specific area. This evaluation is scored numerically and listed in descending order of efficiency. This series of analyses often utilizes common machine learning algorithms.

[0304] The server collates with the conditions registered by the user based on the specified high-efficiency area and generates candidate matches. This procedure is automated by a scoring system that takes into account, for example, the assets of the investor and the required return. When a combination with a high degree of fitness is found, the server automatically notifies the user of the matching proposal.

[0305] The user using the terminal can receive the notification and view the details of the proposed match. By checking the power generation prediction data and the simulation results of the investment, if the conditions are met, a contract can be concluded through the e-commerce transaction procedure. As an example of this process, a prompt sentence such as "Which lands have high power generation potential?" can be utilized, and support can be provided by the generative AI model.

[0306] Since the user can proceed with these procedures stress-free on a smartphone or computer, the investment contract for solar power generation proceeds quickly and efficiently. As a specific example, a scene where investment is considered for unused land in Japan can be considered. With this system, further effective utilization of land resources and promotion of renewable energy investment are expected.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server collects geographical information and meteorological information from an external database or API. The input at this time is the specified API endpoint or the query of the database. As output, raw data in various formats is obtained. The server accumulates this data in preparation for later processing.

[0310] Step 2:

[0311] The server preprocesses the collected data. The input is the raw data collected in step 1. Data cleaning, such as noise reduction and missing value imputation, is performed to obtain a formatted set of information as output. As a result, the data is in a unified format and processed to be suitable for prediction algorithms.

[0312] Step 3:

[0313] The server applies a prediction algorithm using a formatted set of information. The input is prepared data. The server uses a machine learning model to predict and score the power generation capacity for each region. The output is a score list based on the power generation potential of each region. This score list is used to identify regions capable of effective energy generation.

[0314] Step 4:

[0315] The server generates user information and matching candidates based on scored data. Inputs are a score list and registered user criteria. The server automatically scores the best match, taking into account the user's budget and desired return on investment. The output is a list of suggested matches, which is then notified to the user.

[0316] Step 5:

[0317] The user receives notifications using their device and reviews the details of the provided matches. The input is a list of match suggestions from the server. The user reviews the power generation forecast and investment simulation data on the system and selects to proceed to the next step if the conditions are met. The output is the decision of whether or not to proceed with the contract.

[0318] Step 6:

[0319] The user completes the contract procedure through e-commerce via the terminal. The input consists of the user's final decision and contract information. The server executes the contract signing through the electronic contract system, and as a result, a formal contract document is generated. In this process, a "generative AI model" is used, and prompts such as "Which land has high power generation potential?" can be used to assist in verifying the information.

[0320] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0321] This invention is a system that utilizes a computer device to select the optimal land for renewable energy, matches it with users associated with that land, and incorporates an emotion engine to recognize the user's emotions.

[0322] In this system, the server first collects geographic and meteorological information from external databases and public APIs. Because the collected data is in different formats, the server integrates it to generate a dataset suitable for analysis. This dataset is based on factors such as sunshine duration, temperature, and topography, making it possible to evaluate power generation potential.

[0323] Next, the server uses machine learning algorithms to analyze the prepared dataset. Through this analysis, it evaluates the power generation efficiency of each plot of land and identifies the optimal plot based on the scored results. The identified plots of land are then matched with information on landowners and investors registered in the system.

[0324] When a user registers land details and investment conditions using a terminal, the server matches the registered information with identified land information. Furthermore, this invention incorporates an emotion engine, which includes a mechanism for recognizing the user's emotions. The emotion engine analyzes the user's responses and provides data for determining the appropriateness of the proposed content.

[0325] As a concrete example, if a user (investor) wishes to invest with a budget of 5 million yen and inputs their desired return on investment into the system, the server will list land with optimal power generation potential based on collected geographical and weather information. At this point, the emotion engine analyzes the user's facial expressions and tone of voice, and customizes the suggestions to reflect the predicted level of the user's interest and satisfaction. In this way, it is possible to provide more optimized suggestions to the user.

[0326] The user reviews the proposal, accepts it if the conditions are met, and then proceeds to the next step of electronic contract procedures. The terminal displays information about the contract on the screen and completes the process of concluding the contract using a digital signature. In this way, the system based on the present invention realizes the efficient utilization of land resources and the optimization of investment, and provides the user with a highly personalized experience.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The server collects geographical and meteorological information from external databases and public APIs. This includes satellite imagery, sunshine duration, and temperature for each region, and this information is efficiently retrieved using data acquisition scripts.

[0330] Step 2:

[0331] The server integrates the collected data to generate a dataset. This process standardizes data in different formats, unifies geographical coordinates, and organizes it as time-series data. Any missing or outlier data is imputed or corrected at this stage.

[0332] Step 3:

[0333] The server feeds the integrated dataset into a machine learning algorithm for analysis. The algorithm evaluates the power generation potential of each plot of land and performs scoring that comprehensively considers factors such as sunlight, topography, and temperature.

[0334] Step 4:

[0335] The server identifies land predicted to have high power generation efficiency based on the analysis results and creates a ranking from highest to lowest score. This information is stored in a database and made available for subsequent processes.

[0336] Step 5:

[0337] Users enter their land information or investment conditions into a terminal and register them in the system. This sends the location information, area, and details of their desired conditions to the server.

[0338] Step 6:

[0339] The server uses a matching algorithm based on user information and land efficiency data to generate the optimal combination. This takes into account factors such as funding budget, desired return on investment, and land location.

[0340] Step 7:

[0341] The emotion engine analyzes emotional data obtained through user interaction. The device inputs facial expressions and voice tone from the user's camera and microphone, and evaluates the user's response in real time.

[0342] Step 8:

[0343] The server adjusts the suggestions based on the analysis results of the emotion engine. It customizes the suggestions according to the user's level of interest and satisfaction, improving the accuracy of the matching.

[0344] Step 9:

[0345] The device notifies the user of the final matching proposal. The user reviews the proposal, and if the conditions are met, agrees and proceeds to the electronic contract process. The contract is finalized using a digital signature.

[0346] (Example 2)

[0347] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0348] In selecting land for optimal renewable energy utilization, there is a need to effectively integrate geographic and meteorological information to quickly and accurately identify land with high energy generation efficiency. Furthermore, there is a challenge in the process of appropriately matching users with land, as it has not yet been possible to provide individually optimized proposals that take into account the user's feelings.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for using an information processing device that generates contract-related provisions to manage contracts, means for generating an information set by combining geographic information and meteorological information, and means for evaluating the energy generation capacity of land using an information processing algorithm with the information set. This enables the rapid identification of land with high energy generation efficiency and the individual optimization of land that takes into account the feelings of users.

[0351] An "information processing device for generating contract-related provisions" is a dedicated information processing system for managing contract content and generating and editing agreed-upon terms and conditions in a digital format.

[0352] "Geographic information" refers to a dataset that includes location information, topography, land use, and other data about a specific place.

[0353] "Weather information" refers to information about weather conditions such as temperature, precipitation, and wind speed in a specific region, based on past data and forecasts.

[0354] An "information set" is a collection of data that has been integrated and organized from different formats and converted into a format suitable for analysis.

[0355] An "information processing algorithm" is a computational method that defines the procedures and rules for analyzing data.

[0356] "Land's energy generation capacity" refers to the potential of a particular piece of land to efficiently generate renewable energy.

[0357] An "emotion analysis device" is an information processing system that can detect and analyze a user's emotional state.

[0358] "Matching" is the process of finding relationships between different data based on registered information and identifying the optimal combination.

[0359] "Notification" refers to a method or means used to convey specific information or results to a user.

[0360] This system involves multiple steps to select the optimal land for renewable energy and match users with that land. The server collects geographic and meteorological information from external databases and public APIs. Specifically, the software uses geographic information systems (GIS) and meteorological data analysis tools to collect and analyze data related to location and weather conditions.

[0361] Because the collected data is in different formats, the server uses a data integration tool to standardize the format and generate an information set. This information set includes information such as sunshine duration, temperature, and topography, and by applying machine learning algorithms, the energy generation capacity of the land can be evaluated. This utilizes machine learning frameworks such as the Python library Scikit-learn and TensorFlow.

[0362] The server uses the evaluated land information to identify the most suitable land and provides information to the user's terminal based on this. When the user enters investment conditions using the terminal, the server receives the input information in encryption, matches it with the identified land information, and presents the optimal investment options. This process also utilizes an emotion analysis device to detect and analyze the user's emotions, and customizes the suggestions to the user.

[0363] As a concrete example, consider a user, an investor, who wishes to invest with a budget of 5 million yen. In this case, the user inputs their desired rate of return into the system. The server uses data such as sunshine hours and temperature to identify land with high power generation potential and makes investment suggestions based on that. At this time, an emotion analysis device can evaluate the user's response and improve the suggestions.

[0364] An example of a prompt to input into the generating AI model is: "Please propose the optimal renewable energy land investment with a budget of 5 million yen. Also, please evaluate whether the proposal resonates with the user's emotions."

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The server collects geographic and meteorological information using external databases and public APIs. Specifically, it authenticates using an API key and retrieves data for a specified region. Inputs include regional codes and API keys, and outputs include geographic information and meteorological data for each region. Geographic Information Systems (GIS) and meteorological analysis tools are used at this stage.

[0368] Step 2:

[0369] The server integrates the collected geographic and meteorological information. This process uses data integration tools to standardize different data formats and generate a consistent data set. Inputs include geographic and meteorological information in raw data formats, and output is a data set in a format suitable for analysis. Data cleansing is also performed at this stage, including the imputation of missing values ​​and the removal of outliers.

[0370] Step 3:

[0371] The server evaluates the energy generation capacity of land using a data set. This process applies machine learning algorithms to operate a model that predicts power generation efficiency from the dataset. The input is an integrated data set, and the output is a list of power generation efficiency scores for each candidate site. Specifically, the model is trained and evaluated using libraries such as Scikit-learn and TensorFlow.

[0372] Step 4:

[0373] The server identifies the optimal land based on the scored results. This process sorts the land by score, selecting the location with the highest power generation efficiency. The input is a list of scored power generation efficiencies, and the output is information about the optimal land. The server stores this information in a management database.

[0374] Step 5:

[0375] Users input their investment conditions and preferences using a terminal. This information includes budget, desired return on investment, and regional preferences. The terminal encrypts this data and sends it to the server. This allows the server to share additional criteria necessary for selecting the most suitable land.

[0376] Step 6:

[0377] The server matches the user with appropriate land information based on their investment criteria. Inputs include the user's investment criteria and identified land information, and output is an investment proposal optimized for the user. At this stage, an emotion analysis device is also used to analyze the user's emotional state and adjust the proposal accordingly.

[0378] Step 7:

[0379] The user reviews the proposal through their device. Specifically, the device displays detailed information and conditions of the proposed land on the screen for the user to review. If the user accepts the investment, the electronic contract process proceeds, and the contract is concluded through digital signature. At this stage, the contractual provisions are applied, and the transaction is formally completed.

[0380] (Application Example 2)

[0381] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0382] To promote the widespread adoption of renewable energy, the challenge lies in efficiently selecting the optimal land and matching it with users. Furthermore, for the installation of charging and maintenance facilities for autonomous vehicles, it is necessary to quickly and effectively propose the most suitable land. In addition, it is essential to provide higher satisfaction by considering user sentiment and offering individually optimized proposals.

[0383] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0384] In this invention, the server includes means for integrating geographic and meteorological information to generate an information set, means for evaluating the power generation potential of land using machine learning techniques, and means for identifying the user's emotions and customizing suggestions. This enables efficient selection of energy-generating land and optimal suggestions tailored to the user's needs.

[0385] An "information processing device" is a device that receives data, processes it, and generates a specific output.

[0386] An "information set" is a collection of data that integrates different forms of data, such as geographical information and meteorological information.

[0387] "Machine learning techniques" are algorithms and processes used to make predictions and classifications based on data.

[0388] "Power generation potential" is an indicator of a particular piece of land's ability to generate energy.

[0389] "Geographic information" is a general term for information about specific land areas that are capable of generating energy.

[0390] A "user" is an individual or organization that uses this system to select land or perform matching.

[0391] A "candidate site" is land that has been identified by the system as suitable for energy generation.

[0392] "Identifying emotions" means determining a user's emotional state from their voice and facial expressions.

[0393] "Customizing a proposal" means individually adjusting the content of the proposal according to the user's response and needs.

[0394] To implement this invention, a server first functions as an information processing device, collecting geographic and meteorological information from external databases and public APIs. This data is then integrated into an information set. The server processes this information set and uses machine learning techniques to evaluate the power generation potential of the land. Based on the evaluation results, it identifies areas where efficient energy generation is possible.

[0395] Next, the terminal functions as an interface with the user. The user inputs their land selection needs and conditions through the terminal. This data is sent to the server, where the user is matched with identified candidate sites. The server uses emotion recognition software to identify the user's emotions and customizes the suggestions according to the user's individual needs. Specifically, OpenCV models are used for facial expression recognition, and TensorFlow models are used for speech emotion analysis.

[0396] As a concrete example, consider a case where a company wants to select a location to install a charging station for new autonomous vehicles. The company's representative inputs the requirements through an application. The server analyzes the collected data and proposes the optimal installation location. In this process, the system adjusts the proposal based on the representative's facial expressions and tone of voice, ensuring a high level of satisfaction.

[0397] An example of a prompt message would be: "We are looking for land to optimally locate a charging station for our new autonomous vehicles. When selecting a location, please consider power generation potential, accessibility, and future expandability."

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The server collects geographic and meteorological information from external databases and public APIs. Since the collected data exists in various formats, it is integrated into a unified information set. This process standardizes the data format, enabling analysis in the next step. The input consists of geographic and meteorological information, as well as raw data; the output is the integrated information set.

[0401] Step 2:

[0402] The server evaluates the power generation potential of land using machine learning techniques based on the information set. Here, an analysis model is built using libraries such as Scikit-Learn, and the data is used to train the model, thereby performing evaluations for each plot of land. The input is an integrated information set, and the output is numerical data representing the evaluation result.

[0403] Step 3:

[0404] The server identifies candidate sites with efficient energy generation based on the evaluation results. It creates a list of candidate sites based on land information with high scores. The input is the evaluated numerical data, and the output is the list of candidate sites.

[0405] Step 4:

[0406] The terminal receives land selection requirements from the user. The user enters their desired conditions using a smart device. This information is sent to the server and used in the subsequent matching process. The input is the user's requirements data, and the output is the data sent to the server.

[0407] Step 5:

[0408] The server matches the user's requirements with a list of identified candidate locations and performs a matching process to propose the most suitable location. Here, filtering is performed based on the user's conditions to select the optimal proposed location. The input consists of the transmitted data and the candidate location list, and the output is the proposed optimal location information.

[0409] Step 6:

[0410] The terminal presents the user with the proposed optimal location information. The process proceeds only if the user makes a selection. The input is the proposed information from the server, and the output is the information presented to the user.

[0411] Step 7:

[0412] The server uses emotion recognition software to analyze the user's responses and identify the user's emotional state. This analysis is used to customize suggestions and generate more appropriate recommendations. The input is the user's facial expressions and voice data, and the output is emotional state judgment data and customized suggestions.

[0413] Step 8:

[0414] Once the user formally accepts the proposal, the contract requirements are entered from the terminal, and the electronic contract is concluded with a digital signature. This completes the entire process. The input is the user's consent data, and the output is the concluded contract information.

[0415] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0421] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0423] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0425] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0426] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0427] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0428] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0430] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0431] This invention is a system that uses a computer device to effectively select the optimal land for renewable energy, particularly solar power generation, and to match users associated with that identified land.

[0432] In this system, the server first collects geographic and meteorological information from external databases and public APIs. Since the collected information exists in various formats, the server integrates this data into a unified dataset. Here, the data is prepared through preprocessing, such as imputing missing values ​​and removing unwanted noise.

[0433] After data integration and preprocessing are complete, the server runs a machine learning algorithm using the prepared dataset. This algorithm identifies land areas predicted to have high power generation efficiency based on land location, sunshine hours, topographic data, temperature, and other relevant weather data. The analysis results are scored as power generation potential and listed in descending order of efficiency.

[0434] The highly efficient land information identified by the server is cross-referenced with information on landowners and investors registered in the system. In this process, the server scores individual investors based on information such as their budget and desired yield to ensure appropriate matching. When a highly suitable match is found, the server automatically generates a matching proposal and notifies the user.

[0435] Users (investors and landowners) who receive a notification can use their device to view the details of the proposed match. Here, users can view predicted power generation efficiency data and investment simulation results, and accept the proposal if the conditions are met. If a contract is reached, the electronic contract procedure will be carried out through the system.

[0436] As a concrete example, suppose a user who owns unused land in Japan registers that land in the system. At the same time, an investor interested in renewable energy projects registers assets of 5 million yen and a desired return of 5% in the system. The server evaluates the power generation potential of the land based on domestic geographical and weather information, and if it is predicted to be highly efficient, it is added to a list of recommended properties for investors. Investors can check this list via their terminals, and if the conditions are met, a contract is made, the unused land of the landowner is put to effective use, and the investor has the opportunity to obtain a stable return. In this way, the system of the present invention supports the effective use of land resources and the promotion of investment.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The server collects geographic and meteorological information from external databases and public APIs. This includes satellite imagery and time-of-day weather data, and is done by calling the appropriate APIs.

[0440] Step 2:

[0441] The server organizes the collected data by format and integrates it into a unified dataset. Here, data preprocessing is performed, such as integrating map information based on coordinate data and arranging weather data in chronological order.

[0442] Step 3:

[0443] The server runs a machine learning algorithm using pre-processed data. This algorithm evaluates power generation potential from sunlight, temperature, and topographic data, and calculates an efficiency score for each location.

[0444] Step 4:

[0445] The server lists land areas that are predicted to have high power generation efficiency based on the calculated efficiency score. This narrows down the list of high-priority land candidates.

[0446] Step 5:

[0447] Users register detailed information about the land they own and their desired conditions in the system. Investors similarly register details about their budget and desired return on investment.

[0448] Step 6:

[0449] The server calculates a matching score based on registered user information and previously identified land information, and automatically generates matching pairs that are deemed appropriate.

[0450] Step 7:

[0451] The terminal notifies the user of matching proposals received from the server. The user reviews the details and decides whether to accept the proposal based on the conditions.

[0452] Step 8:

[0453] If the user agrees to the proposal, they can proceed with the electronic contract procedure through their device. The system provides a contract signing process using digital signatures, and the contract is concluded.

[0454] (Example 1)

[0455] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0456] There is a need to select optimal land for renewable energy, particularly solar power generation, and to efficiently match that land with prospective investors and landowners. However, methods for accurately evaluating power generation potential using geographical and meteorological information and automatically matching land to individual investment conditions are not yet fully established. This invention solves these problems and provides the identification of land that can be expected to achieve maximum power generation efficiency and an appropriate investment matching process.

[0457] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0458] In this invention, the server includes means for using an information processing device to create contract-related provisions for managing contracts, means for integrating geographic and meteorological information to generate an information set, and means for using this information set to evaluate the power generation efficiency of land using a learning algorithm. This makes it possible to identify locations where efficient energy can be generated and to perform optimal investment matching.

[0459] An "information processing device" is a part of a computer system that has the function of managing contract-related regulations and data.

[0460] An "information set" is an integrated dataset containing geographic and meteorological information collected from various sources.

[0461] A "learning algorithm" is a mathematical method for recognizing patterns from data and making predictions; it is an algorithm that provides a specific calculation method.

[0462] "Power generation efficiency" is a numerical indicator used to evaluate the renewable energy production capacity of a particular area of ​​land.

[0463] An "energy-generating location" is a geographical area where renewable energy sources such as solar power are predicted to be able to be efficiently generated.

[0464] "Match" refers to a situation where the conditions and preferences of the user are compatible with those of a specified energy-generating location, thereby enabling the use or investment of that location.

[0465] A "predictive model" is a mathematical or statistical system used to predict future outcomes or characteristics based on input data.

[0466] This invention is a system for selecting optimal land for renewable energy, particularly solar power generation, and matching that land with prospective investors and landowners.

[0467] The server first collects geographic and meteorological information from external sources. This process utilizes common APIs and databases; specific examples include map APIs for geographic information and meteorological data APIs for meteorological information. Once this information is aggregated, the server uses data processing libraries such as Pandas and NumPy to integrate and preprocess the data. This includes imputing missing values ​​and removing noise. As a result, an integrated data set is generated.

[0468] Next, the server runs a learning algorithm using this prepared data. Specifically, it uses machine learning frameworks such as Scikit-learn and TensorFlow to build a model that predicts power generation efficiency based on land location information, sunshine duration, topographic data, temperature, etc. Based on this prediction, highly efficient energy generation locations are scored and listed in descending order of efficiency.

[0469] Meanwhile, users use their devices to receive highly efficient land information suggested by the server based on their registered information. Here, users can check predicted power generation efficiency data and investment simulation results for each plot of land. If the conditions are met, they proceed with the contract procedures through their devices. Electronic contract platforms such as the DocuSign API are utilized to streamline this process.

[0470] As a concrete example, the server collects and analyzes data from 5,000 plots of land, narrowing down the top 100 locations with high power generation efficiency based on the investor's criteria. An example of a prompt that utilizes the generated AI model is, "Find the best unused land in Japan for an investor looking for a renewable energy investment project with a budget of 5 million yen and a desired return of 5%."

[0471] In this way, the system of the present invention realizes the effective utilization of land resources and the promotion of investment.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The server collects geographic and meteorological information. It obtains data from geographic information APIs and meteorological data APIs as input. This provides raw data on regional climate conditions and geographical characteristics. Specifically, the server sends requests to the APIs and receives data including geographic coordinates, average sunshine hours, and annual precipitation. Based on this data, it compiles the basic information necessary for the initial evaluation of regional power generation efficiency.

[0475] Step 2:

[0476] The server converts the collected raw data into a unified format and performs preprocessing. The raw data obtained in step 1 is used as input. Data processing libraries (e.g., Pandas, NumPy) are used to impute missing values ​​with the mean and remove outliers. The output is a unified dataset with data consistency maintained. Specifically, the server generates a data frame, performs statistical analysis to detect outliers, and prepares cleaned data.

[0477] Step 3:

[0478] The server builds a predictive model for power generation efficiency using preprocessed data and performs the analysis. The integrated dataset obtained in step 2 is used as input. A machine learning framework (e.g., Scikit-learn, TensorFlow) is used to train the data-driven predictive model. The output is an analysis result that scores the power generation potential of each plot of land. Specifically, the server applies a random forest method to score the power generation efficiency of each plot of land based on location information and weather data, and lists them in descending order of efficiency.

[0479] Step 4:

[0480] The server uses a list of highly efficient land plots for power generation, cross-references it with user information, and performs optimal matching. The inputs used are the power generation potential score list generated in step 3 and the investment condition data registered by the user. A scoring algorithm is applied to generate the most suitable user-land combination. The output provides a proposal for the optimal match. Specifically, the server compares each user's desired yield and budget with the land's power generation efficiency to identify the combination that best meets the criteria.

[0481] Step 5:

[0482] Using the terminal, the user reviews the matching proposals notified by the server and examines the details. The matching proposals obtained in step 4 are used as input. The output is the proposal result selected by the user. Specifically, the user views power generation efficiency forecast data and investment simulations through the terminal interface and evaluates whether the conditions are met.

[0483] Step 6:

[0484] If the user agrees to the proposal, the contract process is initiated through the device. The user's accepted proposal data is used as input. An electronic contract API (e.g., DocuSign) is used to create and sign the contract. The output is data confirming the completion of the contract. Specifically, the user reviews the contract, electronically signs it, and receives notification that the contract has been officially completed.

[0485] (Application Example 1)

[0486] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] The challenge lies in effectively selecting land suitable for solar power generation as a renewable energy source and facilitating the swift and smooth conclusion of contracts between landowners and investors. Conventional technologies have resulted in a fragmented process, from land evaluation to matching and contract conclusion, involving many manual procedures and being inefficient. This has led to problems such as lost investment opportunities and insufficient promotion of the effective utilization of land resources.

[0488] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0489] In this invention, the server includes means used to manage an information processing device that creates contract-related provisions, means for integrating geographic and meteorological information to generate a set of information, and means for using this set of information to evaluate the power generation capacity of a location using a predictive algorithm. This enables the identification of the most efficient energy-generating areas, matching users with candidate areas, and rapid contract conclusion through e-commerce.

[0490] An "information processing device" is a device designed for data input, processing, and output, and primarily has the function of creating and managing contract-related provisions.

[0491] "Means used for control" refers to methods or devices for controlling or operating a system or process according to a specific purpose.

[0492] "Geographic information" refers to information that provides physical and spatial data about a specific location or region.

[0493] "Meteorological information" refers to data on atmospheric conditions such as temperature, precipitation, and solar radiation, and is used to create prediction models.

[0494] "Means for generating information sets" refers to a method or apparatus for integrating data from various sources to create a single dataset tailored to a specific purpose.

[0495] A "predictive algorithm" is a computational method that uses collected data to calculate and predict future events and states.

[0496] "Power generation capacity" is an indicator that shows how much electricity a particular location or device can sustainably generate.

[0497] An "energy-generating area" refers to a geographical area that has been identified as being capable of efficiently generating energy using specific energy sources, such as solar power.

[0498] "Match" means that different elements or conditions are mutually compatible, and in this context, it refers to the user and the candidate region meeting each other's conditions.

[0499] "Electronic commerce" refers to the buying, selling, and contracting of goods and services through online networks such as the internet.

[0500] The system implementing this invention is built around an information processing device. The server first acquires geographic and meteorological information by utilizing external databases and APIs. Since this data exists in various formats, the server aggregates the various data and generates a unified set of information. In this process, data preprocessing tools are used as software to fill in missing information and remove unnecessary noise.

[0501] Next, the server uses the compiled information to run a prediction algorithm. This algorithm incorporates location data, sunshine duration, temperature, and topographic information to evaluate the power generation capacity of a specific area. This evaluation is scored numerically and listed in descending order of efficiency. This series of analyses often utilizes common machine learning algorithms.

[0502] The server uses identified high-efficiency areas to match them with user-registered criteria and generate potential matches. This process is automated by a scoring system that considers factors such as the investor's assets and desired returns. If a highly suitable match is found, the server automatically notifies the user of the suggested match.

[0503] Users with a device can receive notifications and view detailed information about suggested matches. They can review power generation forecast data and investment simulation results, and if the conditions are met, they can enter into a contract through e-commerce procedures. As an example of this process, a prompt such as "Which plots of land have high power generation potential?" can be used to provide support from a generative AI model.

[0504] Users can complete these procedures seamlessly on their smartphones or computers, enabling investment contracts for solar power generation to proceed quickly and efficiently. A concrete example would be considering investments in unused land within Japan. This system is expected to promote the more effective use of land resources and accelerate investment in renewable energy.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The server collects geographical and meteorological information from external databases and APIs. The input is a specified API endpoint or database query. The output is raw data in various formats. The server stores this data for later processing.

[0508] Step 2:

[0509] The server preprocesses the collected data. The input is the raw data collected in step 1. Data cleaning, such as noise reduction and missing value imputation, is performed to obtain a formatted set of information as output. As a result, the data is in a unified format and processed to be suitable for prediction algorithms.

[0510] Step 3:

[0511] The server applies a prediction algorithm using a formatted set of information. The input is prepared data. The server uses a machine learning model to predict and score the power generation capacity for each region. The output is a score list based on the power generation potential of each region. This score list is used to identify regions capable of effective energy generation.

[0512] Step 4:

[0513] The server generates user information and matching candidates based on scored data. Inputs are a score list and registered user criteria. The server automatically scores the best match, taking into account the user's budget and desired return on investment. The output is a list of suggested matches, which is then notified to the user.

[0514] Step 5:

[0515] The user receives notifications using their device and reviews the details of the provided matches. The input is a list of match suggestions from the server. The user reviews the power generation forecast and investment simulation data on the system and selects to proceed to the next step if the conditions are met. The output is the decision of whether or not to proceed with the contract.

[0516] Step 6:

[0517] The user completes the contract procedure through e-commerce via the terminal. The input consists of the user's final decision and contract information. The server executes the contract signing through the electronic contract system, and as a result, a formal contract document is generated. In this process, a "generative AI model" is used, and prompts such as "Which land has high power generation potential?" can be used to assist in verifying the information.

[0518] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0519] This invention is a system that utilizes a computer device to select the optimal land for renewable energy, matches it with users associated with that land, and incorporates an emotion engine to recognize the user's emotions.

[0520] In this system, the server first collects geographic and meteorological information from external databases and public APIs. Because the collected data is in different formats, the server integrates it to generate a dataset suitable for analysis. This dataset is based on factors such as sunshine duration, temperature, and topography, making it possible to evaluate power generation potential.

[0521] Next, the server uses machine learning algorithms to analyze the prepared dataset. Through this analysis, it evaluates the power generation efficiency of each plot of land and identifies the optimal plot based on the scored results. The identified plots of land are then matched with information on landowners and investors registered in the system.

[0522] When a user registers land details and investment conditions using a terminal, the server matches the registered information with identified land information. Furthermore, this invention incorporates an emotion engine, which includes a mechanism for recognizing the user's emotions. The emotion engine analyzes the user's responses and provides data for determining the appropriateness of the proposed content.

[0523] As a concrete example, if a user (investor) wishes to invest with a budget of 5 million yen and inputs their desired return on investment into the system, the server will list land with optimal power generation potential based on collected geographical and weather information. At this point, the emotion engine analyzes the user's facial expressions and tone of voice, and customizes the suggestions to reflect the predicted level of the user's interest and satisfaction. In this way, it is possible to provide more optimized suggestions to the user.

[0524] The user reviews the proposal, accepts it if the conditions are met, and then proceeds to the next step of electronic contract procedures. The terminal displays information about the contract on the screen and completes the process of concluding the contract using a digital signature. In this way, the system based on the present invention realizes the efficient utilization of land resources and the optimization of investment, and provides the user with a highly personalized experience.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] The server collects geographical and meteorological information from external databases and public APIs. This includes satellite imagery, sunshine duration, and temperature for each region, and this information is efficiently retrieved using data acquisition scripts.

[0528] Step 2:

[0529] The server integrates the collected data to generate a dataset. This process standardizes data in different formats, unifies geographical coordinates, and organizes it as time-series data. Any missing or outlier data is imputed or corrected at this stage.

[0530] Step 3:

[0531] The server feeds the integrated dataset into a machine learning algorithm for analysis. The algorithm evaluates the power generation potential of each plot of land and performs scoring that comprehensively considers factors such as sunlight, topography, and temperature.

[0532] Step 4:

[0533] The server identifies land predicted to have high power generation efficiency based on the analysis results and creates a ranking from highest to lowest score. This information is stored in a database and made available for subsequent processes.

[0534] Step 5:

[0535] Users enter their land information or investment conditions into a terminal and register them in the system. This sends the location information, area, and details of their desired conditions to the server.

[0536] Step 6:

[0537] The server uses a matching algorithm based on user information and land efficiency data to generate the optimal combination. This takes into account factors such as funding budget, desired return on investment, and land location.

[0538] Step 7:

[0539] The emotion engine analyzes emotional data obtained through user interaction. The device inputs facial expressions and voice tone from the user's camera and microphone, and evaluates the user's response in real time.

[0540] Step 8:

[0541] The server adjusts the suggestions based on the analysis results of the emotion engine. It customizes the suggestions according to the user's level of interest and satisfaction, improving the accuracy of the matching.

[0542] Step 9:

[0543] The device notifies the user of the final matching proposal. The user reviews the proposal, and if the conditions are met, agrees and proceeds to the electronic contract process. The contract is finalized using a digital signature.

[0544] (Example 2)

[0545] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] In selecting land for optimal renewable energy utilization, there is a need to effectively integrate geographic and meteorological information to quickly and accurately identify land with high energy generation efficiency. Furthermore, there is a challenge in the process of appropriately matching users with land, as it has not yet been possible to provide individually optimized proposals that take into account the user's feelings.

[0547] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0548] In this invention, the server includes means for using an information processing device that generates contract-related provisions to manage contracts, means for generating an information set by combining geographic information and meteorological information, and means for evaluating the energy generation capacity of land using an information processing algorithm with the information set. This enables the rapid identification of land with high energy generation efficiency and the individual optimization of land that takes into account the feelings of users.

[0549] An "information processing device for generating contract-related provisions" is a dedicated information processing system for managing contract content and generating and editing agreed-upon terms and conditions in a digital format.

[0550] "Geographic information" refers to a dataset that includes location information, topography, land use, and other data about a specific place.

[0551] "Weather information" refers to information about weather conditions such as temperature, precipitation, and wind speed in a specific region, based on past data and forecasts.

[0552] An "information set" is a collection of data that has been integrated and organized from different formats and converted into a format suitable for analysis.

[0553] An "information processing algorithm" is a computational method that defines the procedures and rules for analyzing data.

[0554] "Land's energy generation capacity" refers to the potential of a particular piece of land to efficiently generate renewable energy.

[0555] An "emotion analysis device" is an information processing system that can detect and analyze a user's emotional state.

[0556] "Matching" is the process of finding relationships between different data based on registered information and identifying the optimal combination.

[0557] "Notification" refers to a method or means used to convey specific information or results to a user.

[0558] This system involves multiple steps to select the optimal land for renewable energy and match users with that land. The server collects geographic and meteorological information from external databases and public APIs. Specifically, the software uses geographic information systems (GIS) and meteorological data analysis tools to collect and analyze data related to location and weather conditions.

[0559] Because the collected data is in different formats, the server uses a data integration tool to standardize the format and generate an information set. This information set includes information such as sunshine duration, temperature, and topography, and by applying machine learning algorithms, the energy generation capacity of the land can be evaluated. This utilizes machine learning frameworks such as the Python library Scikit-learn and TensorFlow.

[0560] The server uses the evaluated land information to identify the most suitable land and provides information to the user's terminal based on this. When the user enters investment conditions using the terminal, the server receives the input information in encryption, matches it with the identified land information, and presents the optimal investment options. This process also utilizes an emotion analysis device to detect and analyze the user's emotions, and customizes the suggestions to the user.

[0561] As a concrete example, consider a user, an investor, who wishes to invest with a budget of 5 million yen. In this case, the user inputs their desired rate of return into the system. The server uses data such as sunshine hours and temperature to identify land with high power generation potential and makes investment suggestions based on that. At this time, an emotion analysis device can evaluate the user's response and improve the suggestions.

[0562] An example of a prompt to input into the generating AI model is: "Please propose the optimal renewable energy land investment with a budget of 5 million yen. Also, please evaluate whether the proposal resonates with the user's emotions."

[0563] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0564] Step 1:

[0565] The server collects geographic and meteorological information using external databases and public APIs. Specifically, it authenticates using an API key and retrieves data for a specified region. Inputs include regional codes and API keys, and outputs include geographic information and meteorological data for each region. Geographic Information Systems (GIS) and meteorological analysis tools are used at this stage.

[0566] Step 2:

[0567] The server integrates the collected geographic and meteorological information. This process uses data integration tools to standardize different data formats and generate a consistent data set. Inputs include geographic and meteorological information in raw data formats, and output is a data set in a format suitable for analysis. Data cleansing is also performed at this stage, including the imputation of missing values ​​and the removal of outliers.

[0568] Step 3:

[0569] The server evaluates the energy generation capacity of land using a data set. This process applies machine learning algorithms to operate a model that predicts power generation efficiency from the dataset. The input is an integrated data set, and the output is a list of power generation efficiency scores for each candidate site. Specifically, the model is trained and evaluated using libraries such as Scikit-learn and TensorFlow.

[0570] Step 4:

[0571] The server identifies the optimal land based on the scored results. This process sorts the land by score, selecting the location with the highest power generation efficiency. The input is a list of scored power generation efficiencies, and the output is information about the optimal land. The server stores this information in a management database.

[0572] Step 5:

[0573] Users input their investment conditions and preferences using a terminal. This information includes budget, desired return on investment, and regional preferences. The terminal encrypts this data and sends it to the server. This allows the server to share additional criteria necessary for selecting the most suitable land.

[0574] Step 6:

[0575] The server matches the user with appropriate land information based on their investment criteria. Inputs include the user's investment criteria and identified land information, and output is an investment proposal optimized for the user. At this stage, an emotion analysis device is also used to analyze the user's emotional state and adjust the proposal accordingly.

[0576] Step 7:

[0577] The user reviews the proposal through their device. Specifically, the device displays detailed information and conditions of the proposed land on the screen for the user to review. If the user accepts the investment, the electronic contract process proceeds, and the contract is concluded through digital signature. At this stage, the contractual provisions are applied, and the transaction is formally completed.

[0578] (Application Example 2)

[0579] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0580] To promote the widespread adoption of renewable energy, the challenge lies in efficiently selecting the optimal land and matching it with users. Furthermore, for the installation of charging and maintenance facilities for autonomous vehicles, it is necessary to quickly and effectively propose the most suitable land. In addition, it is essential to provide higher satisfaction by considering user sentiment and offering individually optimized proposals.

[0581] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0582] In this invention, the server includes means for integrating geographic and meteorological information to generate an information set, means for evaluating the power generation potential of land using machine learning techniques, and means for identifying the user's emotions and customizing suggestions. This enables efficient selection of energy-generating land and optimal suggestions tailored to the user's needs.

[0583] An "information processing device" is a device that receives data, processes it, and generates a specific output.

[0584] An "information set" is a collection of data that integrates different forms of data, such as geographical information and meteorological information.

[0585] "Machine learning techniques" are algorithms and processes used to make predictions and classifications based on data.

[0586] "Power generation potential" is an indicator of a particular piece of land's ability to generate energy.

[0587] "Geographic information" is a general term for information about specific land areas that are capable of generating energy.

[0588] A "user" is an individual or organization that uses this system to select land or perform matching.

[0589] A "candidate site" is land that has been identified by the system as suitable for energy generation.

[0590] "Identifying emotions" means determining a user's emotional state from their voice and facial expressions.

[0591] "Customizing a proposal" means individually adjusting the content of the proposal according to the user's response and needs.

[0592] To implement this invention, a server first functions as an information processing device, collecting geographic and meteorological information from external databases and public APIs. This data is then integrated into an information set. The server processes this information set and uses machine learning techniques to evaluate the power generation potential of the land. Based on the evaluation results, it identifies areas where efficient energy generation is possible.

[0593] Next, the terminal functions as an interface with the user. The user inputs their land selection needs and conditions through the terminal. This data is sent to the server, where the user is matched with identified candidate sites. The server uses emotion recognition software to identify the user's emotions and customizes the suggestions according to the user's individual needs. Specifically, OpenCV models are used for facial expression recognition, and TensorFlow models are used for speech emotion analysis.

[0594] As a concrete example, consider a case where a company wants to select a location to install a charging station for new autonomous vehicles. The company's representative inputs the requirements through an application. The server analyzes the collected data and proposes the optimal installation location. In this process, the system adjusts the proposal based on the representative's facial expressions and tone of voice, ensuring a high level of satisfaction.

[0595] An example of a prompt message would be: "We are looking for land to optimally locate a charging station for our new autonomous vehicles. When selecting a location, please consider power generation potential, accessibility, and future expandability."

[0596] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0597] Step 1:

[0598] The server collects geographic and meteorological information from external databases and public APIs. Since the collected data exists in various formats, it is integrated into a unified information set. This process standardizes the data format, enabling analysis in the next step. The input consists of geographic and meteorological information, as well as raw data; the output is the integrated information set.

[0599] Step 2:

[0600] The server evaluates the power generation potential of land using machine learning techniques based on the information set. Here, an analysis model is built using libraries such as Scikit-Learn, and the data is used to train the model, thereby performing evaluations for each plot of land. The input is an integrated information set, and the output is numerical data representing the evaluation result.

[0601] Step 3:

[0602] The server identifies candidate sites with efficient energy generation based on the evaluation results. It creates a list of candidate sites based on land information with high scores. The input is the evaluated numerical data, and the output is the list of candidate sites.

[0603] Step 4:

[0604] The terminal receives land selection requirements from the user. The user enters their desired conditions using a smart device. This information is sent to the server and used in the subsequent matching process. The input is the user's requirements data, and the output is the data sent to the server.

[0605] Step 5:

[0606] The server matches the user's requirements with a list of identified candidate locations and performs a matching process to propose the most suitable location. Here, filtering is performed based on the user's conditions to select the optimal proposed location. The input consists of the transmitted data and the candidate location list, and the output is the proposed optimal location information.

[0607] Step 6:

[0608] The terminal presents the user with the proposed optimal location information. The process proceeds only if the user makes a selection. The input is the proposed information from the server, and the output is the information presented to the user.

[0609] Step 7:

[0610] The server uses emotion recognition software to analyze the user's responses and identify the user's emotional state. This analysis is used to customize suggestions and generate more appropriate recommendations. The input is the user's facial expressions and voice data, and the output is emotional state judgment data and customized suggestions.

[0611] Step 8:

[0612] Once the user formally accepts the proposal, the contract requirements are entered from the terminal, and the electronic contract is concluded with a digital signature. This completes the entire process. The input is the user's consent data, and the output is the concluded contract information.

[0613] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0614] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0615] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0619] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0620] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0621] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0622] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0623] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0624] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0625] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0626] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0627] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0628] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0629] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] This invention is a system that uses a computer device to effectively select the optimal land for renewable energy, particularly solar power generation, and to match users associated with that identified land.

[0631] In this system, the server first collects geographic and meteorological information from external databases and public APIs. Since the collected information exists in various formats, the server integrates this data into a unified dataset. Here, the data is prepared through preprocessing, such as imputing missing values ​​and removing unwanted noise.

[0632] After data integration and preprocessing are complete, the server runs a machine learning algorithm using the prepared dataset. This algorithm identifies land areas predicted to have high power generation efficiency based on land location, sunshine hours, topographic data, temperature, and other relevant weather data. The analysis results are scored as power generation potential and listed in descending order of efficiency.

[0633] The highly efficient land information identified by the server is cross-referenced with information on landowners and investors registered in the system. In this process, the server scores individual investors based on information such as their budget and desired yield to ensure appropriate matching. When a highly suitable match is found, the server automatically generates a matching proposal and notifies the user.

[0634] Users (investors and landowners) who receive a notification can use their device to view the details of the proposed match. Here, users can view predicted power generation efficiency data and investment simulation results, and accept the proposal if the conditions are met. If a contract is reached, the electronic contract procedure will be carried out through the system.

[0635] As a concrete example, suppose a user who owns unused land in Japan registers that land in the system. At the same time, an investor interested in renewable energy projects registers assets of 5 million yen and a desired return of 5% in the system. The server evaluates the power generation potential of the land based on domestic geographical and weather information, and if it is predicted to be highly efficient, it is added to a list of recommended properties for investors. Investors can check this list via their terminals, and if the conditions are met, a contract is made, the unused land of the landowner is put to effective use, and the investor has the opportunity to obtain a stable return. In this way, the system of the present invention supports the effective use of land resources and the promotion of investment.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The server collects geographic and meteorological information from external databases and public APIs. This includes satellite imagery and time-of-day weather data, and is done by calling the appropriate APIs.

[0639] Step 2:

[0640] The server organizes the collected data by format and integrates it into a unified dataset. Here, data preprocessing is performed, such as integrating map information based on coordinate data and arranging weather data in chronological order.

[0641] Step 3:

[0642] The server runs a machine learning algorithm using pre-processed data. This algorithm evaluates power generation potential from sunlight, temperature, and topographic data, and calculates an efficiency score for each location.

[0643] Step 4:

[0644] The server lists land areas that are predicted to have high power generation efficiency based on the calculated efficiency score. This narrows down the list of high-priority land candidates.

[0645] Step 5:

[0646] Users register detailed information about the land they own and their desired conditions in the system. Investors similarly register details about their budget and desired return on investment.

[0647] Step 6:

[0648] The server calculates a matching score based on registered user information and previously identified land information, and automatically generates matching pairs that are deemed appropriate.

[0649] Step 7:

[0650] The terminal notifies the user of matching proposals received from the server. The user reviews the details and decides whether to accept the proposal based on the conditions.

[0651] Step 8:

[0652] If the user agrees to the proposal, they can proceed with the electronic contract procedure through their device. The system provides a contract signing process using digital signatures, and the contract is concluded.

[0653] (Example 1)

[0654] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] There is a need to select optimal land for renewable energy, particularly solar power generation, and to efficiently match that land with prospective investors and landowners. However, methods for accurately evaluating power generation potential using geographical and meteorological information and automatically matching land to individual investment conditions are not yet fully established. This invention solves these problems and provides the identification of land that can be expected to achieve maximum power generation efficiency and an appropriate investment matching process.

[0656] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0657] In this invention, the server includes means for using an information processing device to create contract-related provisions for managing contracts, means for integrating geographic and meteorological information to generate an information set, and means for using this information set to evaluate the power generation efficiency of land using a learning algorithm. This makes it possible to identify locations where efficient energy can be generated and to perform optimal investment matching.

[0658] An "information processing device" is a part of a computer system that has the function of managing contract-related regulations and data.

[0659] An "information set" is an integrated dataset containing geographic and meteorological information collected from various sources.

[0660] A "learning algorithm" is a mathematical method for recognizing patterns from data and making predictions; it is an algorithm that provides a specific calculation method.

[0661] "Power generation efficiency" is a numerical indicator used to evaluate the renewable energy production capacity of a particular area of ​​land.

[0662] An "energy-generating location" is a geographical area where renewable energy sources such as solar power are predicted to be able to be efficiently generated.

[0663] "Match" refers to a situation where the conditions and preferences of the user are compatible with those of a specified energy-generating location, thereby enabling the use or investment of that location.

[0664] A "predictive model" is a mathematical or statistical system used to predict future outcomes or characteristics based on input data.

[0665] This invention is a system for selecting optimal land for renewable energy, particularly solar power generation, and matching that land with prospective investors and landowners.

[0666] The server first collects geographic and meteorological information from external sources. This process utilizes common APIs and databases; specific examples include map APIs for geographic information and meteorological data APIs for meteorological information. Once this information is aggregated, the server uses data processing libraries such as Pandas and NumPy to integrate and preprocess the data. This includes imputing missing values ​​and removing noise. As a result, an integrated data set is generated.

[0667] Next, the server runs a learning algorithm using this prepared data. Specifically, it uses machine learning frameworks such as Scikit-learn and TensorFlow to build a model that predicts power generation efficiency based on land location information, sunshine duration, topographic data, temperature, etc. Based on this prediction, highly efficient energy generation locations are scored and listed in descending order of efficiency.

[0668] Meanwhile, users use their devices to receive highly efficient land information suggested by the server based on their registered information. Here, users can check predicted power generation efficiency data and investment simulation results for each plot of land. If the conditions are met, they proceed with the contract procedures through their devices. Electronic contract platforms such as the DocuSign API are utilized to streamline this process.

[0669] As a concrete example, the server collects and analyzes data from 5,000 plots of land, narrowing down the top 100 locations with high power generation efficiency based on the investor's criteria. An example of a prompt that utilizes the generated AI model is, "Find the best unused land in Japan for an investor looking for a renewable energy investment project with a budget of 5 million yen and a desired return of 5%."

[0670] In this way, the system of the present invention realizes the effective utilization of land resources and the promotion of investment.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The server collects geographic and meteorological information. It obtains data from geographic information APIs and meteorological data APIs as input. This provides raw data on regional climate conditions and geographical characteristics. Specifically, the server sends requests to the APIs and receives data including geographic coordinates, average sunshine hours, and annual precipitation. Based on this data, it compiles the basic information necessary for the initial evaluation of regional power generation efficiency.

[0674] Step 2:

[0675] The server converts the collected raw data into a unified format and performs preprocessing. The raw data obtained in step 1 is used as input. Data processing libraries (e.g., Pandas, NumPy) are used to impute missing values ​​with the mean and remove outliers. The output is a unified dataset with data consistency maintained. Specifically, the server generates a data frame, performs statistical analysis to detect outliers, and prepares cleaned data.

[0676] Step 3:

[0677] The server builds a predictive model for power generation efficiency using preprocessed data and performs the analysis. The integrated dataset obtained in step 2 is used as input. A machine learning framework (e.g., Scikit-learn, TensorFlow) is used to train the data-driven predictive model. The output is an analysis result that scores the power generation potential of each plot of land. Specifically, the server applies a random forest method to score the power generation efficiency of each plot of land based on location information and weather data, and lists them in descending order of efficiency.

[0678] Step 4:

[0679] The server uses a list of highly efficient land plots for power generation, cross-references it with user information, and performs optimal matching. The inputs used are the power generation potential score list generated in step 3 and the investment condition data registered by the user. A scoring algorithm is applied to generate the most suitable user-land combination. The output provides a proposal for the optimal match. Specifically, the server compares each user's desired yield and budget with the land's power generation efficiency to identify the combination that best meets the criteria.

[0680] Step 5:

[0681] Using the terminal, the user reviews the matching proposals notified by the server and examines the details. The matching proposals obtained in step 4 are used as input. The output is the proposal result selected by the user. Specifically, the user views power generation efficiency forecast data and investment simulations through the terminal interface and evaluates whether the conditions are met.

[0682] Step 6:

[0683] If the user agrees to the proposal, the contract process is initiated through the device. The user's accepted proposal data is used as input. An electronic contract API (e.g., DocuSign) is used to create and sign the contract. The output is data confirming the completion of the contract. Specifically, the user reviews the contract, electronically signs it, and receives notification that the contract has been officially completed.

[0684] (Application Example 1)

[0685] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0686] The challenge lies in effectively selecting land suitable for solar power generation as a renewable energy source and facilitating the swift and smooth conclusion of contracts between landowners and investors. Conventional technologies have resulted in a fragmented process, from land evaluation to matching and contract conclusion, involving many manual procedures and being inefficient. This has led to problems such as lost investment opportunities and insufficient promotion of the effective utilization of land resources.

[0687] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0688] In this invention, the server includes means used to manage an information processing device that creates contract-related provisions, means for integrating geographic and meteorological information to generate a set of information, and means for using this set of information to evaluate the power generation capacity of a location using a predictive algorithm. This enables the identification of the most efficient energy-generating areas, matching users with candidate areas, and rapid contract conclusion through e-commerce.

[0689] An "information processing device" is a device designed for data input, processing, and output, and primarily has the function of creating and managing contract-related provisions.

[0690] "Means used for control" refers to methods or devices for controlling or operating a system or process according to a specific purpose.

[0691] "Geographic information" refers to information that provides physical and spatial data about a specific location or region.

[0692] "Meteorological information" refers to data on atmospheric conditions such as temperature, precipitation, and solar radiation, and is used to create prediction models.

[0693] "Means for generating information sets" refers to a method or apparatus for integrating data from various sources to create a single dataset tailored to a specific purpose.

[0694] A "predictive algorithm" is a computational method that uses collected data to calculate and predict future events and states.

[0695] "Power generation capacity" is an indicator that shows how much electricity a particular location or device can sustainably generate.

[0696] An "energy-generating area" refers to a geographical area that has been identified as being capable of efficiently generating energy using specific energy sources, such as solar power.

[0697] "Match" means that different elements or conditions are mutually compatible, and in this context, it refers to the user and the candidate region meeting each other's conditions.

[0698] "Electronic commerce" refers to the buying, selling, and contracting of goods and services through online networks such as the internet.

[0699] The system implementing this invention is built around an information processing device. The server first acquires geographic and meteorological information by utilizing external databases and APIs. Since this data exists in various formats, the server aggregates the various data and generates a unified set of information. In this process, data preprocessing tools are used as software to fill in missing information and remove unnecessary noise.

[0700] Next, the server uses the compiled information to run a prediction algorithm. This algorithm incorporates location data, sunshine duration, temperature, and topographic information to evaluate the power generation capacity of a specific area. This evaluation is scored numerically and listed in descending order of efficiency. This series of analyses often utilizes common machine learning algorithms.

[0701] The server uses identified high-efficiency areas to match them with user-registered criteria and generate potential matches. This process is automated by a scoring system that considers factors such as the investor's assets and desired returns. If a highly suitable match is found, the server automatically notifies the user of the suggested match.

[0702] Users with a device can receive notifications and view detailed information about suggested matches. They can review power generation forecast data and investment simulation results, and if the conditions are met, they can enter into a contract through e-commerce procedures. As an example of this process, a prompt such as "Which plots of land have high power generation potential?" can be used to provide support from a generative AI model.

[0703] Users can complete these procedures seamlessly on their smartphones or computers, enabling investment contracts for solar power generation to proceed quickly and efficiently. A concrete example would be considering investments in unused land within Japan. This system is expected to promote the more effective use of land resources and accelerate investment in renewable energy.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The server collects geographical and meteorological information from external databases and APIs. The input is a specified API endpoint or database query. The output is raw data in various formats. The server stores this data for later processing.

[0707] Step 2:

[0708] The server preprocesses the collected data. The input is the raw data collected in step 1. Data cleaning, such as noise reduction and missing value imputation, is performed to obtain a formatted set of information as output. As a result, the data is in a unified format and processed to be suitable for prediction algorithms.

[0709] Step 3:

[0710] The server applies a prediction algorithm using a formatted set of information. The input is prepared data. The server uses a machine learning model to predict and score the power generation capacity for each region. The output is a score list based on the power generation potential of each region. This score list is used to identify regions capable of effective energy generation.

[0711] Step 4:

[0712] The server generates user information and matching candidates based on scored data. Inputs are a score list and registered user criteria. The server automatically scores the best match, taking into account the user's budget and desired return on investment. The output is a list of suggested matches, which is then notified to the user.

[0713] Step 5:

[0714] The user receives notifications using their device and reviews the details of the provided matches. The input is a list of match suggestions from the server. The user reviews the power generation forecast and investment simulation data on the system and selects to proceed to the next step if the conditions are met. The output is the decision of whether or not to proceed with the contract.

[0715] Step 6:

[0716] The user completes the contract procedure through e-commerce via the terminal. The input consists of the user's final decision and contract information. The server executes the contract signing through the electronic contract system, and as a result, a formal contract document is generated. In this process, a "generative AI model" is used, and prompts such as "Which land has high power generation potential?" can be used to assist in verifying the information.

[0717] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0718] This invention is a system that utilizes a computer device to select the optimal land for renewable energy, matches it with users associated with that land, and incorporates an emotion engine to recognize the user's emotions.

[0719] In this system, the server first collects geographic and meteorological information from external databases and public APIs. Because the collected data is in different formats, the server integrates it to generate a dataset suitable for analysis. This dataset is based on factors such as sunshine duration, temperature, and topography, making it possible to evaluate power generation potential.

[0720] Next, the server uses machine learning algorithms to analyze the prepared dataset. Through this analysis, it evaluates the power generation efficiency of each plot of land and identifies the optimal plot based on the scored results. The identified plots of land are then matched with information on landowners and investors registered in the system.

[0721] When a user registers land details and investment conditions using a terminal, the server matches the registered information with identified land information. Furthermore, this invention incorporates an emotion engine, which includes a mechanism for recognizing the user's emotions. The emotion engine analyzes the user's responses and provides data for determining the appropriateness of the proposed content.

[0722] As a concrete example, if a user (investor) wishes to invest with a budget of 5 million yen and inputs their desired return on investment into the system, the server will list land with optimal power generation potential based on collected geographical and weather information. At this point, the emotion engine analyzes the user's facial expressions and tone of voice, and customizes the suggestions to reflect the predicted level of the user's interest and satisfaction. In this way, it is possible to provide more optimized suggestions to the user.

[0723] The user reviews the proposal, accepts it if the conditions are met, and then proceeds to the next step of electronic contract procedures. The terminal displays information about the contract on the screen and completes the process of concluding the contract using a digital signature. In this way, the system based on the present invention realizes the efficient utilization of land resources and the optimization of investment, and provides the user with a highly personalized experience.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] The server collects geographical and meteorological information from external databases and public APIs. This includes satellite imagery, sunshine duration, and temperature for each region, and this information is efficiently retrieved using data acquisition scripts.

[0727] Step 2:

[0728] The server integrates the collected data to generate a dataset. This process standardizes data in different formats, unifies geographical coordinates, and organizes it as time-series data. Any missing or outlier data is imputed or corrected at this stage.

[0729] Step 3:

[0730] The server feeds the integrated dataset into a machine learning algorithm for analysis. The algorithm evaluates the power generation potential of each plot of land and performs scoring that comprehensively considers factors such as sunlight, topography, and temperature.

[0731] Step 4:

[0732] The server identifies land predicted to have high power generation efficiency based on the analysis results and creates a ranking from highest to lowest score. This information is stored in a database and made available for subsequent processes.

[0733] Step 5:

[0734] Users enter their land information or investment conditions into a terminal and register them in the system. This sends the location information, area, and details of their desired conditions to the server.

[0735] Step 6:

[0736] The server uses a matching algorithm based on user information and land efficiency data to generate the optimal combination. This takes into account factors such as funding budget, desired return on investment, and land location.

[0737] Step 7:

[0738] The emotion engine analyzes emotional data obtained through user interaction. The device inputs facial expressions and voice tone from the user's camera and microphone, and evaluates the user's response in real time.

[0739] Step 8:

[0740] The server adjusts the suggestions based on the analysis results of the emotion engine. It customizes the suggestions according to the user's level of interest and satisfaction, improving the accuracy of the matching.

[0741] Step 9:

[0742] The device notifies the user of the final matching proposal. The user reviews the proposal, and if the conditions are met, agrees and proceeds to the electronic contract process. The contract is finalized using a digital signature.

[0743] (Example 2)

[0744] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] In selecting land for optimal renewable energy utilization, there is a need to effectively integrate geographic and meteorological information to quickly and accurately identify land with high energy generation efficiency. Furthermore, there is a challenge in the process of appropriately matching users with land, as it has not yet been possible to provide individually optimized proposals that take into account the user's feelings.

[0746] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0747] In this invention, the server includes means for using an information processing device that generates contract-related provisions to manage contracts, means for generating an information set by combining geographic information and meteorological information, and means for evaluating the energy generation capacity of land using an information processing algorithm with the information set. This enables the rapid identification of land with high energy generation efficiency and the individual optimization of land that takes into account the feelings of users.

[0748] An "information processing device for generating contract-related provisions" is a dedicated information processing system for managing contract content and generating and editing agreed-upon terms and conditions in a digital format.

[0749] "Geographic information" refers to a dataset that includes location information, topography, land use, and other data about a specific place.

[0750] "Weather information" refers to information about weather conditions such as temperature, precipitation, and wind speed in a specific region, based on past data and forecasts.

[0751] An "information set" is a collection of data that has been integrated and organized from different formats and converted into a format suitable for analysis.

[0752] An "information processing algorithm" is a computational method that defines the procedures and rules for analyzing data.

[0753] "Land's energy generation capacity" refers to the potential of a particular piece of land to efficiently generate renewable energy.

[0754] An "emotion analysis device" is an information processing system that can detect and analyze a user's emotional state.

[0755] "Matching" is the process of finding relationships between different data based on registered information and identifying the optimal combination.

[0756] "Notification" refers to a method or means used to convey specific information or results to a user.

[0757] This system involves multiple steps to select the optimal land for renewable energy and match users with that land. The server collects geographic and meteorological information from external databases and public APIs. Specifically, the software uses geographic information systems (GIS) and meteorological data analysis tools to collect and analyze data related to location and weather conditions.

[0758] Because the collected data is in different formats, the server uses a data integration tool to standardize the format and generate an information set. This information set includes information such as sunshine duration, temperature, and topography, and by applying machine learning algorithms, the energy generation capacity of the land can be evaluated. This utilizes machine learning frameworks such as the Python library Scikit-learn and TensorFlow.

[0759] The server uses the evaluated land information to identify the most suitable land and provides information to the user's terminal based on this. When the user enters investment conditions using the terminal, the server receives the input information in encryption, matches it with the identified land information, and presents the optimal investment options. This process also utilizes an emotion analysis device to detect and analyze the user's emotions, and customizes the suggestions to the user.

[0760] As a concrete example, consider a user, an investor, who wishes to invest with a budget of 5 million yen. In this case, the user inputs their desired rate of return into the system. The server uses data such as sunshine hours and temperature to identify land with high power generation potential and makes investment suggestions based on that. At this time, an emotion analysis device can evaluate the user's response and improve the suggestions.

[0761] An example of a prompt to input into the generating AI model is: "Please propose the optimal renewable energy land investment with a budget of 5 million yen. Also, please evaluate whether the proposal resonates with the user's emotions."

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] The server collects geographic and meteorological information using external databases and public APIs. Specifically, it authenticates using an API key and retrieves data for a specified region. Inputs include regional codes and API keys, and outputs include geographic information and meteorological data for each region. Geographic Information Systems (GIS) and meteorological analysis tools are used at this stage.

[0765] Step 2:

[0766] The server integrates the collected geographic and meteorological information. This process uses data integration tools to standardize different data formats and generate a consistent data set. Inputs include geographic and meteorological information in raw data formats, and output is a data set in a format suitable for analysis. Data cleansing is also performed at this stage, including the imputation of missing values ​​and the removal of outliers.

[0767] Step 3:

[0768] The server evaluates the energy generation capacity of land using a data set. This process applies machine learning algorithms to operate a model that predicts power generation efficiency from the dataset. The input is an integrated data set, and the output is a list of power generation efficiency scores for each candidate site. Specifically, the model is trained and evaluated using libraries such as Scikit-learn and TensorFlow.

[0769] Step 4:

[0770] The server identifies the optimal land based on the scored results. This process sorts the land by score, selecting the location with the highest power generation efficiency. The input is a list of scored power generation efficiencies, and the output is information about the optimal land. The server stores this information in a management database.

[0771] Step 5:

[0772] Users input their investment conditions and preferences using a terminal. This information includes budget, desired return on investment, and regional preferences. The terminal encrypts this data and sends it to the server. This allows the server to share additional criteria necessary for selecting the most suitable land.

[0773] Step 6:

[0774] The server matches the user with appropriate land information based on their investment criteria. Inputs include the user's investment criteria and identified land information, and output is an investment proposal optimized for the user. At this stage, an emotion analysis device is also used to analyze the user's emotional state and adjust the proposal accordingly.

[0775] Step 7:

[0776] The user reviews the proposal through their device. Specifically, the device displays detailed information and conditions of the proposed land on the screen for the user to review. If the user accepts the investment, the electronic contract process proceeds, and the contract is concluded through digital signature. At this stage, the contractual provisions are applied, and the transaction is formally completed.

[0777] (Application Example 2)

[0778] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0779] To promote the widespread adoption of renewable energy, the challenge lies in efficiently selecting the optimal land and matching it with users. Furthermore, for the installation of charging and maintenance facilities for autonomous vehicles, it is necessary to quickly and effectively propose the most suitable land. In addition, it is essential to provide higher satisfaction by considering user sentiment and offering individually optimized proposals.

[0780] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0781] In this invention, the server includes means for integrating geographic and meteorological information to generate an information set, means for evaluating the power generation potential of land using machine learning techniques, and means for identifying the user's emotions and customizing suggestions. This enables efficient selection of energy-generating land and optimal suggestions tailored to the user's needs.

[0782] An "information processing device" is a device that receives data, processes it, and generates a specific output.

[0783] An "information set" is a collection of data that integrates different forms of data, such as geographical information and meteorological information.

[0784] "Machine learning techniques" are algorithms and processes used to make predictions and classifications based on data.

[0785] "Power generation potential" is an indicator of a particular piece of land's ability to generate energy.

[0786] "Geographic information" is a general term for information about specific land areas that are capable of generating energy.

[0787] A "user" is an individual or organization that uses this system to select land or perform matching.

[0788] A "candidate site" is land that has been identified by the system as suitable for energy generation.

[0789] "Identifying emotions" means determining a user's emotional state from their voice and facial expressions.

[0790] "Customizing a proposal" means individually adjusting the content of the proposal according to the user's response and needs.

[0791] To implement this invention, a server first functions as an information processing device, collecting geographic and meteorological information from external databases and public APIs. This data is then integrated into an information set. The server processes this information set and uses machine learning techniques to evaluate the power generation potential of the land. Based on the evaluation results, it identifies areas where efficient energy generation is possible.

[0792] Next, the terminal functions as an interface with the user. The user inputs their land selection needs and conditions through the terminal. This data is sent to the server, where the user is matched with identified candidate sites. The server uses emotion recognition software to identify the user's emotions and customizes the suggestions according to the user's individual needs. Specifically, OpenCV models are used for facial expression recognition, and TensorFlow models are used for speech emotion analysis.

[0793] As a concrete example, consider a case where a company wants to select a location to install a charging station for new autonomous vehicles. The company's representative inputs the requirements through an application. The server analyzes the collected data and proposes the optimal installation location. In this process, the system adjusts the proposal based on the representative's facial expressions and tone of voice, ensuring a high level of satisfaction.

[0794] An example of a prompt message would be: "We are looking for land to optimally locate a charging station for our new autonomous vehicles. When selecting a location, please consider power generation potential, accessibility, and future expandability."

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The server collects geographic and meteorological information from external databases and public APIs. Since the collected data exists in various formats, it is integrated into a unified information set. This process standardizes the data format, enabling analysis in the next step. The input consists of geographic and meteorological information, as well as raw data; the output is the integrated information set.

[0798] Step 2:

[0799] The server evaluates the power generation potential of land using machine learning techniques based on the information set. Here, an analysis model is built using libraries such as Scikit-Learn, and the data is used to train the model, thereby performing evaluations for each plot of land. The input is an integrated information set, and the output is numerical data representing the evaluation result.

[0800] Step 3:

[0801] The server identifies candidate sites with efficient energy generation based on the evaluation results. It creates a list of candidate sites based on land information with high scores. The input is the evaluated numerical data, and the output is the list of candidate sites.

[0802] Step 4:

[0803] The terminal receives land selection requirements from the user. The user enters their desired conditions using a smart device. This information is sent to the server and used in the subsequent matching process. The input is the user's requirements data, and the output is the data sent to the server.

[0804] Step 5:

[0805] The server matches the user's requirements with a list of identified candidate locations and performs a matching process to propose the most suitable location. Here, filtering is performed based on the user's conditions to select the optimal proposed location. The input consists of the transmitted data and the candidate location list, and the output is the proposed optimal location information.

[0806] Step 6:

[0807] The terminal presents the user with the proposed optimal location information. The process proceeds only if the user makes a selection. The input is the proposed information from the server, and the output is the information presented to the user.

[0808] Step 7:

[0809] The server uses emotion recognition software to analyze the user's responses and identify the user's emotional state. This analysis is used to customize suggestions and generate more appropriate recommendations. The input is the user's facial expressions and voice data, and the output is emotional state judgment data and customized suggestions.

[0810] Step 8:

[0811] Once the user formally accepts the proposal, the contract requirements are entered from the terminal, and the electronic contract is concluded with a digital signature. This completes the entire process. The input is the user's consent data, and the output is the concluded contract information.

[0812] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0813] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0814] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0815] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0816] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0817] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0818] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0819] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0820] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0821] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0822] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0823] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0824] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0825] 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.

[0826] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0827] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0828] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0829] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0830] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0831] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0832] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A computer device used to create contract-related provisions and a means of managing contracts,

[0836] A means of integrating geographic information and meteorological information to generate a dataset,

[0837] A method for evaluating the power generation potential of land using a machine learning algorithm with this dataset,

[0838] A means for identifying the most efficient energy generation location based on the aforementioned evaluation results,

[0839] A means of matching users with candidate locations based on identified location information,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, which uses a predictive model that takes into account sunshine duration, temperature, and topographic data in identifying the candidate locations.

[0843] (Claim 3)

[0844] The system according to claim 1, which, in the matching process, automatically generates matching combinations based on registered user information and notifies the user.

[0845] "Example 1"

[0846] (Claim 1)

[0847] A means of using an information processing device that creates contract-related provisions to manage contracts,

[0848] A means of integrating geographic information and meteorological information to generate an information set,

[0849] A means to evaluate the power generation efficiency of land using a learning algorithm with this information set,

[0850] A means for identifying the most efficient energy generation location based on the aforementioned evaluation results,

[0851] A means of matching users with candidate locations based on identified location information,

[0852] A means for scoring candidate locations and user information to generate the optimal combination,

[0853] Based on the above combination, a means of notifying the user of the proposal,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, which uses a predictive model that takes into account sunshine duration, temperature, topographic data, and related meteorological information in identifying the candidate locations.

[0857] (Claim 3)

[0858] The system according to claim 1, which automatically generates matching combinations based on registered user information and notifies the user of the matching.

[0859] "Application Example 1"

[0860] (Claim 1)

[0861] Means used to manage information processing equipment for creating contract-related provisions,

[0862] A means of integrating geographic information and meteorological information to generate a set of information,

[0863] A means for evaluating the power generation capacity of a location using a predictive algorithm based on this set of information,

[0864] A means for identifying the most efficient energy-generating region based on the aforementioned evaluation results,

[0865] A means of matching users with candidate regions based on identified regional information,

[0866] Means of conducting electronic commerce to conclude a contract,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, which uses a prediction method that takes into account sunshine duration, temperature, and topographic information in identifying the candidate area.

[0870] (Claim 3)

[0871] The system according to claim 1, which, in the aforementioned matching, automatically generates matching combinations based on registered user information, sends notifications, and further completes the contract through electronic commerce procedures.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] A means of using an information processing device that generates contract-related provisions for managing contracts,

[0875] A means of generating an information set by combining geographic information and meteorological information,

[0876] A means for evaluating the energy generation capacity of land using an information processing algorithm with the aforementioned information set,

[0877] A means for identifying the most effective energy generation location based on the aforementioned evaluation results,

[0878] A means for matching users with candidate locations based on identified location information,

[0879] A means equipped with an emotion analysis device that recognizes and evaluates the emotional state of the user,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, which uses a prediction method that takes into account sunshine duration, temperature, and topographic data when identifying candidate locations.

[0883] (Claim 3)

[0884] The system according to claim 1, which, in the aforementioned matching process, automatically generates matching combinations based on registered user information and provides notification.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] A means of using an information processing device that creates contract-related provisions to manage contracts,

[0888] A means of integrating geographic information and meteorological information to generate an information set,

[0889] A means to evaluate the power generation potential of land using machine learning techniques with this information set,

[0890] A means for identifying the most efficient energy-generating site based on the aforementioned evaluation results,

[0891] A means of matching users with candidate sites based on identified location information,

[0892] A means of proposing optimal locations for charging facilities and maintenance equipment to operators of autonomous vehicles,

[0893] A means of identifying the user's emotions and customizing suggestions,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, which uses a predictive model that takes into account sunshine hours, temperature, and topographic data in identifying candidate sites, and also takes into account the charging and maintenance needs of autonomous vehicles.

[0897] (Claim 3)

[0898] The system according to claim 1, which, in the matching process, automatically generates matching combinations based on registered user information, notifies users, and adjusts the content of the suggestions based on the user's emotional response. [Explanation of Symbols]

[0899] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A computer device used to create contract-related provisions and a means to manage the contract, A means of integrating geographic information and meteorological information to generate a dataset, A method for evaluating the power generation potential of land using a machine learning algorithm with this dataset, A means for identifying the most efficient energy generation location based on the aforementioned evaluation results, A means of matching users with candidate locations based on identified location information, A system that includes this.

2. The system according to claim 1, which uses a predictive model that takes into account sunshine duration, temperature, and topographic data in identifying the candidate locations.

3. The system according to claim 1, which, in the matching process, automatically generates matching combinations based on registered user information and notifies the user.

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