System

Abstract

A system is provided.SOLUTION: The system includes a means for acquiring vermin appearance / disappearance information from each local government or organization, a means for acquiring weather change data from a weather information providing service, a means for acquiring mountain village management or development information from a local government, a means for storing the vermin appearance / disappearance information, the weather change data and the development information in a database, a means for cooperatively and mutually analyzing the various kinds of stored data, a means for predicting the appearance / disappearance risk of vermin, a means for generating a countermeasure proposal on the basis of a prediction result, and a means for notifying a related user of the generated countermeasure proposal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Damage caused by pests in agriculture and forestry is on the rise, and effective measures to prevent this damage are needed. Current pest control measures are mainly limited to responding after pests have appeared, and no prompt and effective preventative measures are being taken. Furthermore, the number of personnel in charge of pest control is aging and decreasing, which is causing a decline in the vitality of local areas and raising concerns about the spread of damage. Furthermore, climate change is causing changes in the habitats of pests, and a prompt response is required. Given this background, there is a need for a system that can predict pest appearances and take effective measures in advance. [Means for solving the problem]

[0005] The present invention provides a system including: means for acquiring pest infestation information from various local governments and organizations; means for acquiring climate change data from weather information services; means for acquiring satoyama management and development information from local governments; means for storing the pest infestation information, climate change data, and development information in a database; means for linking and mutually analyzing the various stored data; means for predicting the risk of pest infestation; means for generating countermeasure proposals based on the prediction results; and means for notifying relevant users of the generated countermeasure proposals. This makes it possible to predict pest infestations in advance and take effective countermeasures, thereby preventing pest damage and improving the sustainability of agriculture and forestry.

[0006] "Vermin sighting information" is information provided by local governments and organizations regarding the date, time, location, type, and damage caused by vermin (such as wild boars or deer) in a specific area.

[0007] "Climate change data" refers to statistical information on climate change, such as temperature, rainfall, and air pressure, obtained from the Japan Meteorological Agency and private weather information services.

[0008] "Development information" refers to information provided by local governments and related organizations that is related to Satoyama management and mountain area development plans, such as new logging and large-scale construction projects.

[0009] The "database" is an information management system for storing and managing pest infestation information, climate change data, and development information, and for linking and mutual analysis.

[0010] "Linkage and mutual analysis" is the process of linking pest infestation information, climate change data, and development information, evaluating the relationships between these data, and conducting an integrated analysis.

[0011] A "predictive algorithm" is a calculation method or model that calculates the risk of pest infestation based on collected data and evaluates the possibility of infestation in each area.

[0012] "Countermeasure proposals" are recommendations for specific actions and measures for areas at high risk of pest infestations based on the results of the predictive algorithm.

[0013] "Notification means" refers to the means for communicating the proposed measures to relevant users, and is implemented as email or in-app notifications. [Brief explanation of the drawings]

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

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

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

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

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

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

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0035] This invention is a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on that. This system is composed of a server, terminals, and users.

[0036] Data collection

[0037] First, the server obtains information on pest infestations from each local government or organization. This information includes the date and time of the infestation, location (latitude and longitude), species, and damage details. The server periodically obtains this data using a web API and stores it in a database.

[0038] Next, the server retrieves climate change data from a weather information service. This data includes information about climate changes, such as temperature, precipitation, and air pressure. The server periodically retrieves the data using the weather information API and stores it in a database.

[0039] Additionally, the server obtains Satoyama management and development information from local governments, including new logging plans and large-scale construction projects, using a web API to retrieve this information and store it in a database.

[0040] Data linkage and analysis

[0041] The server connects and cross-analyzes pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information chronologically and evaluates its relevance. The server analyzes how climatic conditions and development plans in a specific area affect the risk of pest infestation.

[0042] The server then uses a predictive algorithm to score each region's risk of pest infestation. For example, if a region experiences both rising temperatures and new logging projects, it predicts that the region's risk of infestation will increase. The server stores these predictions in a database and visualizes them on a dashboard.

[0043] Countermeasure proposals

[0044] The server generates countermeasure proposals based on the prediction results, such as recommending the installation of pest netting and strengthening of on-site inspections in high-risk areas, or prompt implementation of pest control activities in emergencies.

[0045] The generated countermeasure proposals are notified to relevant users (e.g., hunting license holders or local government officials). The server does this using email and in-app notifications.

[0046] User notification and countermeasure implementation

[0047] Users can access the dashboard using their devices (PC or smartphone) to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0048] Based on the provided information, users conduct on-site surveys and implement specific countermeasures such as installing protective nets and capturing the animals. Users also provide feedback to the system on the results of the measures they have implemented and on new on-site information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm.

[0049] Through the above processes, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The server runs a daily script and sends requests to each local government's Web API endpoint. The server obtains pest sighting information (date and time of sighting, location, species, and damage details), converts the obtained data into an appropriate format, and saves it in a database.

[0053] Step 2:

[0054] The server calls the API of a weather service to obtain climate change data such as temperature, precipitation, and air pressure. The server stores this climate data in a database. The weather data often includes daily, weekly, and monthly statistics.

[0055] Step 3:

[0056] The server periodically accesses the local government's Web API endpoint to obtain information on Satoyama management and mountain area development (new logging plans, large-scale construction projects, etc.), and stores this development information in a database.

[0057] Step 4:

[0058] The server extracts pest infestation information, climate change data, and development information from the database, executes queries to combine these data, organizes the combined data in chronological order, and evaluates the relevance of each piece of information.

[0059] Step 5:

[0060] The server applies predictive algorithms to analyze the combined data retrieved from the database. The server scores each region's pest infestation risk and generates a pest infestation forecast. For example, it predicts that areas experiencing a combination of rising temperatures and drought will be at increased risk of pest infestation.

[0061] Step 6:

[0062] Based on the prediction results, the server generates specific countermeasure proposals for high-risk areas, such as "Due to the increasing risk of overhunting in this mountainous area over the next three months, we recommend that the hunting association strengthen patrols."

[0063] Step 7:

[0064] The server stores the generated countermeasure proposals in a database, reflects them on the dashboard, and delivers them to relevant users via email and in-app notifications.

[0065] Step 8:

[0066] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0067] Step 9:

[0068] Based on the information obtained from the dashboard, users can conduct on-site surveys and implement specific measures such as installing necessary protective nets and conducting capture activities.

[0069] Step 10:

[0070] Users provide feedback to the system on the results of the measures they have implemented and new local information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm. This feedback is used to make more accurate predictions in the future.

[0071] Example 1

[0072] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0073] In recent years, damage caused by pests in agriculture and forestry has become increasingly serious. It is particularly important to accurately predict the impact of climate change and development activities on pest behavior and to implement appropriate countermeasures. However, conventional methods have difficulty comprehensively managing and analyzing such complex factors. Therefore, a system that can effectively predict the risk of pest infestation and propose appropriate countermeasures is needed.

[0074] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0075] In this invention, the server includes: means for acquiring pest infestation information from local governments and organizations; means for acquiring climate change data from weather information services; means for acquiring satoyama management and development information from local governments; means for storing the pest infestation information, climate change data, and development information in a database; means for linking and cross-analyzing the various stored data; means for predicting the risk of pest infestation; means for generating countermeasure proposals based on the prediction results; means for notifying related users of the generated countermeasure proposals; means for displaying the prediction information and countermeasure proposals via a user interface; and means for receiving feedback information from related users and using it to improve the accuracy of the prediction algorithm. This enables a comprehensive analysis of the impact of climate change and development activities on pest behavior, enabling accurate risk predictions and appropriate countermeasure proposals.

[0076] "Vermin sighting information" is information reported by local governments and organizations regarding the location and date of appearance of vermin, the extent of damage, etc.

[0077] "Climate change data" refers to data on meteorological elements such as temperature, precipitation, and air pressure, and is obtained from weather information services.

[0078] "Development information" refers to information provided by local governments on satoyama management, new logging plans, large-scale construction projects, and so on.

[0079] A "database" is a system for storing various acquired information and for searching and analyzing it as needed.

[0080] "Linkage and cross-analysis" is the act of integrating multiple data sets and analyzing their interrelationships.

[0081] "Pest infestation risk" is an indicator that shows the possibility of pests appearing in a particular area.

[0082] "Countermeasure proposals" are specific actions or measures recommended to reduce the risk of pest infestation.

[0083] "Users" are relevant individuals or organizations, such as local government officials and hunting license holders, who use this system.

[0084] A "generative AI model" is an artificial intelligence algorithm used in data analysis to generate predictions.

[0085] "Feedback information" refers to the results of measures taken by the user and newly obtained local information, and is fed back to the system.

[0086] This invention provides a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on the results. This system is composed of a server, terminals, and users.

[0087] Data collection

[0088] The server first obtains pest sighting information from each local government or organization. This information includes the date and time of the pest sighting, location (latitude and longitude), type, and details of the damage. The server periodically obtains this data using a web API and stores it in a database. As a specific example, the server obtains data from the API "https: / / example.com / api / animal_sightings" and stores it in the "Sighting_Info" table in the database.

[0089] The server then retrieves climate change data from a weather information service, including temperature, precipitation, and air pressure. The server accesses https: / / weatherapi.com / data and stores the data in a database table called Climate_Data.

[0090] Additionally, the server retrieves Satoyama management and development information from local governments, including new logging plans and large-scale construction projects. The server accesses the API "https: / / municipality.net / dev_info" and stores the data in the "Development_Info" table in the database.

[0091] Data linkage and analysis

[0092] The server performs cross-analysis based on the pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information in chronological order and uses Python libraries such as Pandas and Scikit-learn to evaluate the relevance.

[0093] For example, the server combines data sets to analyze how rising temperatures and new logging plans in a particular region affect the risk of pest infestations. It uses predictive algorithms like random forests and neural networks to score each region's risk. The results are stored in a database and visualized on a dashboard.

[0094] Countermeasure proposals

[0095] The server generates countermeasure proposals based on the prediction results. For example, it may recommend installing pest nets or strengthening on-site inspections in high-risk areas. It may also call for prompt pest control activities in emergencies. The generated countermeasure proposals are notified to relevant users. The server distributes the information via email or in-app notifications.

[0096] User notification and countermeasure implementation

[0097] Users access the dashboard using their devices (PC or smartphone) to check the forecast information and proposed countermeasures. The dashboard displays risk areas in different colors on a map, allowing users to identify high-risk areas at a glance. Users conduct on-site surveys based on the information provided and implement specific countermeasures such as installing protective nets and conducting capture activities. Users also provide feedback to the system on the results of the implemented countermeasures and any new on-site information. The server collects this feedback information and uses it to improve the accuracy of the forecast algorithm.

[0098] Prompt Sentence Examples

[0099] Use the system to predict the risk of pest infestation in a specific area and generate countermeasure proposals. For example, evaluate the risk in an area where the temperature is over 30 degrees, there is little rainfall, and there are plans for new logging, and propose appropriate countermeasures.

[0100] As a result of the above, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Step 1: Data collection

[0103] First, the server obtains information on pest sightings from each local government or organization. This information includes the date and time of sighting, location (latitude and longitude), species, and details of the damage. The input is raw data obtained from the web API, and the output is formatted information stored in the "Sighting_Info" table in the database. Specifically, the server accesses "https: / / example.com / api / animal_sightings," parses the obtained JSON data, and stores it in the database.

[0104] Next, the server retrieves climate change data from the weather information service. The input is weather data retrieved from the API, and the output is stored in the "Climate_Data" table in the database. Specifically, the server accesses "https: / / weatherapi.com / data" and periodically retrieves information such as temperature, precipitation, and air pressure, and stores it in the database.

[0105] Furthermore, the server obtains Satoyama management and development information from local governments. The input is the development information provided, and the output is stored in the "Development_Info" table of the database. Specifically, the server accesses "https: / / municipality.net / dev_info" to obtain information on new logging plans and large-scale construction projects and stores it in the database.

[0106] Step 2: Data integration

[0107] The server performs data integration processing of pest infestation information, climate change data, and development information stored in the database. It uses information from the "Sighting_Info," "Climate_Data," and "Development_Info" tables as input and generates an integrated dataset as output. Specifically, the server uses Python Pandas and SQL queries to organize each dataset in chronological order and combine related data.

[0108] Step 3: Data analysis

[0109] The server performs analysis using the federated dataset. It has the integrated dataset as input and a pest infestation risk score for each region as output. Specifically, the server uses generative AI models to analyze how specific weather conditions or development plans affect pest infestation risk. For example, it uses random forests or neural networks to score the risk for each region.

[0110] Step 4: Generate countermeasure proposals

[0111] The server generates countermeasure proposals based on the analysis results. The input is the pest infestation risk score, and the output is specific countermeasure proposals. Specific operations include the installation of pest nets in high-risk areas and strengthening on-site inspections, and the proposals are saved in a database.

[0112] Step 5: Notify users

[0113] The server notifies the relevant users of the generated countermeasure proposals. The input is each generated countermeasure proposal, and the output is a notification to the user. Specific operations include delivering information via email or in-app notifications.

[0114] Step 6: User confirmation and implementation of measures

[0115] Users access the dashboard using their devices (PC or smartphone) to check the forecast information and proposed countermeasures. Notifications are received from the server as input, and the user implements countermeasures as output. Specifically, risk areas are displayed in different colors on the dashboard, and the user takes action based on the proposed countermeasures.

[0116] Step 7: Gather feedback

[0117] Users provide feedback to the server on the results of the measures they have implemented and any new local information. The input is feedback information from users, and the output is improvements to the accuracy of the prediction algorithm. Specifically, the server receives the feedback information, stores it in a database, and uses it to retrain the prediction algorithm.

[0118] Through these processing steps, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0119] (Application example 1)

[0120] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] Damage to crops and property caused by pest infestations is a serious problem, and effective prevention requires accurate prediction of pest infestation risk and prompt implementation of countermeasures. However, existing systems lack the ability to link and utilize pest infestation information, climate change data, and development information, and are lacking in mechanisms for displaying risks in real time and continuously incorporating user feedback. As a result, countermeasures to prevent pest damage are often delayed. The present invention aims to solve these issues and provide more effective prediction of pest infestation risk and proposal of countermeasures.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0123] In this invention, the server includes means for acquiring pest infestation information from local governments and organizations, means for acquiring climate change data from weather information services, means for acquiring satoyama management and development information from local governments, means for storing the pest infestation information, climate change data, and development information in a database, means for linking and cross-analyzing the various stored data, means for predicting the risk of pest infestation, means for generating countermeasure proposals based on the prediction results, means for notifying relevant users of the generated countermeasure proposals, means for displaying the infestation risk in color-coded on a map for users in real time, and means for reporting the effectiveness of countermeasures implemented by users and new local information. This makes it possible to quickly and accurately predict the risk of pest infestation, enabling rapid response and effective damage prevention measures to be implemented.

[0124] "Means for obtaining information on pest infestations from local governments and organizations" refers to methods and devices for collecting information on pest infestations from local governments and related organizations.

[0125] The "means for acquiring climate change data from a weather information service" refers to a method or device for acquiring data related to climate change, such as temperature, precipitation, and air pressure, from a weather information service.

[0126] "Means for obtaining information on Satoyama management and development from local governments" refers to methods and devices for collecting information on Satoyama management and development plans from local governments.

[0127] "Means for storing the pest infestation information, climate change data, and development information in a database" refers to a method or device for storing the collected pest infestation information, climate change data, and development information in a database that centrally manages the information.

[0128] "Means for linking and mutually analyzing various types of stored data" refers to methods and devices for linking information stored in a database and evaluating and analyzing the mutual relationships.

[0129] A "means for predicting the risk of pest infestation" is a method or device for predicting the risk of pest infestation in a specific area based on collected and analyzed data.

[0130] The "means for generating countermeasure proposals based on prediction results" refers to a method or device for proposing appropriate protective measures or countermeasures based on the prediction results of the risk of pest infestation.

[0131] "Means for notifying relevant users of the generated countermeasure proposals" refers to a method or device for notifying relevant users (e.g., hunting license holders or local government officials) of the generated countermeasure proposals.

[0132] "Means for displaying appearance risks in different colors on a map in real time to the user" refers to a method or device for displaying appearance risks in different colors on a map in real time so that the user can grasp the risks at a glance.

[0133] The "means by which the user can report the effects of measures taken and new local information" refers to a method or device for the user to report the effects of measures taken and newly collected local information to the system.

[0134] This invention is a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts specific pest infestation risks, and proposes countermeasures based on the predictions. This system is composed of a server, terminals, and users.

[0135] Data collection

[0136] The server obtains pest sighting information from each local government or organization. Specifically, it periodically obtains pest sighting information (date and time of sighting, location, type, and damage details) using APIs provided by the local government or organization and stores it in a database.

[0137] The server also uses an API to obtain climate change data (temperature, precipitation, air pressure, etc.) from a weather information service, periodically obtains this data, and stores it in a database.

[0138] In addition, the server obtains Satoyama management and development information (such as new logging plans and construction projects) from local governments and stores this information in a database as well.

[0139] Data linkage and analysis

[0140] The server links and analyzes the information stored in the database, including pest infestation information, climate change data, and development information. Specifically, it organizes this information in chronological order and evaluates its relevance.

[0141] The server then uses a predictive algorithm to score each area's risk of pest infestation. For example, an area experiencing both rising temperatures and new logging projects could be predicted to be at increased risk of pest infestation. The risk scores are stored in a database and visualized on a map.

[0142] Real-time map display

[0143] The application installed on the user's device (smartphone or PC) retrieves the latest risk score from the server and displays risk areas in color on a map in real time, allowing the user to see high-risk areas at a glance.

[0144] Countermeasure proposals and user notifications

[0145] Furthermore, the server generates countermeasure suggestions based on the prediction results and notifies the user via in-app notifications or emails. The countermeasure suggestions include installing protective nets, strengthening on-site inspections, and promptly implementing pest control activities in case of emergency.

[0146] Feedback and improved prediction accuracy

[0147] Users can report the effectiveness of the measures they have implemented and new local information they have acquired within the system. The server receives this feedback information and uses it to improve the accuracy of the prediction algorithm.

[0148] Examples and prompts

[0149] For example, if there are plans for new logging in a certain area of ​​Akita Prefecture and temperatures have recently risen, the system will determine that the area is a high-risk area and notify the local government to "strengthen on-site inspections and install pest nets."

[0150] Example prompt sentence:

[0151] You are given datasets from [ANIMAL_API], [WEATHER_API], and [DEV_API] containing wildlife occurrence, weather changes, and development plans. Use these datasets to create a script to assess and visualize wildlife risk areas. Generate countermeasures if the risk score exceeds a certain threshold.

[0152] The system aims to prevent damage caused by pests and promote the sustainable development of agriculture and forestry.

[0153] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0154] Step 1: Data collection

[0155] The server obtains information on pest sightings from local governments and organizations. The data obtained includes the date and time of sighting, location (latitude and longitude), species, and details of damage. Here, information is collected periodically using an API and stored in a database. Similarly, climate change data (temperature, rainfall, air pressure, etc.) is obtained from a weather information provider via API and stored in the database. In addition, information on satoyama management and development (new logging plans, large-scale construction projects, etc.) is collected from local governments and this is also stored in the database. In this way, pest sighting information, climate change data, and development information are accumulated in the database.

[0156] Step 2: Data integration and analysis

[0157] The server chronologically organizes the pest infestation information, climate change data, and development information stored in the database and evaluates their correlations. To do this, it uses an algorithm that links each data set and analyzes correlations. For example, it analyzes patterns such as an increase in the risk of infestation in an area when a rise in temperature occurs simultaneously with a new logging plan. It uses pest infestation information, climate data, and development information as input data and generates a risk score as output.

[0158] Step 3: Risk prediction

[0159] The server uses a predictive algorithm based on the linked and analyzed data to calculate the risk of pest infestation for each region. A risk score is set for each region and saved in the database. For example, an area where rising temperatures coincide with planned logging will be scored as high risk. The input data is the results of the linkage and analysis, and the output is a risk score.

[0160] Step 4: Visualization

[0161] The device (smartphone or PC) retrieves the latest risk score from the server and displays the risk area on a map in real time, color-coding it. This visualized information helps users understand high-risk areas at a glance. The input data is the risk score, and the output is a color-coded map.

[0162] Step 5: Propose a solution

[0163] The server generates appropriate countermeasure proposals based on the prediction results. For example, it recommends installing protective nets, strengthening on-site inspections, and promptly exterminating pests in emergencies. The generated countermeasure proposals are stored in a database and sent to relevant users (e.g., hunting license holders and local government officials) via in-app notifications or email. The input data is a risk score, and the output is a countermeasure proposal.

[0164] Step 6: Notify users and implement countermeasures

[0165] Users access the dashboard using their devices to check the latest risk information and proposed countermeasures. By viewing risk areas color-coded on a map, they can quickly implement necessary countermeasures, such as conducting on-site surveys or installing protective nets. The input data is countermeasure proposals, and the output is countermeasure implementation.

[0166] Step 7: Gather feedback and improve the algorithm

[0167] Users report the effectiveness of the measures they have implemented and new local information within the application. The server incorporates this feedback information into a database and uses it to improve the accuracy of the prediction algorithm. The input data is user feedback, and the output is an improved prediction algorithm and its results.

[0168] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0169] This invention combines a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on that, with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[0170] Data collection

[0171] First, the server obtains information on pest infestations from each local government or organization. This information includes the date and time of the infestation, location (latitude and longitude), species, and damage details. The server periodically obtains this data using a web API and stores it in a database.

[0172] Next, the server retrieves climate change data from a weather information service. This data includes information about climate changes, such as temperature, precipitation, and air pressure. The server periodically retrieves the data using the weather information API and stores it in a database.

[0173] Additionally, the server obtains Satoyama management and development information from local governments, including new logging plans and large-scale construction projects, using a web API to retrieve this information and store it in a database.

[0174] Data linkage and analysis

[0175] The server connects and cross-analyzes pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information in chronological order and evaluates the correlation between each piece of information. The server analyzes how climatic conditions and development plans in a specific area affect the risk of pest infestation.

[0176] The server then uses a predictive algorithm to score each region's risk of pest infestation. For example, if a region experiences both rising temperatures and new logging projects, it predicts that the region's risk of infestation will increase. The server stores these predictions in a database and visualizes them on a dashboard.

[0177] Incorporating an emotion engine

[0178] The server incorporates an emotion engine that recognizes the user's emotions when generating the countermeasure proposal. The emotion engine can recognize the user's emotions by analyzing the user's reactions and feedback when receiving the countermeasure proposal.

[0179] For example, if the user expresses positive feelings toward the proposed measure, the server will evaluate the suggestion as appropriate and use this as a reference for future similar suggestions. On the other hand, if the user expresses negative feelings, the server will reevaluate the suggestion and consider alternative measures that are more suitable for the user.

[0180] Countermeasure proposals and notifications

[0181] The server generates countermeasure proposals based on the prediction results and the analysis results of the emotion engine. For example, it may recommend the installation of pest netting and strengthening of on-site inspections in high-risk areas. In an emergency, it may also request the prompt implementation of pest control activities.

[0182] The generated action proposals are notified to relevant users (e.g., hunting license holders or local government officials). The server does this using email or in-app notifications. The emotion engine can adjust the notification method and content based on the user's emotions.

[0183] User notification and countermeasure implementation

[0184] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0185] Based on the information provided, users conduct on-site surveys and implement specific countermeasures, such as installing protective nets and capturing the animals. Users also provide feedback to the system on the results of the measures they have implemented and on new on-site information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm and emotion engine.

[0186] Through the above process, this system takes into consideration the user's feelings, prevents damage caused by pests, and contributes to the sustainable development of agriculture and forestry.

[0187] The processing flow will be explained below.

[0188] Step 1:

[0189] The server runs a daily script and sends requests to each local government's Web API endpoint. The server obtains pest sighting information (sighting date and time, location, species, and damage details), converts the format as needed, and saves it in a database.

[0190] Step 2:

[0191] The server calls the API of a weather service to obtain climate change data such as temperature, precipitation, and air pressure. The server stores this climate data in a database. The weather data often includes daily, weekly, and monthly statistics.

[0192] Step 3:

[0193] The server periodically accesses the local government's Web API endpoint to obtain information on Satoyama management and mountain area development (new logging plans, large-scale construction projects, etc.), and stores this development information in a database.

[0194] Step 4:

[0195] The server extracts pest infestation information, climate change data, and development information from the database, executes queries to combine these data, organizes the combined data in chronological order, and evaluates the relevance of each piece of information.

[0196] Step 5:

[0197] The server applies predictive algorithms to analyze the combined data retrieved from the database. The server scores each region's pest infestation risk and generates a pest infestation forecast. For example, it predicts that areas experiencing a combination of rising temperatures and drought will be at increased risk of pest infestation.

[0198] Step 6:

[0199] Based on the prediction results, the server generates specific countermeasure proposals for high-risk areas, such as "Due to the increasing risk of overhunting in this mountainous area over the next three months, we recommend that the hunting association strengthen patrols."

[0200] Step 7:

[0201] Before notifying the user of the proposed measures, the server adds a means to recognize the user's emotions using an emotion engine. The emotion engine analyzes the user's emotions based on the user's past feedback and reaction data.

[0202] Step 8:

[0203] The server adjusts the content of the proposed measures and notification method based on the results of the emotion engine. For example, if the user expressed negative emotions about the previous proposed measures, the server reevaluates the proposed measures and considers a different solution. On the other hand, if the user expressed positive emotions, the server maintains the same proposal.

[0204] Step 9:

[0205] The server stores the adjusted countermeasure suggestions in a database, reflects them on the dashboard, and distributes the countermeasure suggestions to relevant users via email and in-app notifications.

[0206] Step 10:

[0207] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0208] Step 11:

[0209] Based on the information obtained from the dashboard, users can conduct on-site surveys and implement specific measures such as installing necessary protective nets and conducting capture activities.

[0210] Step 12:

[0211] Users provide feedback to the system on the results of the measures they have implemented and any new local information. The server collects this feedback and uses it to improve the accuracy of the prediction algorithm and emotion engine. This feedback is used to make more accurate predictions in the future.

[0212] Example 2

[0213] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0214] Conventional pest infestation prediction systems collect individual data or make predictions from a single data source, but lack the ability to comprehensively analyze a wide variety of influencing factors. Furthermore, they often propose countermeasures without taking the user's emotions into consideration, which can result in low user satisfaction. The present invention aims to realize highly accurate predictions of pest infestation risk and effective countermeasure proposals based on the user's emotions.

[0215] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring animal sighting information from each administrative agency or organization, a means for acquiring climate change data from a weather information providing system, and a means for acquiring environmental management and development information from local administrative agencies. This makes it possible to predict the risk of animal sightings with high accuracy and propose appropriate and effective countermeasures based on the user's emotions.

[0216] "Each government agency or organization" refers to public organizations and related non-profit organizations that provide animal sighting information.

[0217] "Animal sighting information" refers to information including the date and time, location (latitude and longitude), type, and damage details of wild animals that have appeared in a specific area.

[0218] "Weather information providing system" refers to a system that provides climate change data such as temperature, precipitation, and air pressure.

[0219] "Climate change data" refers to information about weather conditions such as temperature, rainfall, and air pressure.

[0220] "Local government agencies" refer to local government bodies that provide information on environmental management and urban development in a particular area.

[0221] "Environmental management information" refers to information about activities that affect the natural environment, such as new logging plans or large-scale construction projects.

[0222] "Development information" refers to information about urban planning and construction projects.

[0223] "Data Storage" means a database or other recording means for storing collected data.

[0224] "Linked / mutual analysis" refers to an analytical method that integrates multiple types of data and evaluates their mutual relevance.

[0225] "Animal sighting risk" refers to a risk index that assesses the likelihood of future wild animal sightings in a particular area.

[0226] "Predictive algorithms" refer to mathematical methods and models used to calculate future risk of animal appearances based on collected data.

[0227] "Countermeasure proposals" refer to recommended measures and methods for preventing animal damage based on the prediction results.

[0228] "Relevant users" refers to users who need the collected data and countermeasure proposals, such as farmers and local government officials.

[0229] "Notification" refers to the means of communication used to inform relevant users of proposed measures, such as email or in-app notification.

[0230] An "emotion engine" refers to technology or software that analyzes a user's emotions and makes suggestions or responses accordingly.

[0231] This invention is a system that predicts the risk of animal sightings with high accuracy by linking and mutually analyzing animal sighting information from various government agencies and organizations, climate change data from weather information systems, and environmental management and development information from local government agencies.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose measures that are optimal for the user.

[0232] Data collection

[0233] The server collects animal sighting information from various government agencies and organizations. This information includes the date and time of the animal's appearance, its location (latitude and longitude), its species, and the details of the damage. Specifically, the server periodically obtains this data using a web API and saves it in data storage. For example, the server obtains information such as "Wild boars destroyed fields in a specific area on October 10, 2023."

[0234] Next, the server obtains climate change data from the weather information system. This data includes information such as temperature, precipitation, and air pressure. The server periodically obtains the data using the Japan Meteorological Agency's API and saves it in data storage. For example, the server collects data such as "The maximum temperature in a specific area on October 10, 2023 will be 30°C and the amount of precipitation will be 10mm."

[0235] In addition, the server obtains environmental management and development information from local government agencies. This information includes new logging plans and large-scale construction projects. The server obtains this information using urban planning APIs and stores it in data storage. For example, the server obtains information such as "Plans for the construction of a new highway in a specific area are underway."

[0236] Data linkage and analysis

[0237] The server integrates and analyzes the collected animal sighting information, climate change data, and environmental management and development information. These three types of information are organized chronologically and their correlations are evaluated. The server analyzes how climatic conditions and development plans in a specific area affect the risk of animal sightings. For example, it evaluates correlations such as "rising temperatures in a specific area and new logging plans increase the risk of wild boar sightings."

[0238] Risk prediction and countermeasure proposals

[0239] The server uses a predictive algorithm to score the risk of animal infestation in each area. For example, areas with rising temperatures and ongoing logging plans are assigned a high risk score. The results are stored in data storage and simultaneously visualized on a dashboard.

[0240] The server then generates countermeasure suggestions based on the prediction results and notifies relevant users, for example, recommending the installation of pest netting or strengthening on-site inspections in high-risk areas. Notifications are sent to relevant users via email or in-app notifications.

[0241] Using the Emotion Engine

[0242] The server uses an emotion engine to recognize the user's emotions. For example, if the user rates a proposed measure as "very satisfied," the server evaluates the measure as appropriate. On the other hand, if the user expresses negative emotions, the server reevaluates the proposal and considers alternative measures.

[0243] User feedback and system accuracy improvement

[0244] Users access the dashboard using their devices to check the latest forecast information and proposed countermeasures. For example, risk areas are color-coded on a map, allowing users to identify high-risk areas at a glance. Based on the information provided, users conduct on-site surveys and implement specific countermeasures, such as installing protective nets and conducting capture activities. After that, users provide feedback on the results of their surveys and any new on-site information to the server. The server collects this feedback information and uses it to improve the accuracy of the forecast algorithm and emotion engine.

[0245] Specific examples and input prompts for the generative AI model

[0246] Example: "What are the risks of animal sightings in a specific area during the second week of October and what are the appropriate countermeasures?"

[0247] This system takes into consideration the user's feelings through the above process, preventing animal damage before it occurs and contributing to the sustainable development of agriculture and forestry.

[0248] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0249] Step 1:

[0250] The server obtains animal sighting information from various government agencies and organizations. This information includes the date and time of the animal's appearance, its location (latitude and longitude), its species, and the details of the damage. The server periodically obtains the data using a web API and saves it in data storage. For example, the server obtains information such as "Field damage caused by wild boars in a specific area on October 10, 2023." The input is the data obtained from the API, and the output is the animal sighting information saved in data storage.

[0251] Step 2:

[0252] The server obtains climate change data from the weather information system. Climate change data includes temperature, precipitation, and air pressure, and this data is also obtained periodically using an API and stored in data storage. For example, information such as "the maximum temperature in a specific area on October 10, 2023 will be 30°C and the amount of precipitation will be 10mm" is collected. The input is weather information obtained from the API, and the output is climate change data stored in data storage.

[0253] Step 3:

[0254] The server obtains environmental management and development information from local government agencies. The information obtained includes new logging plans and large-scale construction projects. The server uses urban planning APIs to periodically obtain and store data. For example, it collects information such as "a new highway construction plan is underway in a specific area." The input is the development information obtained from the API, and the output is the environmental management and development information stored in data storage.

[0255] Step 4:

[0256] The server connects collected animal sighting information, climate change data, and environmental management and development information from data storage and analyzes them mutually. The server organizes each piece of information in chronological order and evaluates their correlation. For example, it analyzes whether "rising temperatures in a specific area and new logging plans increase the risk of wild boar sightings." The input is various types of stored data, and the output is the results of mutual analysis.

[0257] Step 5:

[0258] The server uses a predictive algorithm to score the risk of animal infestation in each area. The input is the cross-analyzed data, and the output is a risk score. The server assigns a high risk score to areas with rising temperatures and planned logging, saves the results in data storage, and visualizes them on a dashboard. For example, a specific area may be assessed with a risk score of 70 / 100.

[0259] Step 6:

[0260] The server uses an emotion engine to recognize the user's emotions. For example, if a user rates a proposed measure as "very satisfied," it is assumed that the proposal is appropriate. The input is the user's feedback information, and the output is the analysis result through the emotion engine.

[0261] Step 7:

[0262] The server generates countermeasure proposals based on the prediction results and the emotion engine's analysis results. For example, it may recommend "installing pest nets" or "strengthening on-site inspections" in high-risk areas. The generated countermeasure proposals are sent to relevant users via email or in-app notifications. The input is the risk score and the emotion engine's analysis results, and the output is specific countermeasure proposals.

[0263] Step 8:

[0264] Users access the dashboard from their devices to check the latest forecast information and proposed countermeasures. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance. For example, specific areas are displayed in red. The input is risk and countermeasure proposal data, and the output is visualized risk information.

[0265] Step 9:

[0266] Users implement the proposed measures, such as installing protective nets and carrying out capture activities. They then provide feedback on the results of their actions and any new on-site information to the server. The server collects this information and uses it to improve the accuracy of the prediction algorithm and emotion engine. The input is feedback information from users, and the output is data used to improve the system's accuracy.

[0267] (Application example 2)

[0268] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0269] In modern times, damage caused by pest infestations has a significant impact on agriculture, forest management, and the lives of residents. Furthermore, as climate change and changes in development status affect the behavioral patterns of pests, risk prediction is becoming increasingly complex. Conventional countermeasure systems that use pest infestation information and weather data lack the ability to link these data for analysis, and do not take user emotions or feedback into account, making it difficult to provide optimal countermeasures tailored to real-world situations.

[0270] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring vermin infestation information from local governments and organizations, means for acquiring climate change data from a weather information service, means for acquiring satoyama management and development information from local governments, means for storing the vermin infestation information, climate change data, and development information in a database, means for linking and mutually analyzing the various stored data, means for predicting the risk of vermin infestation, means for generating countermeasure proposals based on the prediction results, means for notifying relevant users of the generated countermeasure proposals, means for recognizing user emotions and customizing the countermeasure proposals based on feedback, and means for providing users with a smartphone app that displays the vermin infestation risk in a color-coded map. This enables improved accuracy in predicting vermin infestation risk and countermeasure proposals, and individual responses based on user feedback.

[0271] "Municipalities" are local public organizations that provide information on pest infestations, Satoyama management, and development information.

[0272] "Organization" refers to an organization other than a local government that has the role of collecting and providing information on pest infestations.

[0273] "Vermin sighting information" is data relating to the date and time of a vermin sighting, its location, type, and the details of the damage.

[0274] "Climate change data" is information about weather fluctuations, such as temperature, rainfall, and air pressure.

[0275] "Satoyama management information" refers to information related to the management of Satoyama, such as logging plans and conservation plans.

[0276] "Development information" is data about local land use and development plans, including new construction projects.

[0277] A "database" is a system for storing various types of information and for linking and mutual analysis.

[0278] "Collaboration and cross-analysis" is the process of combining multiple pieces of information, evaluating their relevance, and deriving comprehensive insights.

[0279] A "predictive algorithm" is a mathematical model for calculating and predicting the risk of pest infestation.

[0280] "Countermeasure proposals" are proposals that provide specific protective measures and guidelines for action based on the risk of pest infestation.

[0281] "Notification means" is a function that notifies the user of the generated countermeasure proposals via email or in-app notification.

[0282] The "emotion engine" is a system that analyzes user feedback and recognizes emotions.

[0283] "Customization" means individually adjusting the content of suggestions and notification methods based on the user's emotions and feedback.

[0284] The "smartphone app" is an application for mobile devices that provides users with information on the risk of pest infestations and suggestions for countermeasures.

[0285] "Color-coded display" is a display method that uses different colors on a map to visually distinguish risk levels.

[0286] This invention provides a system that links and analyzes pest infestation information, climate change data, and development information to predict the risk of pest infestation and propose countermeasures. This system collects and integrates data from various municipalities and organizations, weather information providers, and local governments, and generates countermeasure proposals by combining a predictive algorithm and an emotion engine.

[0287] Data collection

[0288] The server obtains pest sighting information from local governments and organizations via API and stores it in a database. This pest sighting information includes the date and time of sighting, location (latitude and longitude), species, and details of damage. Next, it obtains climate change data from a weather information provider and stores it in the same database. This includes weather information such as temperature, rainfall, and air pressure. Furthermore, it obtains satoyama management and development information, such as logging plans and construction projects, from local governments and stores it in the database.

[0289] Data linkage and analysis

[0290] The server links and organizes the accumulated pest infestation information, climate change data, and development information in chronological order, and evaluates the correlation between each piece of information. This linking and analysis is performed using a Python program and libraries such as Scikit-learn. The server also uses a predictive algorithm to score the pest infestation risk for each area and stores the results in a database.

[0291] Incorporating an emotion engine

[0292] The server incorporates an emotion engine that recognizes the user's emotions when generating countermeasure proposals. This emotion engine analyzes user feedback using, for example, IBM Watson NLP API to recognize emotions. If the user expresses positive or negative emotions toward a countermeasure proposal, the appropriateness of the proposal is evaluated and reflected in the next proposal generation.

[0293] Countermeasure proposals and notifications

[0294] The server generates countermeasure proposals based on the prediction results and the analysis results of the emotion engine. The generated countermeasure proposals include recommendations for rapid pest control activities in emergencies and the installation of protective nets. These countermeasure proposals are notified to relevant users (e.g., hunting license holders and local government officials) via a smartphone app. Notifications are sent via email or in-app push notifications, and the method and content of the notifications are adjusted based on the user's emotions.

[0295] Feedback and improved prediction accuracy

[0296] Users can check the latest forecast information and countermeasure suggestions through the dashboard of the smartphone app. Feedback information provided by users is sent to the server and used to improve the accuracy of the prediction algorithm and emotion engine. This allows the system to evolve over time and make more appropriate countermeasure suggestions.

[0297] Implementation example

[0298] For example, if a region experiences rising temperatures and new development projects at the same time, the system can predict an increased risk of pest infestations and send notifications to users in the area recommending the installation of protective netting. If users provide positive feedback on the suggestions, the system will consider the suggestions effective and will make similar suggestions in future similar situations.

[0299] Prompt Sentence Examples

[0300] markdown

[0301] Invention Contents

[0302] Develop a new application that combines the emotion engine of a system that links and cross-analyzes pest infestation information, climate change data, and development information to predict the risk of pest infestations. As an example, we are looking for ideas for a security app for smartphones that suggests personalized measures based on the user's emotions, allowing residents to respond appropriately.

[0303] input

[0304] The system collects and analyzes information on pest infestations, climate change data, and development information from local governments, organizations, and weather information providers, and predicts the risk of pest infestations.

[0305] Uses an emotion engine to customize countermeasure suggestions based on user feedback.

[0306] Example output

[0307] 1. A smartphone app that displays the risk of pest infestations in a color-coded manner on a map.

[0308] 2. Notify and display customized countermeasure suggestions based on the user's emotions.

[0309] 3. It has a dashboard function that allows you to understand risk areas at a glance.

[0310] 4. Specific implementation methods include clearly indicating the APIs and libraries to be used (e.g., Google Maps API, Scikit-learn, IBM Watson NLP API).

[0311] It is expected that the system for implementing this invention will effectively prevent damage caused by pests and contribute to the sustainability of agricultural and forest management.

[0312] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0313] System program processing steps

[0314] Step 1:

[0315] Information on pest sightings is obtained from each local government or organization. The server accesses the API of the local government or organization to obtain the date and time of sighting, location (latitude and longitude), type, and details of damage. The obtained data is stored in the server's database. The input is pest sighting information from the API, and the output is the information stored in the database.

[0316] Step 2:

[0317] Climate change data is obtained from a weather information service. The server uses the weather information service's API to obtain weather information such as temperature, precipitation, and air pressure. The obtained data is stored in a database. The input is climate change data from the API, and the output is the information stored in the database.

[0318] Step 3:

[0319] The server obtains Satoyama management and development information from local governments. Using the local government's API, the server obtains information such as new logging plans and construction projects. The obtained data is stored in a database. The input is Satoyama management and development information from the API, and the output is the information stored in the database.

[0320] Step 4:

[0321] The various types of stored data are linked and cross-analyzed. The server uses a Python program to link and cross-analyze the pest infestation information, climate change data, and development information stored in the database. This is the process of organizing data in chronological order and evaluating the relevance between each piece of information. The input is the information stored in the database, and the output is the results of the linkage and cross-analysis.

[0322] Step 5:

[0323] Predict the risk of pest infestation. The server uses the Scikit-learn library to apply a prediction algorithm and score the risk of pest infestation for each area. The input is the results of collaboration and cross-analysis, and the output is the risk score for each area.

[0324] Step 6:

[0325] Generate countermeasure proposals. Based on the prediction results, the server generates specific countermeasure proposals such as pest control activities and the installation of protective nets. The input is the risk score, and the output is the countermeasure proposals.

[0326] Step 7:

[0327] The emotion engine is used to customize countermeasure suggestions. The server uses IBM Watson NLP API to analyze user feedback and recognize the user's emotions. Based on the results, countermeasure suggestions are adjusted and customized. The input is user feedback, and the output is customized countermeasure suggestions.

[0328] Step 8:

[0329] The generated countermeasure proposals are notified to the user. The server notifies the user of the countermeasure proposals via a smartphone app via push notification or email. The input is the customized countermeasure proposals, and the output is a notification to the user.

[0330] Step 9:

[0331] Receives user feedback and uses it to improve the accuracy of the system. Users send feedback on proposed measures via a smartphone app. The server receives this feedback and uses it to improve the accuracy of the prediction algorithm and emotion engine. The input is user feedback, and the output is an improved prediction algorithm and emotion engine.

[0332] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0333] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0334] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0335] [Second embodiment]

[0336] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0337] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0338] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0339] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0340] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0341] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0342] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0343] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0344] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0345] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0346] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0347] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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] This invention is a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on that. This system is composed of a server, terminals, and users.

[0349] Data collection

[0350] First, the server obtains information on pest infestations from each local government or organization. This information includes the date and time of the infestation, location (latitude and longitude), species, and damage details. The server periodically obtains this data using a web API and stores it in a database.

[0351] Next, the server retrieves climate change data from a weather information service. This data includes information about climate changes, such as temperature, precipitation, and air pressure. The server periodically retrieves the data using the weather information API and stores it in a database.

[0352] Additionally, the server obtains Satoyama management and development information from local governments, including new logging plans and large-scale construction projects, using a web API to retrieve this information and store it in a database.

[0353] Data linkage and analysis

[0354] The server connects and cross-analyzes pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information chronologically and evaluates its relevance. The server analyzes how climatic conditions and development plans in a specific area affect the risk of pest infestation.

[0355] The server then uses a predictive algorithm to score each region's risk of pest infestation. For example, if a region experiences both rising temperatures and new logging projects, it predicts that the region's risk of infestation will increase. The server stores these predictions in a database and visualizes them on a dashboard.

[0356] Countermeasure proposals

[0357] The server generates countermeasure proposals based on the prediction results, such as recommending the installation of pest netting and strengthening of on-site inspections in high-risk areas, or prompt implementation of pest control activities in emergencies.

[0358] The generated countermeasure proposals are notified to relevant users (e.g., hunting license holders or local government officials). The server does this using email and in-app notifications.

[0359] User notification and countermeasure implementation

[0360] Users can access the dashboard using their devices (PC or smartphone) to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0361] Based on the provided information, users conduct on-site surveys and implement specific countermeasures such as installing protective nets and capturing the animals. Users also provide feedback to the system on the results of the measures they have implemented and on new on-site information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm.

[0362] Through the above processes, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0363] The processing flow will be explained below.

[0364] Step 1:

[0365] The server runs a daily script and sends requests to each local government's Web API endpoint. The server obtains pest sighting information (date and time of sighting, location, species, and damage details), converts the obtained data into an appropriate format, and saves it in a database.

[0366] Step 2:

[0367] The server calls the API of a weather service to obtain climate change data such as temperature, precipitation, and air pressure. The server stores this climate data in a database. The weather data often includes daily, weekly, and monthly statistics.

[0368] Step 3:

[0369] The server periodically accesses the local government's Web API endpoint to obtain information on Satoyama management and mountain area development (new logging plans, large-scale construction projects, etc.), and stores this development information in a database.

[0370] Step 4:

[0371] The server extracts pest infestation information, climate change data, and development information from the database, executes queries to combine these data, organizes the combined data in chronological order, and evaluates the relevance of each piece of information.

[0372] Step 5:

[0373] The server applies predictive algorithms to analyze the combined data retrieved from the database. The server scores each region's pest infestation risk and generates a pest infestation forecast. For example, it predicts that areas experiencing a combination of rising temperatures and drought will be at increased risk of pest infestation.

[0374] Step 6:

[0375] Based on the prediction results, the server generates specific countermeasure proposals for high-risk areas, such as "Due to the increasing risk of overhunting in this mountainous area over the next three months, we recommend that the hunting association strengthen patrols."

[0376] Step 7:

[0377] The server stores the generated countermeasure proposals in a database, reflects them on the dashboard, and delivers them to relevant users via email and in-app notifications.

[0378] Step 8:

[0379] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0380] Step 9:

[0381] Based on the information obtained from the dashboard, users can conduct on-site surveys and implement specific measures such as installing necessary protective nets and conducting capture activities.

[0382] Step 10:

[0383] Users provide feedback to the system on the results of the measures they have implemented and new local information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm. This feedback is used to make more accurate predictions in the future.

[0384] Example 1

[0385] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0386] In recent years, damage caused by pests in agriculture and forestry has become increasingly serious. It is particularly important to accurately predict the impact of climate change and development activities on pest behavior and to implement appropriate countermeasures. However, conventional methods have difficulty comprehensively managing and analyzing such complex factors. Therefore, a system that can effectively predict the risk of pest infestation and propose appropriate countermeasures is needed.

[0387] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0388] In this invention, the server includes: means for acquiring pest infestation information from local governments and organizations; means for acquiring climate change data from weather information services; means for acquiring satoyama management and development information from local governments; means for storing the pest infestation information, climate change data, and development information in a database; means for linking and cross-analyzing the various stored data; means for predicting the risk of pest infestation; means for generating countermeasure proposals based on the prediction results; means for notifying related users of the generated countermeasure proposals; means for displaying the prediction information and countermeasure proposals via a user interface; and means for receiving feedback information from related users and using it to improve the accuracy of the prediction algorithm. This enables a comprehensive analysis of the impact of climate change and development activities on pest behavior, enabling accurate risk predictions and appropriate countermeasure proposals.

[0389] "Vermin sighting information" is information reported by local governments and organizations regarding the location and date of appearance of vermin, the extent of damage, etc.

[0390] "Climate change data" refers to data on meteorological elements such as temperature, precipitation, and air pressure, and is obtained from weather information services.

[0391] "Development information" refers to information provided by local governments on satoyama management, new logging plans, large-scale construction projects, and so on.

[0392] A "database" is a system for storing various acquired information and for searching and analyzing it as needed.

[0393] "Linkage and cross-analysis" is the act of integrating multiple data sets and analyzing their interrelationships.

[0394] "Pest infestation risk" is an indicator that shows the possibility of pests appearing in a particular area.

[0395] "Countermeasure proposals" are specific actions or measures recommended to reduce the risk of pest infestation.

[0396] "Users" are relevant individuals or organizations, such as local government officials and hunting license holders, who use this system.

[0397] A "generative AI model" is an artificial intelligence algorithm used in data analysis to generate predictions.

[0398] "Feedback information" refers to the results of measures taken by the user and newly obtained local information, and is fed back to the system.

[0399] This invention provides a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on the results. This system is composed of a server, terminals, and users.

[0400] Data collection

[0401] The server first obtains pest sighting information from each local government or organization. This information includes the date and time of the pest sighting, location (latitude and longitude), type, and details of the damage. The server periodically obtains this data using a web API and stores it in a database. As a specific example, the server obtains data from the API "https: / / example.com / api / animal_sightings" and stores it in the "Sighting_Info" table in the database.

[0402] The server then retrieves climate change data from a weather information service, including temperature, precipitation, and air pressure. The server accesses https: / / weatherapi.com / data and stores the data in a database table called Climate_Data.

[0403] Additionally, the server retrieves Satoyama management and development information from local governments, including new logging plans and large-scale construction projects. The server accesses the API "https: / / municipality.net / dev_info" and stores the data in the "Development_Info" table in the database.

[0404] Data linkage and analysis

[0405] The server performs cross-analysis based on the pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information in chronological order and uses Python libraries such as Pandas and Scikit-learn to evaluate the relevance.

[0406] For example, the server combines data sets to analyze how rising temperatures and new logging plans in a particular region affect the risk of pest infestations. It uses predictive algorithms like random forests and neural networks to score each region's risk. The results are stored in a database and visualized on a dashboard.

[0407] Countermeasure proposals

[0408] The server generates countermeasure proposals based on the prediction results. For example, it may recommend installing pest nets or strengthening on-site inspections in high-risk areas. It may also call for prompt pest control activities in emergencies. The generated countermeasure proposals are notified to relevant users. The server distributes the information via email or in-app notifications.

[0409] User notification and countermeasure implementation

[0410] Users access the dashboard using their devices (PC or smartphone) to check the forecast information and proposed countermeasures. The dashboard displays risk areas in different colors on a map, allowing users to identify high-risk areas at a glance. Users conduct on-site surveys based on the information provided and implement specific countermeasures such as installing protective nets and conducting capture activities. Users also provide feedback to the system on the results of the implemented countermeasures and any new on-site information. The server collects this feedback information and uses it to improve the accuracy of the forecast algorithm.

[0411] Prompt Sentence Examples

[0412] Use the system to predict the risk of pest infestation in a specific area and generate countermeasure proposals. For example, evaluate the risk in an area where the temperature is over 30 degrees, there is little rainfall, and there are plans for new logging, and propose appropriate countermeasures.

[0413] As a result of the above, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0414] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0415] Step 1: Data collection

[0416] First, the server obtains information on pest sightings from each local government or organization. This information includes the date and time of sighting, location (latitude and longitude), species, and details of the damage. The input is raw data obtained from the web API, and the output is formatted information stored in the "Sighting_Info" table in the database. Specifically, the server accesses "https: / / example.com / api / animal_sightings," parses the obtained JSON data, and stores it in the database.

[0417] Next, the server retrieves climate change data from the weather information service. The input is weather data retrieved from the API, and the output is stored in the "Climate_Data" table in the database. Specifically, the server accesses "https: / / weatherapi.com / data" and periodically retrieves information such as temperature, precipitation, and air pressure, and stores it in the database.

[0418] Furthermore, the server obtains Satoyama management and development information from local governments. The input is the development information provided, and the output is stored in the "Development_Info" table of the database. Specifically, the server accesses "https: / / municipality.net / dev_info" to obtain information on new logging plans and large-scale construction projects and stores it in the database.

[0419] Step 2: Data integration

[0420] The server performs data integration processing of pest infestation information, climate change data, and development information stored in the database. It uses information from the "Sighting_Info," "Climate_Data," and "Development_Info" tables as input and generates an integrated dataset as output. Specifically, the server uses Python Pandas and SQL queries to organize each dataset in chronological order and combine related data.

[0421] Step 3: Data analysis

[0422] The server performs analysis using the federated dataset. It has the integrated dataset as input and a pest infestation risk score for each region as output. Specifically, the server uses generative AI models to analyze how specific weather conditions or development plans affect pest infestation risk. For example, it uses random forests or neural networks to score the risk for each region.

[0423] Step 4: Generate countermeasure proposals

[0424] The server generates countermeasure proposals based on the analysis results. The input is the pest infestation risk score, and the output is specific countermeasure proposals. Specific operations include the installation of pest nets in high-risk areas and strengthening on-site inspections, and the proposals are saved in a database.

[0425] Step 5: Notify users

[0426] The server notifies the relevant users of the generated countermeasure proposals. The input is each generated countermeasure proposal, and the output is a notification to the user. Specific operations include delivering information via email or in-app notifications.

[0427] Step 6: User confirmation and implementation of measures

[0428] Users access the dashboard using their devices (PC or smartphone) to check the forecast information and proposed countermeasures. Notifications are received from the server as input, and the user implements countermeasures as output. Specifically, risk areas are displayed in different colors on the dashboard, and the user takes action based on the proposed countermeasures.

[0429] Step 7: Gather feedback

[0430] Users provide feedback to the server on the results of the measures they have implemented and any new local information. The input is feedback information from users, and the output is improvements to the accuracy of the prediction algorithm. Specifically, the server receives the feedback information, stores it in a database, and uses it to retrain the prediction algorithm.

[0431] Through these processing steps, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0432] (Application example 1)

[0433] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0434] Damage to crops and property caused by pest infestations is a serious problem, and effective prevention requires accurate prediction of pest infestation risk and prompt implementation of countermeasures. However, existing systems lack the ability to link and utilize pest infestation information, climate change data, and development information, and are lacking in mechanisms for displaying risks in real time and continuously incorporating user feedback. As a result, countermeasures to prevent pest damage are often delayed. The present invention aims to solve these issues and provide more effective prediction of pest infestation risk and proposal of countermeasures.

[0435] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0436] In this invention, the server includes means for acquiring pest infestation information from local governments and organizations, means for acquiring climate change data from weather information services, means for acquiring satoyama management and development information from local governments, means for storing the pest infestation information, climate change data, and development information in a database, means for linking and cross-analyzing the various stored data, means for predicting the risk of pest infestation, means for generating countermeasure proposals based on the prediction results, means for notifying relevant users of the generated countermeasure proposals, means for displaying the infestation risk in color-coded on a map for users in real time, and means for reporting the effectiveness of countermeasures implemented by users and new local information. This makes it possible to quickly and accurately predict the risk of pest infestation, enabling rapid response and effective damage prevention measures to be implemented.

[0437] "Means for obtaining information on pest infestations from local governments and organizations" refers to methods and devices for collecting information on pest infestations from local governments and related organizations.

[0438] The "means for acquiring climate change data from a weather information service" refers to a method or device for acquiring data related to climate change, such as temperature, precipitation, and air pressure, from a weather information service.

[0439] "Means for obtaining information on Satoyama management and development from local governments" refers to methods and devices for collecting information on Satoyama management and development plans from local governments.

[0440] "Means for storing the pest infestation information, climate change data, and development information in a database" refers to a method or device for storing the collected pest infestation information, climate change data, and development information in a database that centrally manages the information.

[0441] "Means for linking and mutually analyzing various types of stored data" refers to methods and devices for linking information stored in a database and evaluating and analyzing the mutual relationships.

[0442] A "means for predicting the risk of pest infestation" is a method or device for predicting the risk of pest infestation in a specific area based on collected and analyzed data.

[0443] The "means for generating countermeasure proposals based on prediction results" refers to a method or device for proposing appropriate protective measures or countermeasures based on the prediction results of the risk of pest infestation.

[0444] "Means for notifying relevant users of the generated countermeasure proposals" refers to a method or device for notifying relevant users (e.g., hunting license holders or local government officials) of the generated countermeasure proposals.

[0445] "Means for displaying appearance risks in different colors on a map in real time to the user" refers to a method or device for displaying appearance risks in different colors on a map in real time so that the user can grasp the risks at a glance.

[0446] The "means by which the user can report the effects of measures taken and new local information" refers to a method or device for the user to report the effects of measures taken and newly collected local information to the system.

[0447] This invention is a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts specific pest infestation risks, and proposes countermeasures based on the predictions. This system is composed of a server, terminals, and users.

[0448] Data collection

[0449] The server obtains pest sighting information from each local government or organization. Specifically, it periodically obtains pest sighting information (date and time of sighting, location, type, and damage details) using APIs provided by the local government or organization and stores it in a database.

[0450] The server also uses an API to obtain climate change data (temperature, precipitation, air pressure, etc.) from a weather information service, periodically obtains this data, and stores it in a database.

[0451] In addition, the server obtains Satoyama management and development information (such as new logging plans and construction projects) from local governments and stores this information in a database as well.

[0452] Data linkage and analysis

[0453] The server links and analyzes the information stored in the database, including pest infestation information, climate change data, and development information. Specifically, it organizes this information in chronological order and evaluates its relevance.

[0454] The server then uses a predictive algorithm to score each area's risk of pest infestation. For example, an area experiencing both rising temperatures and new logging projects could be predicted to be at increased risk of pest infestation. The risk scores are stored in a database and visualized on a map.

[0455] Real-time map display

[0456] The application installed on the user's device (smartphone or PC) retrieves the latest risk score from the server and displays risk areas in color on a map in real time, allowing the user to see high-risk areas at a glance.

[0457] Countermeasure proposals and user notifications

[0458] Furthermore, the server generates countermeasure suggestions based on the prediction results and notifies the user via in-app notifications or emails. The countermeasure suggestions include installing protective nets, strengthening on-site inspections, and promptly implementing pest control activities in case of emergency.

[0459] Feedback and improved prediction accuracy

[0460] Users can report the effectiveness of the measures they have implemented and new local information they have acquired within the system. The server receives this feedback information and uses it to improve the accuracy of the prediction algorithm.

[0461] Examples and prompts

[0462] For example, if there are plans for new logging in a certain area of ​​Akita Prefecture and temperatures have recently risen, the system will determine that the area is a high-risk area and notify the local government to "strengthen on-site inspections and install pest nets."

[0463] Example prompt sentence:

[0464] You are given datasets from [ANIMAL_API], [WEATHER_API], and [DEV_API] containing wildlife occurrence, weather changes, and development plans. Use these datasets to create a script to assess and visualize wildlife risk areas. Generate countermeasures if the risk score exceeds a certain threshold.

[0465] The system aims to prevent damage caused by pests and promote the sustainable development of agriculture and forestry.

[0466] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0467] Step 1: Data collection

[0468] The server obtains information on pest sightings from local governments and organizations. The data obtained includes the date and time of sighting, location (latitude and longitude), species, and details of damage. Here, information is collected periodically using an API and stored in a database. Similarly, climate change data (temperature, rainfall, air pressure, etc.) is obtained from a weather information provider via API and stored in the database. In addition, information on satoyama management and development (new logging plans, large-scale construction projects, etc.) is collected from local governments and this is also stored in the database. In this way, pest sighting information, climate change data, and development information are accumulated in the database.

[0469] Step 2: Data integration and analysis

[0470] The server chronologically organizes the pest infestation information, climate change data, and development information stored in the database and evaluates their correlations. To do this, it uses an algorithm that links each data set and analyzes correlations. For example, it analyzes patterns such as an increase in the risk of infestation in an area when a rise in temperature occurs simultaneously with a new logging plan. It uses pest infestation information, climate data, and development information as input data and generates a risk score as output.

[0471] Step 3: Risk prediction

[0472] The server uses a predictive algorithm based on the linked and analyzed data to calculate the risk of pest infestation for each region. A risk score is set for each region and saved in the database. For example, an area where rising temperatures coincide with planned logging will be scored as high risk. The input data is the results of the linkage and analysis, and the output is a risk score.

[0473] Step 4: Visualization

[0474] The device (smartphone or PC) retrieves the latest risk score from the server and displays the risk area on a map in real time, color-coding it. This visualized information helps users understand high-risk areas at a glance. The input data is the risk score, and the output is a color-coded map.

[0475] Step 5: Propose a solution

[0476] The server generates appropriate countermeasure proposals based on the prediction results. For example, it recommends installing protective nets, strengthening on-site inspections, and promptly exterminating pests in emergencies. The generated countermeasure proposals are stored in a database and sent to relevant users (e.g., hunting license holders and local government officials) via in-app notifications or email. The input data is a risk score, and the output is a countermeasure proposal.

[0477] Step 6: Notify users and implement countermeasures

[0478] Users access the dashboard using their devices to check the latest risk information and proposed countermeasures. By viewing risk areas color-coded on a map, they can quickly implement necessary countermeasures, such as conducting on-site surveys or installing protective nets. The input data is countermeasure proposals, and the output is countermeasure implementation.

[0479] Step 7: Gather feedback and improve the algorithm

[0480] Users report the effectiveness of the measures they have implemented and new local information within the application. The server incorporates this feedback information into a database and uses it to improve the accuracy of the prediction algorithm. The input data is user feedback, and the output is an improved prediction algorithm and its results.

[0481] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0482] This invention combines a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on that, with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[0483] Data collection

[0484] First, the server obtains information on pest infestations from each local government or organization. This information includes the date and time of the infestation, location (latitude and longitude), species, and damage details. The server periodically obtains this data using a web API and stores it in a database.

[0485] Next, the server retrieves climate change data from a weather information service. This data includes information about climate changes, such as temperature, precipitation, and air pressure. The server periodically retrieves the data using the weather information API and stores it in a database.

[0486] Additionally, the server obtains Satoyama management and development information from local governments, including new logging plans and large-scale construction projects, using a web API to retrieve this information and store it in a database.

[0487] Data linkage and analysis

[0488] The server connects and cross-analyzes pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information in chronological order and evaluates the correlation between each piece of information. The server analyzes how climatic conditions and development plans in a specific area affect the risk of pest infestation.

[0489] The server then uses a predictive algorithm to score each region's risk of pest infestation. For example, if a region experiences both rising temperatures and new logging projects, it predicts that the region's risk of infestation will increase. The server stores these predictions in a database and visualizes them on a dashboard.

[0490] Incorporating an emotion engine

[0491] The server incorporates an emotion engine that recognizes the user's emotions when generating the countermeasure proposal. The emotion engine can recognize the user's emotions by analyzing the user's reactions and feedback when receiving the countermeasure proposal.

[0492] For example, if the user expresses positive feelings toward the proposed measure, the server will evaluate the suggestion as appropriate and use this as a reference for future similar suggestions. On the other hand, if the user expresses negative feelings, the server will reevaluate the suggestion and consider alternative measures that are more suitable for the user.

[0493] Countermeasure proposals and notifications

[0494] The server generates countermeasure proposals based on the prediction results and the analysis results of the emotion engine. For example, it may recommend the installation of pest netting and strengthening of on-site inspections in high-risk areas. In an emergency, it may also request the prompt implementation of pest control activities.

[0495] The generated action proposals are notified to relevant users (e.g., hunting license holders or local government officials). The server does this using email or in-app notifications. The emotion engine can adjust the notification method and content based on the user's emotions.

[0496] User notification and countermeasure implementation

[0497] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0498] Based on the information provided, users conduct on-site surveys and implement specific countermeasures, such as installing protective nets and capturing the animals. Users also provide feedback to the system on the results of the measures they have implemented and on new on-site information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm and emotion engine.

[0499] Through the above process, this system takes into consideration the user's feelings, prevents damage caused by pests, and contributes to the sustainable development of agriculture and forestry.

[0500] The processing flow will be explained below.

[0501] Step 1:

[0502] The server runs a daily script and sends requests to each local government's Web API endpoint. The server obtains pest sighting information (sighting date and time, location, species, and damage details), converts the format as needed, and saves it in a database.

[0503] Step 2:

[0504] The server calls the API of a weather service to obtain climate change data such as temperature, precipitation, and air pressure. The server stores this climate data in a database. The weather data often includes daily, weekly, and monthly statistics.

[0505] Step 3:

[0506] The server periodically accesses the local government's Web API endpoint to obtain information on Satoyama management and mountain area development (new logging plans, large-scale construction projects, etc.), and stores this development information in a database.

[0507] Step 4:

[0508] The server extracts pest infestation information, climate change data, and development information from the database, executes queries to combine these data, organizes the combined data in chronological order, and evaluates the relevance of each piece of information.

[0509] Step 5:

[0510] The server applies predictive algorithms to analyze the combined data retrieved from the database. The server scores each region's pest infestation risk and generates a pest infestation forecast. For example, it predicts that areas experiencing a combination of rising temperatures and drought will be at increased risk of pest infestation.

[0511] Step 6:

[0512] Based on the prediction results, the server generates specific countermeasure proposals for high-risk areas, such as "Due to the increasing risk of overhunting in this mountainous area over the next three months, we recommend that the hunting association strengthen patrols."

[0513] Step 7:

[0514] Before notifying the user of the proposed measures, the server adds a means to recognize the user's emotions using an emotion engine. The emotion engine analyzes the user's emotions based on the user's past feedback and reaction data.

[0515] Step 8:

[0516] The server adjusts the content of the proposed measures and notification method based on the results of the emotion engine. For example, if the user expressed negative emotions about the previous proposed measures, the server reevaluates the proposed measures and considers a different solution. On the other hand, if the user expressed positive emotions, the server maintains the same proposal.

[0517] Step 9:

[0518] The server stores the adjusted countermeasure suggestions in a database, reflects them on the dashboard, and distributes the countermeasure suggestions to relevant users via email and in-app notifications.

[0519] Step 10:

[0520] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0521] Step 11:

[0522] Based on the information obtained from the dashboard, users can conduct on-site surveys and implement specific measures such as installing necessary protective nets and conducting capture activities.

[0523] Step 12:

[0524] Users provide feedback to the system on the results of the measures they have implemented and any new local information. The server collects this feedback and uses it to improve the accuracy of the prediction algorithm and emotion engine. This feedback is used to make more accurate predictions in the future.

[0525] Example 2

[0526] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0527] Conventional pest infestation prediction systems collect individual data or make predictions from a single data source, but lack the ability to comprehensively analyze a wide variety of influencing factors. Furthermore, they often propose countermeasures without taking the user's emotions into consideration, which can result in low user satisfaction. The present invention aims to realize highly accurate predictions of pest infestation risk and effective countermeasure proposals based on the user's emotions.

[0528] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring animal sighting information from each administrative agency or organization, a means for acquiring climate change data from a weather information providing system, and a means for acquiring environmental management and development information from local administrative agencies. This makes it possible to predict the risk of animal sightings with high accuracy and propose appropriate and effective countermeasures based on the user's emotions.

[0529] "Each government agency or organization" refers to public organizations and related non-profit organizations that provide animal sighting information.

[0530] "Animal sighting information" refers to information including the date and time, location (latitude and longitude), type, and damage details of wild animals that have appeared in a specific area.

[0531] "Weather information providing system" refers to a system that provides climate change data such as temperature, precipitation, and air pressure.

[0532] "Climate change data" refers to information about weather conditions such as temperature, rainfall, and air pressure.

[0533] "Local government agencies" refer to local government bodies that provide information on environmental management and urban development in a particular area.

[0534] "Environmental management information" refers to information about activities that affect the natural environment, such as new logging plans or large-scale construction projects.

[0535] "Development information" refers to information about urban planning and construction projects.

[0536] "Data Storage" means a database or other recording means for storing collected data.

[0537] "Linked / mutual analysis" refers to an analytical method that integrates multiple types of data and evaluates their mutual relevance.

[0538] "Animal sighting risk" refers to a risk index that assesses the likelihood of future wild animal sightings in a particular area.

[0539] "Predictive algorithms" refer to mathematical methods and models used to calculate future risk of animal appearances based on collected data.

[0540] "Countermeasure proposals" refer to recommended measures and methods for preventing animal damage based on the prediction results.

[0541] "Relevant users" refers to users who need the collected data and countermeasure proposals, such as farmers and local government officials.

[0542] "Notification" refers to the means of communication used to inform relevant users of proposed measures, such as email or in-app notification.

[0543] An "emotion engine" refers to technology or software that analyzes a user's emotions and makes suggestions or responses accordingly.

[0544] This invention is a system that predicts the risk of animal sightings with high accuracy by linking and mutually analyzing animal sighting information from various government agencies and organizations, climate change data from weather information systems, and environmental management and development information from local government agencies.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose measures that are optimal for the user.

[0545] Data collection

[0546] The server collects animal sighting information from various government agencies and organizations. This information includes the date and time of the animal's appearance, its location (latitude and longitude), its species, and the details of the damage. Specifically, the server periodically obtains this data using a web API and saves it in data storage. For example, the server obtains information such as "Wild boars destroyed fields in a specific area on October 10, 2023."

[0547] Next, the server obtains climate change data from the weather information system. This data includes information such as temperature, precipitation, and air pressure. The server periodically obtains the data using the Japan Meteorological Agency's API and saves it in data storage. For example, the server collects data such as "The maximum temperature in a specific area on October 10, 2023 will be 30°C and the amount of precipitation will be 10mm."

[0548] In addition, the server obtains environmental management and development information from local government agencies. This information includes new logging plans and large-scale construction projects. The server obtains this information using urban planning APIs and stores it in data storage. For example, the server obtains information such as "Plans for the construction of a new highway in a specific area are underway."

[0549] Data linkage and analysis

[0550] The server integrates and analyzes the collected animal sighting information, climate change data, and environmental management and development information. These three types of information are organized chronologically and their correlations are evaluated. The server analyzes how climatic conditions and development plans in a specific area affect the risk of animal sightings. For example, it evaluates correlations such as "rising temperatures in a specific area and new logging plans increase the risk of wild boar sightings."

[0551] Risk prediction and countermeasure proposals

[0552] The server uses a predictive algorithm to score the risk of animal infestation in each area. For example, areas with rising temperatures and ongoing logging plans are assigned a high risk score. The results are stored in data storage and simultaneously visualized on a dashboard.

[0553] The server then generates countermeasure suggestions based on the prediction results and notifies relevant users, for example, recommending the installation of pest netting or strengthening on-site inspections in high-risk areas. Notifications are sent to relevant users via email or in-app notifications.

[0554] Using the Emotion Engine

[0555] The server uses an emotion engine to recognize the user's emotions. For example, if the user rates a proposed measure as "very satisfied," the server evaluates the measure as appropriate. On the other hand, if the user expresses negative emotions, the server reevaluates the proposal and considers alternative measures.

[0556] User feedback and system accuracy improvement

[0557] Users access the dashboard using their devices to check the latest forecast information and proposed countermeasures. For example, risk areas are color-coded on a map, allowing users to identify high-risk areas at a glance. Based on the information provided, users conduct on-site surveys and implement specific countermeasures, such as installing protective nets and conducting capture activities. After that, users provide feedback on the results of their surveys and any new on-site information to the server. The server collects this feedback information and uses it to improve the accuracy of the forecast algorithm and emotion engine.

[0558] Specific examples and input prompts for the generative AI model

[0559] Example: "What are the risks of animal sightings in a specific area during the second week of October and what are the appropriate countermeasures?"

[0560] This system takes into consideration the user's feelings through the above process, preventing animal damage before it occurs and contributing to the sustainable development of agriculture and forestry.

[0561] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0562] Step 1:

[0563] The server obtains animal sighting information from various government agencies and organizations. This information includes the date and time of the animal's appearance, its location (latitude and longitude), its species, and the details of the damage. The server periodically obtains the data using a web API and saves it in data storage. For example, the server obtains information such as "Field damage caused by wild boars in a specific area on October 10, 2023." The input is the data obtained from the API, and the output is the animal sighting information saved in data storage.

[0564] Step 2:

[0565] The server obtains climate change data from the weather information system. Climate change data includes temperature, precipitation, and air pressure, and this data is also obtained periodically using an API and stored in data storage. For example, information such as "the maximum temperature in a specific area on October 10, 2023 will be 30°C and the amount of precipitation will be 10mm" is collected. The input is weather information obtained from the API, and the output is climate change data stored in data storage.

[0566] Step 3:

[0567] The server obtains environmental management and development information from local government agencies. The information obtained includes new logging plans and large-scale construction projects. The server uses urban planning APIs to periodically obtain and store data. For example, it collects information such as "a new highway construction plan is underway in a specific area." The input is the development information obtained from the API, and the output is the environmental management and development information stored in data storage.

[0568] Step 4:

[0569] The server connects collected animal sighting information, climate change data, and environmental management and development information from data storage and analyzes them mutually. The server organizes each piece of information in chronological order and evaluates their correlation. For example, it analyzes whether "rising temperatures in a specific area and new logging plans increase the risk of wild boar sightings." The input is various types of stored data, and the output is the results of mutual analysis.

[0570] Step 5:

[0571] The server uses a predictive algorithm to score the risk of animal infestation in each area. The input is the cross-analyzed data, and the output is a risk score. The server assigns a high risk score to areas with rising temperatures and planned logging, saves the results in data storage, and visualizes them on a dashboard. For example, a specific area may be assessed with a risk score of 70 / 100.

[0572] Step 6:

[0573] The server uses an emotion engine to recognize the user's emotions. For example, if a user rates a proposed measure as "very satisfied," it is assumed that the proposal is appropriate. The input is the user's feedback information, and the output is the analysis result through the emotion engine.

[0574] Step 7:

[0575] The server generates countermeasure proposals based on the prediction results and the emotion engine's analysis results. For example, it may recommend "installing pest nets" or "strengthening on-site inspections" in high-risk areas. The generated countermeasure proposals are sent to relevant users via email or in-app notifications. The input is the risk score and the emotion engine's analysis results, and the output is specific countermeasure proposals.

[0576] Step 8:

[0577] Users access the dashboard from their devices to check the latest forecast information and proposed countermeasures. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance. For example, specific areas are displayed in red. The input is risk and countermeasure proposal data, and the output is visualized risk information.

[0578] Step 9:

[0579] Users implement the proposed measures, such as installing protective nets and carrying out capture activities. They then provide feedback on the results of their actions and any new on-site information to the server. The server collects this information and uses it to improve the accuracy of the prediction algorithm and emotion engine. The input is feedback information from users, and the output is data used to improve the system's accuracy.

[0580] (Application example 2)

[0581] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0582] In modern times, damage caused by pest infestations has a significant impact on agriculture, forest management, and the lives of residents. Furthermore, as climate change and changes in development status affect the behavioral patterns of pests, risk prediction is becoming increasingly complex. Conventional countermeasure systems that use pest infestation information and weather data lack the ability to link these data for analysis, and do not take user emotions or feedback into account, making it difficult to provide optimal countermeasures tailored to real-world situations.

[0583] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring vermin infestation information from local governments and organizations, means for acquiring climate change data from a weather information service, means for acquiring satoyama management and development information from local governments, means for storing the vermin infestation information, climate change data, and development information in a database, means for linking and mutually analyzing the various stored data, means for predicting the risk of vermin infestation, means for generating countermeasure proposals based on the prediction results, means for notifying relevant users of the generated countermeasure proposals, means for recognizing user emotions and customizing the countermeasure proposals based on feedback, and means for providing users with a smartphone app that displays the vermin infestation risk in a color-coded map. This enables improved accuracy in predicting vermin infestation risk and countermeasure proposals, and individual responses based on user feedback.

[0584] "Municipalities" are local public organizations that provide information on pest infestations, Satoyama management, and development information.

[0585] "Organization" refers to an organization other than a local government that has the role of collecting and providing information on pest infestations.

[0586] "Vermin sighting information" is data relating to the date and time of a vermin sighting, its location, type, and the details of the damage.

[0587] "Climate change data" is information about weather fluctuations, such as temperature, rainfall, and air pressure.

[0588] "Satoyama management information" refers to information related to the management of Satoyama, such as logging plans and conservation plans.

[0589] "Development information" is data about local land use and development plans, including new construction projects.

[0590] A "database" is a system for storing various types of information and for linking and mutual analysis.

[0591] "Collaboration and cross-analysis" is the process of combining multiple pieces of information, evaluating their relevance, and deriving comprehensive insights.

[0592] A "predictive algorithm" is a mathematical model for calculating and predicting the risk of pest infestation.

[0593] "Countermeasure proposals" are proposals that provide specific protective measures and guidelines for action based on the risk of pest infestation.

[0594] "Notification means" is a function that notifies the user of the generated countermeasure proposals via email or in-app notification.

[0595] The "emotion engine" is a system that analyzes user feedback and recognizes emotions.

[0596] "Customization" means individually adjusting the content of suggestions and notification methods based on the user's emotions and feedback.

[0597] The "smartphone app" is an application for mobile devices that provides users with information on the risk of pest infestations and suggestions for countermeasures.

[0598] "Color-coded display" is a display method that uses different colors on a map to visually distinguish risk levels.

[0599] This invention provides a system that links and analyzes pest infestation information, climate change data, and development information to predict the risk of pest infestation and propose countermeasures. This system collects and integrates data from various municipalities and organizations, weather information providers, and local governments, and generates countermeasure proposals by combining a predictive algorithm and an emotion engine.

[0600] Data collection

[0601] The server obtains pest sighting information from local governments and organizations via API and stores it in a database. This pest sighting information includes the date and time of sighting, location (latitude and longitude), species, and details of damage. Next, it obtains climate change data from a weather information provider and stores it in the same database. This includes weather information such as temperature, rainfall, and air pressure. Furthermore, it obtains satoyama management and development information, such as logging plans and construction projects, from local governments and stores it in the database.

[0602] Data linkage and analysis

[0603] The server links and organizes the accumulated pest infestation information, climate change data, and development information in chronological order, and evaluates the correlation between each piece of information. This linking and analysis is performed using a Python program and libraries such as Scikit-learn. The server also uses a predictive algorithm to score the pest infestation risk for each area and stores the results in a database.

[0604] Incorporating an emotion engine

[0605] The server incorporates an emotion engine that recognizes the user's emotions when generating countermeasure proposals. This emotion engine analyzes user feedback using, for example, IBM Watson NLP API to recognize emotions. If the user expresses positive or negative emotions toward a countermeasure proposal, the appropriateness of the proposal is evaluated and reflected in the next proposal generation.

[0606] Countermeasure proposals and notifications

[0607] The server generates countermeasure proposals based on the prediction results and the analysis results of the emotion engine. The generated countermeasure proposals include recommendations for rapid pest control activities in emergencies and the installation of protective nets. These countermeasure proposals are notified to relevant users (e.g., hunting license holders and local government officials) via a smartphone app. Notifications are sent via email or in-app push notifications, and the method and content of the notifications are adjusted based on the user's emotions.

[0608] Feedback and improved prediction accuracy

[0609] Users can check the latest forecast information and countermeasure suggestions through the dashboard of the smartphone app. Feedback information provided by users is sent to the server and used to improve the accuracy of the prediction algorithm and emotion engine. This allows the system to evolve over time and make more appropriate countermeasure suggestions.

[0610] Implementation example

[0611] For example, if a region experiences rising temperatures and new development projects at the same time, the system can predict an increased risk of pest infestations and send notifications to users in the area recommending the installation of protective netting. If users provide positive feedback on the suggestions, the system will consider the suggestions effective and will make similar suggestions in future similar situations.

[0612] Prompt Sentence Examples

[0613] markdown

[0614] Invention Contents

[0615] Develop a new application that combines the emotion engine of a system that links and cross-analyzes pest infestation information, climate change data, and development information to predict the risk of pest infestations. As an example, we are looking for ideas for a security app for smartphones that suggests personalized measures based on the user's emotions, allowing residents to respond appropriately.

[0616] input

[0617] The system collects and analyzes information on pest infestations, climate change data, and development information from local governments, organizations, and weather information providers, and predicts the risk of pest infestations.

[0618] Uses an emotion engine to customize countermeasure suggestions based on user feedback.

[0619] Example output

[0620] 1. A smartphone app that displays the risk of pest infestations in a color-coded manner on a map.

[0621] 2. Notify and display customized countermeasure suggestions based on the user's emotions.

[0622] 3. It has a dashboard function that allows you to understand risk areas at a glance.

[0623] 4. Specific implementation methods include clearly indicating the APIs and libraries to be used (e.g., Google Maps API, Scikit-learn, IBM Watson NLP API).

[0624] It is expected that the system for implementing this invention will effectively prevent damage caused by pests and contribute to the sustainability of agricultural and forest management.

[0625] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0626] System program processing steps

[0627] Step 1:

[0628] Information on pest sightings is obtained from each local government or organization. The server accesses the API of the local government or organization to obtain the date and time of sighting, location (latitude and longitude), type, and details of damage. The obtained data is stored in the server's database. The input is pest sighting information from the API, and the output is the information stored in the database.

[0629] Step 2:

[0630] Climate change data is obtained from a weather information service. The server uses the weather information service's API to obtain weather information such as temperature, precipitation, and air pressure. The obtained data is stored in a database. The input is climate change data from the API, and the output is the information stored in the database.

[0631] Step 3:

[0632] The server obtains Satoyama management and development information from local governments. Using the local government's API, the server obtains information such as new logging plans and construction projects. The obtained data is stored in a database. The input is Satoyama management and development information from the API, and the output is the information stored in the database.

[0633] Step 4:

[0634] The various types of stored data are linked and cross-analyzed. The server uses a Python program to link and cross-analyze the pest infestation information, climate change data, and development information stored in the database. This is the process of organizing data in chronological order and evaluating the relevance between each piece of information. The input is the information stored in the database, and the output is the results of the linkage and cross-analysis.

[0635] Step 5:

[0636] Predict the risk of pest infestation. The server uses the Scikit-learn library to apply a prediction algorithm and score the risk of pest infestation for each area. The input is the results of collaboration and cross-analysis, and the output is the risk score for each area.

[0637] Step 6:

[0638] Generate countermeasure proposals. Based on the prediction results, the server generates specific countermeasure proposals such as pest control activities and the installation of protective nets. The input is the risk score, and the output is the countermeasure proposals.

[0639] Step 7:

[0640] The emotion engine is used to customize countermeasure suggestions. The server uses IBM Watson NLP API to analyze user feedback and recognize the user's emotions. Based on the results, countermeasure suggestions are adjusted and customized. The input is user feedback, and the output is customized countermeasure suggestions.

[0641] Step 8:

[0642] The generated countermeasure proposals are notified to the user. The server notifies the user of the countermeasure proposals via a smartphone app via push notification or email. The input is the customized countermeasure proposals, and the output is a notification to the user.

[0643] Step 9:

[0644] Receives user feedback and uses it to improve the accuracy of the system. Users send feedback on proposed measures via a smartphone app. The server receives this feedback and uses it to improve the accuracy of the prediction algorithm and emotion engine. The input is user feedback, and the output is an improved prediction algorithm and emotion engine.

[0645] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0646] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0647] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0648] [Third embodiment]

[0649] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0650] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0651] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0652] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0653] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0654] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0655] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0656] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0657] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0658] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0659] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0660] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0661] This invention is a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on that. This system is composed of a server, terminals, and users.

[0662] Data collection

[0663] First, the server obtains information on pest infestations from each local government or organization. This information includes the date and time of the infestation, location (latitude and longitude), species, and damage details. The server periodically obtains this data using a web API and stores it in a database.

[0664] Next, the server retrieves climate change data from a weather information service. This data includes information about climate changes, such as temperature, precipitation, and air pressure. The server periodically retrieves the data using the weather information API and stores it in a database.

[0665] Additionally, the server obtains Satoyama management and development information from local governments, including new logging plans and large-scale construction projects, using a web API to retrieve this information and store it in a database.

[0666] Data linkage and analysis

[0667] The server connects and cross-analyzes pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information chronologically and evaluates its relevance. The server analyzes how climatic conditions and development plans in a specific area affect the risk of pest infestation.

[0668] The server then uses a predictive algorithm to score each region's risk of pest infestation. For example, if a region experiences both rising temperatures and new logging projects, it predicts that the region's risk of infestation will increase. The server stores these predictions in a database and visualizes them on a dashboard.

[0669] Countermeasure proposals

[0670] The server generates countermeasure proposals based on the prediction results, such as recommending the installation of pest netting and strengthening of on-site inspections in high-risk areas, or prompt implementation of pest control activities in emergencies.

[0671] The generated countermeasure proposals are notified to relevant users (e.g., hunting license holders or local government officials). The server does this using email and in-app notifications.

[0672] User notification and countermeasure implementation

[0673] Users can access the dashboard using their devices (PC or smartphone) to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0674] Based on the provided information, users conduct on-site surveys and implement specific countermeasures such as installing protective nets and capturing the animals. Users also provide feedback to the system on the results of the measures they have implemented and on new on-site information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm.

[0675] Through the above processes, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0676] The processing flow will be explained below.

[0677] Step 1:

[0678] The server runs a daily script and sends requests to each local government's Web API endpoint. The server obtains pest sighting information (date and time of sighting, location, species, and damage details), converts the obtained data into an appropriate format, and saves it in a database.

[0679] Step 2:

[0680] The server calls the API of a weather service to obtain climate change data such as temperature, precipitation, and air pressure. The server stores this climate data in a database. The weather data often includes daily, weekly, and monthly statistics.

[0681] Step 3:

[0682] The server periodically accesses the local government's Web API endpoint to obtain information on Satoyama management and mountain area development (new logging plans, large-scale construction projects, etc.), and stores this development information in a database.

[0683] Step 4:

[0684] The server extracts pest infestation information, climate change data, and development information from the database, executes queries to combine these data, organizes the combined data in chronological order, and evaluates the relevance of each piece of information.

[0685] Step 5:

[0686] The server applies predictive algorithms to analyze the combined data retrieved from the database. The server scores each region's pest infestation risk and generates a pest infestation forecast. For example, it predicts that areas experiencing a combination of rising temperatures and drought will be at increased risk of pest infestation.

[0687] Step 6:

[0688] Based on the prediction results, the server generates specific countermeasure proposals for high-risk areas, such as "Due to the increasing risk of overhunting in this mountainous area over the next three months, we recommend that the hunting association strengthen patrols."

[0689] Step 7:

[0690] The server stores the generated countermeasure proposals in a database, reflects them on the dashboard, and delivers them to relevant users via email and in-app notifications.

[0691] Step 8:

[0692] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0693] Step 9:

[0694] Based on the information obtained from the dashboard, users can conduct on-site surveys and implement specific measures such as installing necessary protective nets and conducting capture activities.

[0695] Step 10:

[0696] Users provide feedback to the system on the results of the measures they have implemented and new local information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm. This feedback is used to make more accurate predictions in the future.

[0697] Example 1

[0698] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0699] In recent years, damage caused by pests in agriculture and forestry has become increasingly serious. It is particularly important to accurately predict the impact of climate change and development activities on pest behavior and to implement appropriate countermeasures. However, conventional methods have difficulty comprehensively managing and analyzing such complex factors. Therefore, a system that can effectively predict the risk of pest infestation and propose appropriate countermeasures is needed.

[0700] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0701] In this invention, the server includes: means for acquiring pest infestation information from local governments and organizations; means for acquiring climate change data from weather information services; means for acquiring satoyama management and development information from local governments; means for storing the pest infestation information, climate change data, and development information in a database; means for linking and cross-analyzing the various stored data; means for predicting the risk of pest infestation; means for generating countermeasure proposals based on the prediction results; means for notifying related users of the generated countermeasure proposals; means for displaying the prediction information and countermeasure proposals via a user interface; and means for receiving feedback information from related users and using it to improve the accuracy of the prediction algorithm. This enables a comprehensive analysis of the impact of climate change and development activities on pest behavior, enabling accurate risk predictions and appropriate countermeasure proposals.

[0702] "Vermin sighting information" is information reported by local governments and organizations regarding the location and date of appearance of vermin, the extent of damage, etc.

[0703] "Climate change data" refers to data on meteorological elements such as temperature, precipitation, and air pressure, and is obtained from weather information services.

[0704] "Development information" refers to information provided by local governments on satoyama management, new logging plans, large-scale construction projects, and so on.

[0705] A "database" is a system for storing various acquired information and for searching and analyzing it as needed.

[0706] "Linkage and cross-analysis" is the act of integrating multiple data sets and analyzing their interrelationships.

[0707] "Pest infestation risk" is an indicator that shows the possibility of pests appearing in a particular area.

[0708] "Countermeasure proposals" are specific actions or measures recommended to reduce the risk of pest infestation.

[0709] "Users" are relevant individuals or organizations, such as local government officials and hunting license holders, who use this system.

[0710] A "generative AI model" is an artificial intelligence algorithm used in data analysis to generate predictions.

[0711] "Feedback information" refers to the results of measures taken by the user and newly obtained local information, and is fed back to the system.

[0712] This invention provides a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on the results. This system is composed of a server, terminals, and users.

[0713] Data collection

[0714] The server first obtains pest sighting information from each local government or organization. This information includes the date and time of the pest sighting, location (latitude and longitude), type, and details of the damage. The server periodically obtains this data using a web API and stores it in a database. As a specific example, the server obtains data from the API "https: / / example.com / api / animal_sightings" and stores it in the "Sighting_Info" table in the database.

[0715] The server then retrieves climate change data from a weather information service, including temperature, precipitation, and air pressure. The server accesses https: / / weatherapi.com / data and stores the data in a database table called Climate_Data.

[0716] Additionally, the server retrieves Satoyama management and development information from local governments, including new logging plans and large-scale construction projects. The server accesses the API "https: / / municipality.net / dev_info" and stores the data in the "Development_Info" table in the database.

[0717] Data linkage and analysis

[0718] The server performs cross-analysis based on the pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information in chronological order and uses Python libraries such as Pandas and Scikit-learn to evaluate the relevance.

[0719] For example, the server combines data sets to analyze how rising temperatures and new logging plans in a particular region affect the risk of pest infestations. It uses predictive algorithms like random forests and neural networks to score each region's risk. The results are stored in a database and visualized on a dashboard.

[0720] Countermeasure proposals

[0721] The server generates countermeasure proposals based on the prediction results. For example, it may recommend installing pest nets or strengthening on-site inspections in high-risk areas. It may also call for prompt pest control activities in emergencies. The generated countermeasure proposals are notified to relevant users. The server distributes the information via email or in-app notifications.

[0722] User notification and countermeasure implementation

[0723] Users access the dashboard using their devices (PC or smartphone) to check the forecast information and proposed countermeasures. The dashboard displays risk areas in different colors on a map, allowing users to identify high-risk areas at a glance. Users conduct on-site surveys based on the information provided and implement specific countermeasures such as installing protective nets and conducting capture activities. Users also provide feedback to the system on the results of the implemented countermeasures and any new on-site information. The server collects this feedback information and uses it to improve the accuracy of the forecast algorithm.

[0724] Prompt Sentence Examples

[0725] Use the system to predict the risk of pest infestation in a specific area and generate countermeasure proposals. For example, evaluate the risk in an area where the temperature is over 30 degrees, there is little rainfall, and there are plans for new logging, and propose appropriate countermeasures.

[0726] As a result of the above, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0727] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0728] Step 1: Data collection

[0729] First, the server obtains information on pest sightings from each local government or organization. This information includes the date and time of sighting, location (latitude and longitude), species, and details of the damage. The input is raw data obtained from the web API, and the output is formatted information stored in the "Sighting_Info" table in the database. Specifically, the server accesses "https: / / example.com / api / animal_sightings," parses the obtained JSON data, and stores it in the database.

[0730] Next, the server retrieves climate change data from the weather information service. The input is weather data retrieved from the API, and the output is stored in the "Climate_Data" table in the database. Specifically, the server accesses "https: / / weatherapi.com / data" and periodically retrieves information such as temperature, precipitation, and air pressure, and stores it in the database.

[0731] Furthermore, the server obtains Satoyama management and development information from local governments. The input is the development information provided, and the output is stored in the "Development_Info" table of the database. Specifically, the server accesses "https: / / municipality.net / dev_info" to obtain information on new logging plans and large-scale construction projects and stores it in the database.

[0732] Step 2: Data integration

[0733] The server performs data integration processing of pest infestation information, climate change data, and development information stored in the database. It uses information from the "Sighting_Info," "Climate_Data," and "Development_Info" tables as input and generates an integrated dataset as output. Specifically, the server uses Python Pandas and SQL queries to organize each dataset in chronological order and combine related data.

[0734] Step 3: Data analysis

[0735] The server performs analysis using the federated dataset. It has the integrated dataset as input and a pest infestation risk score for each region as output. Specifically, the server uses generative AI models to analyze how specific weather conditions or development plans affect pest infestation risk. For example, it uses random forests or neural networks to score the risk for each region.

[0736] Step 4: Generate countermeasure proposals

[0737] The server generates countermeasure proposals based on the analysis results. The input is the pest infestation risk score, and the output is specific countermeasure proposals. Specific operations include the installation of pest nets in high-risk areas and strengthening on-site inspections, and the proposals are saved in a database.

[0738] Step 5: Notify users

[0739] The server notifies the relevant users of the generated countermeasure proposals. The input is each generated countermeasure proposal, and the output is a notification to the user. Specific operations include delivering information via email or in-app notifications.

[0740] Step 6: User confirmation and implementation of measures

[0741] Users access the dashboard using their devices (PC or smartphone) to check the forecast information and proposed countermeasures. Notifications are received from the server as input, and the user implements countermeasures as output. Specifically, risk areas are displayed in different colors on the dashboard, and the user takes action based on the proposed countermeasures.

[0742] Step 7: Gather feedback

[0743] Users provide feedback to the server on the results of the measures they have implemented and any new local information. The input is feedback information from users, and the output is improvements to the accuracy of the prediction algorithm. Specifically, the server receives the feedback information, stores it in a database, and uses it to retrain the prediction algorithm.

[0744] Through these processing steps, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0745] (Application example 1)

[0746] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0747] Damage to crops and property caused by pest infestations is a serious problem, and effective prevention requires accurate prediction of pest infestation risk and prompt implementation of countermeasures. However, existing systems lack the ability to link and utilize pest infestation information, climate change data, and development information, and are lacking in mechanisms for displaying risks in real time and continuously incorporating user feedback. As a result, countermeasures to prevent pest damage are often delayed. The present invention aims to solve these issues and provide more effective prediction of pest infestation risk and proposal of countermeasures.

[0748] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0749] In this invention, the server includes means for acquiring pest infestation information from local governments and organizations, means for acquiring climate change data from weather information services, means for acquiring satoyama management and development information from local governments, means for storing the pest infestation information, climate change data, and development information in a database, means for linking and cross-analyzing the various stored data, means for predicting the risk of pest infestation, means for generating countermeasure proposals based on the prediction results, means for notifying relevant users of the generated countermeasure proposals, means for displaying the infestation risk in color-coded on a map for users in real time, and means for reporting the effectiveness of countermeasures implemented by users and new local information. This makes it possible to quickly and accurately predict the risk of pest infestation, enabling rapid response and effective damage prevention measures to be implemented.

[0750] "Means for obtaining information on pest infestations from local governments and organizations" refers to methods and devices for collecting information on pest infestations from local governments and related organizations.

[0751] The "means for acquiring climate change data from a weather information service" refers to a method or device for acquiring data related to climate change, such as temperature, precipitation, and air pressure, from a weather information service.

[0752] "Means for obtaining information on Satoyama management and development from local governments" refers to methods and devices for collecting information on Satoyama management and development plans from local governments.

[0753] "Means for storing the pest infestation information, climate change data, and development information in a database" refers to a method or device for storing the collected pest infestation information, climate change data, and development information in a database that centrally manages the information.

[0754] "Means for linking and mutually analyzing various types of stored data" refers to methods and devices for linking information stored in a database and evaluating and analyzing the mutual relationships.

[0755] A "means for predicting the risk of pest infestation" is a method or device for predicting the risk of pest infestation in a specific area based on collected and analyzed data.

[0756] The "means for generating countermeasure proposals based on prediction results" refers to a method or device for proposing appropriate protective measures or countermeasures based on the prediction results of the risk of pest infestation.

[0757] "Means for notifying relevant users of the generated countermeasure proposals" refers to a method or device for notifying relevant users (e.g., hunting license holders or local government officials) of the generated countermeasure proposals.

[0758] "Means for displaying appearance risks in different colors on a map in real time to the user" refers to a method or device for displaying appearance risks in different colors on a map in real time so that the user can grasp the risks at a glance.

[0759] The "means by which the user can report the effects of measures taken and new local information" refers to a method or device for the user to report the effects of measures taken and newly collected local information to the system.

[0760] This invention is a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts specific pest infestation risks, and proposes countermeasures based on the predictions. This system is composed of a server, terminals, and users.

[0761] Data collection

[0762] The server obtains pest sighting information from each local government or organization. Specifically, it periodically obtains pest sighting information (date and time of sighting, location, type, and damage details) using APIs provided by the local government or organization and stores it in a database.

[0763] The server also uses an API to obtain climate change data (temperature, precipitation, air pressure, etc.) from a weather information service, periodically obtains this data, and stores it in a database.

[0764] In addition, the server obtains Satoyama management and development information (such as new logging plans and construction projects) from local governments and stores this information in a database as well.

[0765] Data linkage and analysis

[0766] The server links and analyzes the information stored in the database, including pest infestation information, climate change data, and development information. Specifically, it organizes this information in chronological order and evaluates its relevance.

[0767] The server then uses a predictive algorithm to score each area's risk of pest infestation. For example, an area experiencing both rising temperatures and new logging projects could be predicted to be at increased risk of pest infestation. The risk scores are stored in a database and visualized on a map.

[0768] Real-time map display

[0769] The application installed on the user's device (smartphone or PC) retrieves the latest risk score from the server and displays risk areas in color on a map in real time, allowing the user to see high-risk areas at a glance.

[0770] Countermeasure proposals and user notifications

[0771] Furthermore, the server generates countermeasure suggestions based on the prediction results and notifies the user via in-app notifications or emails. The countermeasure suggestions include installing protective nets, strengthening on-site inspections, and promptly implementing pest control activities in case of emergency.

[0772] Feedback and improved prediction accuracy

[0773] Users can report the effectiveness of the measures they have implemented and new local information they have acquired within the system. The server receives this feedback information and uses it to improve the accuracy of the prediction algorithm.

[0774] Examples and prompts

[0775] For example, if there are plans for new logging in a certain area of ​​Akita Prefecture and temperatures have recently risen, the system will determine that the area is a high-risk area and notify the local government to "strengthen on-site inspections and install pest nets."

[0776] Example prompt sentence:

[0777] You are given datasets from [ANIMAL_API], [WEATHER_API], and [DEV_API] containing wildlife occurrence, weather changes, and development plans. Use these datasets to create a script to assess and visualize wildlife risk areas. Generate countermeasures if the risk score exceeds a certain threshold.

[0778] The system aims to prevent damage caused by pests and promote the sustainable development of agriculture and forestry.

[0779] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0780] Step 1: Data collection

[0781] The server obtains information on pest sightings from local governments and organizations. The data obtained includes the date and time of sighting, location (latitude and longitude), species, and details of damage. Here, information is collected periodically using an API and stored in a database. Similarly, climate change data (temperature, rainfall, air pressure, etc.) is obtained from a weather information provider via API and stored in the database. In addition, information on satoyama management and development (new logging plans, large-scale construction projects, etc.) is collected from local governments and this is also stored in the database. In this way, pest sighting information, climate change data, and development information are accumulated in the database.

[0782] Step 2: Data integration and analysis

[0783] The server chronologically organizes the pest infestation information, climate change data, and development information stored in the database and evaluates their correlations. To do this, it uses an algorithm that links each data set and analyzes correlations. For example, it analyzes patterns such as an increase in the risk of infestation in an area when a rise in temperature occurs simultaneously with a new logging plan. It uses pest infestation information, climate data, and development information as input data and generates a risk score as output.

[0784] Step 3: Risk prediction

[0785] The server uses a predictive algorithm based on the linked and analyzed data to calculate the risk of pest infestation for each region. A risk score is set for each region and saved in the database. For example, an area where rising temperatures coincide with planned logging will be scored as high risk. The input data is the results of the linkage and analysis, and the output is a risk score.

[0786] Step 4: Visualization

[0787] The device (smartphone or PC) retrieves the latest risk score from the server and displays the risk area on a map in real time, color-coding it. This visualized information helps users understand high-risk areas at a glance. The input data is the risk score, and the output is a color-coded map.

[0788] Step 5: Propose a solution

[0789] The server generates appropriate countermeasure proposals based on the prediction results. For example, it recommends installing protective nets, strengthening on-site inspections, and promptly exterminating pests in emergencies. The generated countermeasure proposals are stored in a database and sent to relevant users (e.g., hunting license holders and local government officials) via in-app notifications or email. The input data is a risk score, and the output is a countermeasure proposal.

[0790] Step 6: Notify users and implement countermeasures

[0791] Users access the dashboard using their devices to check the latest risk information and proposed countermeasures. By viewing risk areas color-coded on a map, they can quickly implement necessary countermeasures, such as conducting on-site surveys or installing protective nets. The input data is countermeasure proposals, and the output is countermeasure implementation.

[0792] Step 7: Gather feedback and improve the algorithm

[0793] Users report the effectiveness of the measures they have implemented and new local information within the application. The server incorporates this feedback information into a database and uses it to improve the accuracy of the prediction algorithm. The input data is user feedback, and the output is an improved prediction algorithm and its results.

[0794] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0795] This invention combines a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on that, with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[0796] Data collection

[0797] First, the server obtains information on pest infestations from each local government or organization. This information includes the date and time of the infestation, location (latitude and longitude), species, and damage details. The server periodically obtains this data using a web API and stores it in a database.

[0798] Next, the server retrieves climate change data from a weather information service. This data includes information about climate changes, such as temperature, precipitation, and air pressure. The server periodically retrieves the data using the weather information API and stores it in a database.

[0799] Additionally, the server obtains Satoyama management and development information from local governments, including new logging plans and large-scale construction projects, using a web API to retrieve this information and store it in a database.

[0800] Data linkage and analysis

[0801] The server connects and cross-analyzes pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information in chronological order and evaluates the correlation between each piece of information. The server analyzes how climatic conditions and development plans in a specific area affect the risk of pest infestation.

[0802] The server then uses a predictive algorithm to score each region's risk of pest infestation. For example, if a region experiences both rising temperatures and new logging projects, it predicts that the region's risk of infestation will increase. The server stores these predictions in a database and visualizes them on a dashboard.

[0803] Incorporating an emotion engine

[0804] The server incorporates an emotion engine that recognizes the user's emotions when generating the countermeasure proposal. The emotion engine can recognize the user's emotions by analyzing the user's reactions and feedback when receiving the countermeasure proposal.

[0805] For example, if the user expresses positive feelings toward the proposed measure, the server will evaluate the suggestion as appropriate and use this as a reference for future similar suggestions. On the other hand, if the user expresses negative feelings, the server will reevaluate the suggestion and consider alternative measures that are more suitable for the user.

[0806] Countermeasure proposals and notifications

[0807] The server generates countermeasure proposals based on the prediction results and the analysis results of the emotion engine. For example, it may recommend the installation of pest netting and strengthening of on-site inspections in high-risk areas. In an emergency, it may also request the prompt implementation of pest control activities.

[0808] The generated action proposals are notified to relevant users (e.g., hunting license holders or local government officials). The server does this using email or in-app notifications. The emotion engine can adjust the notification method and content based on the user's emotions.

[0809] User notification and countermeasure implementation

[0810] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0811] Based on the information provided, users conduct on-site surveys and implement specific countermeasures, such as installing protective nets and capturing the animals. Users also provide feedback to the system on the results of the measures they have implemented and on new on-site information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm and emotion engine.

[0812] Through the above process, this system takes into consideration the user's feelings, prevents damage caused by pests, and contributes to the sustainable development of agriculture and forestry.

[0813] The processing flow will be explained below.

[0814] Step 1:

[0815] The server runs a daily script and sends requests to each local government's Web API endpoint. The server obtains pest sighting information (sighting date and time, location, species, and damage details), converts the format as needed, and saves it in a database.

[0816] Step 2:

[0817] The server calls the API of a weather service to obtain climate change data such as temperature, precipitation, and air pressure. The server stores this climate data in a database. The weather data often includes daily, weekly, and monthly statistics.

[0818] Step 3:

[0819] The server periodically accesses the local government's Web API endpoint to obtain information on Satoyama management and mountain area development (new logging plans, large-scale construction projects, etc.), and stores this development information in a database.

[0820] Step 4:

[0821] The server extracts pest infestation information, climate change data, and development information from the database, executes queries to combine these data, organizes the combined data in chronological order, and evaluates the relevance of each piece of information.

[0822] Step 5:

[0823] The server applies predictive algorithms to analyze the combined data retrieved from the database. The server scores each region's pest infestation risk and generates a pest infestation forecast. For example, it predicts that areas experiencing a combination of rising temperatures and drought will be at increased risk of pest infestation.

[0824] Step 6:

[0825] Based on the prediction results, the server generates specific countermeasure proposals for high-risk areas, such as "Due to the increasing risk of overhunting in this mountainous area over the next three months, we recommend that the hunting association strengthen patrols."

[0826] Step 7:

[0827] Before notifying the user of the proposed measures, the server adds a means to recognize the user's emotions using an emotion engine. The emotion engine analyzes the user's emotions based on the user's past feedback and reaction data.

[0828] Step 8:

[0829] The server adjusts the content of the proposed measures and notification method based on the results of the emotion engine. For example, if the user expressed negative emotions about the previous proposed measures, the server reevaluates the proposed measures and considers a different solution. On the other hand, if the user expressed positive emotions, the server maintains the same proposal.

[0830] Step 9:

[0831] The server stores the adjusted countermeasure suggestions in a database, reflects them on the dashboard, and distributes the countermeasure suggestions to relevant users via email and in-app notifications.

[0832] Step 10:

[0833] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0834] Step 11:

[0835] Based on the information obtained from the dashboard, users can conduct on-site surveys and implement specific measures such as installing necessary protective nets and conducting capture activities.

[0836] Step 12:

[0837] Users provide feedback to the system on the results of the measures they have implemented and any new local information. The server collects this feedback and uses it to improve the accuracy of the prediction algorithm and emotion engine. This feedback is used to make more accurate predictions in the future.

[0838] Example 2

[0839] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0840] Conventional pest infestation prediction systems collect individual data or make predictions from a single data source, but lack the ability to comprehensively analyze a wide variety of influencing factors. Furthermore, they often propose countermeasures without taking the user's emotions into consideration, which can result in low user satisfaction. The present invention aims to realize highly accurate predictions of pest infestation risk and effective countermeasure proposals based on the user's emotions.

[0841] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring animal sighting information from each administrative agency or organization, a means for acquiring climate change data from a weather information providing system, and a means for acquiring environmental management and development information from local administrative agencies. This makes it possible to predict the risk of animal sightings with high accuracy and propose appropriate and effective countermeasures based on the user's emotions.

[0842] "Each government agency or organization" refers to public organizations and related non-profit organizations that provide animal sighting information.

[0843] "Animal sighting information" refers to information including the date and time, location (latitude and longitude), type, and damage details of wild animals that have appeared in a specific area.

[0844] "Weather information providing system" refers to a system that provides climate change data such as temperature, precipitation, and air pressure.

[0845] "Climate change data" refers to information about weather conditions such as temperature, rainfall, and air pressure.

[0846] "Local government agencies" refer to local government bodies that provide information on environmental management and urban development in a particular area.

[0847] "Environmental management information" refers to information about activities that affect the natural environment, such as new logging plans or large-scale construction projects.

[0848] "Development information" refers to information about urban planning and construction projects.

[0849] "Data Storage" means a database or other recording means for storing collected data.

[0850] "Linked / mutual analysis" refers to an analytical method that integrates multiple types of data and evaluates their mutual relevance.

[0851] "Animal sighting risk" refers to a risk index that assesses the likelihood of future wild animal sightings in a particular area.

[0852] "Predictive algorithms" refer to mathematical methods and models used to calculate future risk of animal appearances based on collected data.

[0853] "Countermeasure proposals" refer to recommended measures and methods for preventing animal damage based on the prediction results.

[0854] "Relevant users" refers to users who need the collected data and countermeasure proposals, such as farmers and local government officials.

[0855] "Notification" refers to the means of communication used to inform relevant users of proposed measures, such as email or in-app notification.

[0856] An "emotion engine" refers to technology or software that analyzes a user's emotions and makes suggestions or responses accordingly.

[0857] This invention is a system that predicts the risk of animal sightings with high accuracy by linking and mutually analyzing animal sighting information from various government agencies and organizations, climate change data from weather information systems, and environmental management and development information from local government agencies.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose measures that are optimal for the user.

[0858] Data collection

[0859] The server collects animal sighting information from various government agencies and organizations. This information includes the date and time of the animal's appearance, its location (latitude and longitude), its species, and the details of the damage. Specifically, the server periodically obtains this data using a web API and saves it in data storage. For example, the server obtains information such as "Wild boars destroyed fields in a specific area on October 10, 2023."

[0860] Next, the server obtains climate change data from the weather information system. This data includes information such as temperature, precipitation, and air pressure. The server periodically obtains the data using the Japan Meteorological Agency's API and saves it in data storage. For example, the server collects data such as "The maximum temperature in a specific area on October 10, 2023 will be 30°C and the amount of precipitation will be 10mm."

[0861] In addition, the server obtains environmental management and development information from local government agencies. This information includes new logging plans and large-scale construction projects. The server obtains this information using urban planning APIs and stores it in data storage. For example, the server obtains information such as "Plans for the construction of a new highway in a specific area are underway."

[0862] Data linkage and analysis

[0863] The server integrates and analyzes the collected animal sighting information, climate change data, and environmental management and development information. These three types of information are organized chronologically and their correlations are evaluated. The server analyzes how climatic conditions and development plans in a specific area affect the risk of animal sightings. For example, it evaluates correlations such as "rising temperatures in a specific area and new logging plans increase the risk of wild boar sightings."

[0864] Risk prediction and countermeasure proposals

[0865] The server uses a predictive algorithm to score the risk of animal infestation in each area. For example, areas with rising temperatures and ongoing logging plans are assigned a high risk score. The results are stored in data storage and simultaneously visualized on a dashboard.

[0866] The server then generates countermeasure suggestions based on the prediction results and notifies relevant users, for example, recommending the installation of pest netting or strengthening on-site inspections in high-risk areas. Notifications are sent to relevant users via email or in-app notifications.

[0867] Using the Emotion Engine

[0868] The server uses an emotion engine to recognize the user's emotions. For example, if the user rates a proposed measure as "very satisfied," the server evaluates the measure as appropriate. On the other hand, if the user expresses negative emotions, the server reevaluates the proposal and considers alternative measures.

[0869] User feedback and system accuracy improvement

[0870] Users access the dashboard using their devices to check the latest forecast information and proposed countermeasures. For example, risk areas are color-coded on a map, allowing users to identify high-risk areas at a glance. Based on the information provided, users conduct on-site surveys and implement specific countermeasures, such as installing protective nets and conducting capture activities. After that, users provide feedback on the results of their surveys and any new on-site information to the server. The server collects this feedback information and uses it to improve the accuracy of the forecast algorithm and emotion engine.

[0871] Specific examples and input prompts for the generative AI model

[0872] Example: "What are the risks of animal sightings in a specific area during the second week of October and what are the appropriate countermeasures?"

[0873] This system takes into consideration the user's feelings through the above process, preventing animal damage before it occurs and contributing to the sustainable development of agriculture and forestry.

[0874] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0875] Step 1:

[0876] The server obtains animal sighting information from various government agencies and organizations. This information includes the date and time of the animal's appearance, its location (latitude and longitude), its species, and the details of the damage. The server periodically obtains the data using a web API and saves it in data storage. For example, the server obtains information such as "Field damage caused by wild boars in a specific area on October 10, 2023." The input is the data obtained from the API, and the output is the animal sighting information saved in data storage.

[0877] Step 2:

[0878] The server obtains climate change data from the weather information system. Climate change data includes temperature, precipitation, and air pressure, and this data is also obtained periodically using an API and stored in data storage. For example, information such as "the maximum temperature in a specific area on October 10, 2023 will be 30°C and the amount of precipitation will be 10mm" is collected. The input is weather information obtained from the API, and the output is climate change data stored in data storage.

[0879] Step 3:

[0880] The server obtains environmental management and development information from local government agencies. The information obtained includes new logging plans and large-scale construction projects. The server uses urban planning APIs to periodically obtain and store data. For example, it collects information such as "a new highway construction plan is underway in a specific area." The input is the development information obtained from the API, and the output is the environmental management and development information stored in data storage.

[0881] Step 4:

[0882] The server connects collected animal sighting information, climate change data, and environmental management and development information from data storage and analyzes them mutually. The server organizes each piece of information in chronological order and evaluates their correlation. For example, it analyzes whether "rising temperatures in a specific area and new logging plans increase the risk of wild boar sightings." The input is various types of stored data, and the output is the results of mutual analysis.

[0883] Step 5:

[0884] The server uses a predictive algorithm to score the risk of animal infestation in each area. The input is the cross-analyzed data, and the output is a risk score. The server assigns a high risk score to areas with rising temperatures and planned logging, saves the results in data storage, and visualizes them on a dashboard. For example, a specific area may be assessed with a risk score of 70 / 100.

[0885] Step 6:

[0886] The server uses an emotion engine to recognize the user's emotions. For example, if a user rates a proposed measure as "very satisfied," it is assumed that the proposal is appropriate. The input is the user's feedback information, and the output is the analysis result through the emotion engine.

[0887] Step 7:

[0888] The server generates countermeasure proposals based on the prediction results and the emotion engine's analysis results. For example, it may recommend "installing pest nets" or "strengthening on-site inspections" in high-risk areas. The generated countermeasure proposals are sent to relevant users via email or in-app notifications. The input is the risk score and the emotion engine's analysis results, and the output is specific countermeasure proposals.

[0889] Step 8:

[0890] Users access the dashboard from their devices to check the latest forecast information and proposed countermeasures. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance. For example, specific areas are displayed in red. The input is risk and countermeasure proposal data, and the output is visualized risk information.

[0891] Step 9:

[0892] Users implement the proposed measures, such as installing protective nets and carrying out capture activities. They then provide feedback on the results of their actions and any new on-site information to the server. The server collects this information and uses it to improve the accuracy of the prediction algorithm and emotion engine. The input is feedback information from users, and the output is data used to improve the system's accuracy.

[0893] (Application example 2)

[0894] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0895] In modern times, damage caused by pest infestations has a significant impact on agriculture, forest management, and the lives of residents. Furthermore, as climate change and changes in development status affect the behavioral patterns of pests, risk prediction is becoming increasingly complex. Conventional countermeasure systems that use pest infestation information and weather data lack the ability to link these data for analysis, and do not take user emotions or feedback into account, making it difficult to provide optimal countermeasures tailored to real-world situations.

[0896] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring vermin infestation information from local governments and organizations, means for acquiring climate change data from a weather information service, means for acquiring satoyama management and development information from local governments, means for storing the vermin infestation information, climate change data, and development information in a database, means for linking and mutually analyzing the various stored data, means for predicting the risk of vermin infestation, means for generating countermeasure proposals based on the prediction results, means for notifying relevant users of the generated countermeasure proposals, means for recognizing user emotions and customizing the countermeasure proposals based on feedback, and means for providing users with a smartphone app that displays the vermin infestation risk in a color-coded map. This enables improved accuracy in predicting vermin infestation risk and countermeasure proposals, and individual responses based on user feedback.

[0897] "Municipalities" are local public organizations that provide information on pest infestations, Satoyama management, and development information.

[0898] "Organization" refers to an organization other than a local government that has the role of collecting and providing information on pest infestations.

[0899] "Vermin sighting information" is data relating to the date and time of a vermin sighting, its location, type, and the details of the damage.

[0900] "Climate change data" is information about weather fluctuations, such as temperature, rainfall, and air pressure.

[0901] "Satoyama management information" refers to information related to the management of Satoyama, such as logging plans and conservation plans.

[0902] "Development information" is data about local land use and development plans, including new construction projects.

[0903] A "database" is a system for storing various types of information and for linking and mutual analysis.

[0904] "Collaboration and cross-analysis" is the process of combining multiple pieces of information, evaluating their relevance, and deriving comprehensive insights.

[0905] A "predictive algorithm" is a mathematical model for calculating and predicting the risk of pest infestation.

[0906] "Countermeasure proposals" are proposals that provide specific protective measures and guidelines for action based on the risk of pest infestation.

[0907] "Notification means" is a function that notifies the user of the generated countermeasure proposals via email or in-app notification.

[0908] The "emotion engine" is a system that analyzes user feedback and recognizes emotions.

[0909] "Customization" means individually adjusting the content of suggestions and notification methods based on the user's emotions and feedback.

[0910] The "smartphone app" is an application for mobile devices that provides users with information on the risk of pest infestations and suggestions for countermeasures.

[0911] "Color-coded display" is a display method that uses different colors on a map to visually distinguish risk levels.

[0912] This invention provides a system that links and analyzes pest infestation information, climate change data, and development information to predict the risk of pest infestation and propose countermeasures. This system collects and integrates data from various municipalities and organizations, weather information providers, and local governments, and generates countermeasure proposals by combining a predictive algorithm and an emotion engine.

[0913] Data collection

[0914] The server obtains pest sighting information from local governments and organizations via API and stores it in a database. This pest sighting information includes the date and time of sighting, location (latitude and longitude), species, and details of damage. Next, it obtains climate change data from a weather information provider and stores it in the same database. This includes weather information such as temperature, rainfall, and air pressure. Furthermore, it obtains satoyama management and development information, such as logging plans and construction projects, from local governments and stores it in the database.

[0915] Data linkage and analysis

[0916] The server links and organizes the accumulated pest infestation information, climate change data, and development information in chronological order, and evaluates the correlation between each piece of information. This linking and analysis is performed using a Python program and libraries such as Scikit-learn. The server also uses a predictive algorithm to score the pest infestation risk for each area and stores the results in a database.

[0917] Incorporating an emotion engine

[0918] The server incorporates an emotion engine that recognizes the user's emotions when generating countermeasure proposals. This emotion engine analyzes user feedback using, for example, IBM Watson NLP API to recognize emotions. If the user expresses positive or negative emotions toward a countermeasure proposal, the appropriateness of the proposal is evaluated and reflected in the next proposal generation.

[0919] Countermeasure proposals and notifications

[0920] The server generates countermeasure proposals based on the prediction results and the analysis results of the emotion engine. The generated countermeasure proposals include recommendations for rapid pest control activities in emergencies and the installation of protective nets. These countermeasure proposals are notified to relevant users (e.g., hunting license holders and local government officials) via a smartphone app. Notifications are sent via email or in-app push notifications, and the method and content of the notifications are adjusted based on the user's emotions.

[0921] Feedback and improved prediction accuracy

[0922] Users can check the latest forecast information and countermeasure suggestions through the dashboard of the smartphone app. Feedback information provided by users is sent to the server and used to improve the accuracy of the prediction algorithm and emotion engine. This allows the system to evolve over time and make more appropriate countermeasure suggestions.

[0923] Implementation example

[0924] For example, if a region experiences rising temperatures and new development projects at the same time, the system can predict an increased risk of pest infestations and send notifications to users in the area recommending the installation of protective netting. If users provide positive feedback on the suggestions, the system will consider the suggestions effective and will make similar suggestions in future similar situations.

[0925] Prompt Sentence Examples

[0926] markdown

[0927] Invention Contents

[0928] Develop a new application that combines the emotion engine of a system that links and cross-analyzes pest infestation information, climate change data, and development information to predict the risk of pest infestations. As an example, we are looking for ideas for a security app for smartphones that suggests personalized measures based on the user's emotions, allowing residents to respond appropriately.

[0929] input

[0930] The system collects and analyzes information on pest infestations, climate change data, and development information from local governments, organizations, and weather information providers, and predicts the risk of pest infestations.

[0931] Uses an emotion engine to customize countermeasure suggestions based on user feedback.

[0932] Example output

[0933] 1. A smartphone app that displays the risk of pest infestations in a color-coded manner on a map.

[0934] 2. Notify and display customized countermeasure suggestions based on the user's emotions.

[0935] 3. It has a dashboard function that allows you to understand risk areas at a glance.

[0936] 4. Specific implementation methods include clearly indicating the APIs and libraries to be used (e.g., Google Maps API, Scikit-learn, IBM Watson NLP API).

[0937] It is expected that the system for implementing this invention will effectively prevent damage caused by pests and contribute to the sustainability of agricultural and forest management.

[0938] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0939] System program processing steps

[0940] Step 1:

[0941] Information on pest sightings is obtained from each local government or organization. The server accesses the API of the local government or organization to obtain the date and time of sighting, location (latitude and longitude), type, and details of damage. The obtained data is stored in the server's database. The input is pest sighting information from the API, and the output is the information stored in the database.

[0942] Step 2:

[0943] Climate change data is obtained from a weather information service. The server uses the weather information service's API to obtain weather information such as temperature, precipitation, and air pressure. The obtained data is stored in a database. The input is climate change data from the API, and the output is the information stored in the database.

[0944] Step 3:

[0945] The server obtains Satoyama management and development information from local governments. Using the local government's API, the server obtains information such as new logging plans and construction projects. The obtained data is stored in a database. The input is Satoyama management and development information from the API, and the output is the information stored in the database.

[0946] Step 4:

[0947] The various types of stored data are linked and cross-analyzed. The server uses a Python program to link and cross-analyze the pest infestation information, climate change data, and development information stored in the database. This is the process of organizing data in chronological order and evaluating the relevance between each piece of information. The input is the information stored in the database, and the output is the results of the linkage and cross-analysis.

[0948] Step 5:

[0949] Predict the risk of pest infestation. The server uses the Scikit-learn library to apply a prediction algorithm and score the risk of pest infestation for each area. The input is the results of collaboration and cross-analysis, and the output is the risk score for each area.

[0950] Step 6:

[0951] Generate countermeasure proposals. Based on the prediction results, the server generates specific countermeasure proposals such as pest control activities and the installation of protective nets. The input is the risk score, and the output is the countermeasure proposals.

[0952] Step 7:

[0953] The emotion engine is used to customize countermeasure suggestions. The server uses IBM Watson NLP API to analyze user feedback and recognize the user's emotions. Based on the results, countermeasure suggestions are adjusted and customized. The input is user feedback, and the output is customized countermeasure suggestions.

[0954] Step 8:

[0955] The generated countermeasure proposals are notified to the user. The server notifies the user of the countermeasure proposals via a smartphone app via push notification or email. The input is the customized countermeasure proposals, and the output is a notification to the user.

[0956] Step 9:

[0957] Receives user feedback and uses it to improve the accuracy of the system. Users send feedback on proposed measures via a smartphone app. The server receives this feedback and uses it to improve the accuracy of the prediction algorithm and emotion engine. The input is user feedback, and the output is an improved prediction algorithm and emotion engine.

[0958] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0959] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0960] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0961] [Fourth embodiment]

[0962] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0963] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0964] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0965] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0966] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0967] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0968] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0969] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0970] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0971] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0972] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0973] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0974] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0975] This invention is a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on that. This system is composed of a server, terminals, and users.

[0976] Data collection

[0977] First, the server obtains information on pest infestations from each local government or organization. This information includes the date and time of the infestation, location (latitude and longitude), species, and damage details. The server periodically obtains this data using a web API and stores it in a database.

[0978] Next, the server retrieves climate change data from a weather information service. This data includes information about climate changes, such as temperature, precipitation, and air pressure. The server periodically retrieves the data using the weather information API and stores it in a database.

[0979] Additionally, the server obtains Satoyama management and development information from local governments, including new logging plans and large-scale construction projects, using a web API to retrieve this information and store it in a database.

[0980] Data linkage and analysis

[0981] The server connects and cross-analyzes pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information chronologically and evaluates its relevance. The server analyzes how climatic conditions and development plans in a specific area affect the risk of pest infestation.

[0982] The server then uses a predictive algorithm to score each region's risk of pest infestation. For example, if a region experiences both rising temperatures and new logging projects, it predicts that the region's risk of infestation will increase. The server stores these predictions in a database and visualizes them on a dashboard.

[0983] Countermeasure proposals

[0984] The server generates countermeasure proposals based on the prediction results, such as recommending the installation of pest netting and strengthening of on-site inspections in high-risk areas, or prompt implementation of pest control activities in emergencies.

[0985] The generated countermeasure proposals are notified to relevant users (e.g., hunting license holders or local government officials). The server does this using email and in-app notifications.

[0986] User notification and countermeasure implementation

[0987] Users can access the dashboard using their devices (PC or smartphone) to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[0988] Based on the provided information, users conduct on-site surveys and implement specific countermeasures such as installing protective nets and capturing the animals. Users also provide feedback to the system on the results of the measures they have implemented and on new on-site information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm.

[0989] Through the above processes, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[0990] The processing flow will be explained below.

[0991] Step 1:

[0992] The server runs a daily script and sends requests to each local government's Web API endpoint. The server obtains pest sighting information (date and time of sighting, location, species, and damage details), converts the obtained data into an appropriate format, and saves it in a database.

[0993] Step 2:

[0994] The server calls the API of a weather service to obtain climate change data such as temperature, precipitation, and air pressure. The server stores this climate data in a database. The weather data often includes daily, weekly, and monthly statistics.

[0995] Step 3:

[0996] The server periodically accesses the local government's Web API endpoint to obtain information on Satoyama management and mountain area development (new logging plans, large-scale construction projects, etc.), and stores this development information in a database.

[0997] Step 4:

[0998] The server extracts pest infestation information, climate change data, and development information from the database, executes queries to combine these data, organizes the combined data in chronological order, and evaluates the relevance of each piece of information.

[0999] Step 5:

[1000] The server applies predictive algorithms to analyze the combined data retrieved from the database. The server scores each region's pest infestation risk and generates a pest infestation forecast. For example, it predicts that areas experiencing a combination of rising temperatures and drought will be at increased risk of pest infestation.

[1001] Step 6:

[1002] Based on the prediction results, the server generates specific countermeasure proposals for high-risk areas, such as "Due to the increasing risk of overhunting in this mountainous area over the next three months, we recommend that the hunting association strengthen patrols."

[1003] Step 7:

[1004] The server stores the generated countermeasure proposals in a database, reflects them on the dashboard, and delivers them to relevant users via email and in-app notifications.

[1005] Step 8:

[1006] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[1007] Step 9:

[1008] Based on the information obtained from the dashboard, users can conduct on-site surveys and implement specific measures such as installing necessary protective nets and conducting capture activities.

[1009] Step 10:

[1010] Users provide feedback to the system on the results of the measures they have implemented and new local information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm. This feedback is used to make more accurate predictions in the future.

[1011] Example 1

[1012] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1013] In recent years, damage caused by pests in agriculture and forestry has become increasingly serious. It is particularly important to accurately predict the impact of climate change and development activities on pest behavior and to implement appropriate countermeasures. However, conventional methods have difficulty comprehensively managing and analyzing such complex factors. Therefore, a system that can effectively predict the risk of pest infestation and propose appropriate countermeasures is needed.

[1014] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1015] In this invention, the server includes: means for acquiring pest infestation information from local governments and organizations; means for acquiring climate change data from weather information services; means for acquiring satoyama management and development information from local governments; means for storing the pest infestation information, climate change data, and development information in a database; means for linking and cross-analyzing the various stored data; means for predicting the risk of pest infestation; means for generating countermeasure proposals based on the prediction results; means for notifying related users of the generated countermeasure proposals; means for displaying the prediction information and countermeasure proposals via a user interface; and means for receiving feedback information from related users and using it to improve the accuracy of the prediction algorithm. This enables a comprehensive analysis of the impact of climate change and development activities on pest behavior, enabling accurate risk predictions and appropriate countermeasure proposals.

[1016] "Vermin sighting information" is information reported by local governments and organizations regarding the location and date of appearance of vermin, the extent of damage, etc.

[1017] "Climate change data" refers to data on meteorological elements such as temperature, precipitation, and air pressure, and is obtained from weather information services.

[1018] "Development information" refers to information provided by local governments on satoyama management, new logging plans, large-scale construction projects, and so on.

[1019] A "database" is a system for storing various acquired information and for searching and analyzing it as needed.

[1020] "Linkage and cross-analysis" is the act of integrating multiple data sets and analyzing their interrelationships.

[1021] "Pest infestation risk" is an indicator that shows the possibility of pests appearing in a particular area.

[1022] "Countermeasure proposals" are specific actions or measures recommended to reduce the risk of pest infestation.

[1023] "Users" are relevant individuals or organizations, such as local government officials and hunting license holders, who use this system.

[1024] A "generative AI model" is an artificial intelligence algorithm used in data analysis to generate predictions.

[1025] "Feedback information" refers to the results of measures taken by the user and newly obtained local information, and is fed back to the system.

[1026] This invention provides a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on the results. This system is composed of a server, terminals, and users.

[1027] Data collection

[1028] The server first obtains pest sighting information from each local government or organization. This information includes the date and time of the pest sighting, location (latitude and longitude), type, and details of the damage. The server periodically obtains this data using a web API and stores it in a database. As a specific example, the server obtains data from the API "https: / / example.com / api / animal_sightings" and stores it in the "Sighting_Info" table in the database.

[1029] The server then retrieves climate change data from a weather information service, including temperature, precipitation, and air pressure. The server accesses https: / / weatherapi.com / data and stores the data in a database table called Climate_Data.

[1030] Additionally, the server retrieves Satoyama management and development information from local governments, including new logging plans and large-scale construction projects. The server accesses the API "https: / / municipality.net / dev_info" and stores the data in the "Development_Info" table in the database.

[1031] Data linkage and analysis

[1032] The server performs cross-analysis based on the pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information in chronological order and uses Python libraries such as Pandas and Scikit-learn to evaluate the relevance.

[1033] For example, the server combines data sets to analyze how rising temperatures and new logging plans in a particular region affect the risk of pest infestations. It uses predictive algorithms like random forests and neural networks to score each region's risk. The results are stored in a database and visualized on a dashboard.

[1034] Countermeasure proposals

[1035] The server generates countermeasure proposals based on the prediction results. For example, it may recommend installing pest nets or strengthening on-site inspections in high-risk areas. It may also call for prompt pest control activities in emergencies. The generated countermeasure proposals are notified to relevant users. The server distributes the information via email or in-app notifications.

[1036] User notification and countermeasure implementation

[1037] Users access the dashboard using their devices (PC or smartphone) to check the forecast information and proposed countermeasures. The dashboard displays risk areas in different colors on a map, allowing users to identify high-risk areas at a glance. Users conduct on-site surveys based on the information provided and implement specific countermeasures such as installing protective nets and conducting capture activities. Users also provide feedback to the system on the results of the implemented countermeasures and any new on-site information. The server collects this feedback information and uses it to improve the accuracy of the forecast algorithm.

[1038] Prompt Sentence Examples

[1039] Use the system to predict the risk of pest infestation in a specific area and generate countermeasure proposals. For example, evaluate the risk in an area where the temperature is over 30 degrees, there is little rainfall, and there are plans for new logging, and propose appropriate countermeasures.

[1040] As a result of the above, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[1041] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1042] Step 1: Data collection

[1043] First, the server obtains information on pest sightings from each local government or organization. This information includes the date and time of sighting, location (latitude and longitude), species, and details of the damage. The input is raw data obtained from the web API, and the output is formatted information stored in the "Sighting_Info" table in the database. Specifically, the server accesses "https: / / example.com / api / animal_sightings," parses the obtained JSON data, and stores it in the database.

[1044] Next, the server retrieves climate change data from the weather information service. The input is weather data retrieved from the API, and the output is stored in the "Climate_Data" table in the database. Specifically, the server accesses "https: / / weatherapi.com / data" and periodically retrieves information such as temperature, precipitation, and air pressure, and stores it in the database.

[1045] Furthermore, the server obtains Satoyama management and development information from local governments. The input is the development information provided, and the output is stored in the "Development_Info" table of the database. Specifically, the server accesses "https: / / municipality.net / dev_info" to obtain information on new logging plans and large-scale construction projects and stores it in the database.

[1046] Step 2: Data integration

[1047] The server performs data integration processing of pest infestation information, climate change data, and development information stored in the database. It uses information from the "Sighting_Info," "Climate_Data," and "Development_Info" tables as input and generates an integrated dataset as output. Specifically, the server uses Python Pandas and SQL queries to organize each dataset in chronological order and combine related data.

[1048] Step 3: Data analysis

[1049] The server performs analysis using the federated dataset. It has the integrated dataset as input and a pest infestation risk score for each region as output. Specifically, the server uses generative AI models to analyze how specific weather conditions or development plans affect pest infestation risk. For example, it uses random forests or neural networks to score the risk for each region.

[1050] Step 4: Generate countermeasure proposals

[1051] The server generates countermeasure proposals based on the analysis results. The input is the pest infestation risk score, and the output is specific countermeasure proposals. Specific operations include the installation of pest nets in high-risk areas and strengthening on-site inspections, and the proposals are saved in a database.

[1052] Step 5: Notify users

[1053] The server notifies the relevant users of the generated countermeasure proposals. The input is each generated countermeasure proposal, and the output is a notification to the user. Specific operations include delivering information via email or in-app notifications.

[1054] Step 6: User confirmation and implementation of measures

[1055] Users access the dashboard using their devices (PC or smartphone) to check the forecast information and proposed countermeasures. Notifications are received from the server as input, and the user implements countermeasures as output. Specifically, risk areas are displayed in different colors on the dashboard, and the user takes action based on the proposed countermeasures.

[1056] Step 7: Gather feedback

[1057] Users provide feedback to the server on the results of the measures they have implemented and any new local information. The input is feedback information from users, and the output is improvements to the accuracy of the prediction algorithm. Specifically, the server receives the feedback information, stores it in a database, and uses it to retrain the prediction algorithm.

[1058] Through these processing steps, this system can prevent damage caused by pests and contribute to the sustainable development of agriculture and forestry.

[1059] (Application example 1)

[1060] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1061] Damage to crops and property caused by pest infestations is a serious problem, and effective prevention requires accurate prediction of pest infestation risk and prompt implementation of countermeasures. However, existing systems lack the ability to link and utilize pest infestation information, climate change data, and development information, and are lacking in mechanisms for displaying risks in real time and continuously incorporating user feedback. As a result, countermeasures to prevent pest damage are often delayed. The present invention aims to solve these issues and provide more effective prediction of pest infestation risk and proposal of countermeasures.

[1062] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1063] In this invention, the server includes means for acquiring pest infestation information from local governments and organizations, means for acquiring climate change data from weather information services, means for acquiring satoyama management and development information from local governments, means for storing the pest infestation information, climate change data, and development information in a database, means for linking and cross-analyzing the various stored data, means for predicting the risk of pest infestation, means for generating countermeasure proposals based on the prediction results, means for notifying relevant users of the generated countermeasure proposals, means for displaying the infestation risk in color-coded on a map for users in real time, and means for reporting the effectiveness of countermeasures implemented by users and new local information. This makes it possible to quickly and accurately predict the risk of pest infestation, enabling rapid response and effective damage prevention measures to be implemented.

[1064] "Means for obtaining information on pest infestations from local governments and organizations" refers to methods and devices for collecting information on pest infestations from local governments and related organizations.

[1065] The "means for acquiring climate change data from a weather information service" refers to a method or device for acquiring data related to climate change, such as temperature, precipitation, and air pressure, from a weather information service.

[1066] "Means for obtaining information on Satoyama management and development from local governments" refers to methods and devices for collecting information on Satoyama management and development plans from local governments.

[1067] "Means for storing the pest infestation information, climate change data, and development information in a database" refers to a method or device for storing the collected pest infestation information, climate change data, and development information in a database that centrally manages the information.

[1068] "Means for linking and mutually analyzing various types of stored data" refers to methods and devices for linking information stored in a database and evaluating and analyzing the mutual relationships.

[1069] A "means for predicting the risk of pest infestation" is a method or device for predicting the risk of pest infestation in a specific area based on collected and analyzed data.

[1070] The "means for generating countermeasure proposals based on prediction results" refers to a method or device for proposing appropriate protective measures or countermeasures based on the prediction results of the risk of pest infestation.

[1071] "Means for notifying relevant users of the generated countermeasure proposals" refers to a method or device for notifying relevant users (e.g., hunting license holders or local government officials) of the generated countermeasure proposals.

[1072] "Means for displaying appearance risks in different colors on a map in real time to the user" refers to a method or device for displaying appearance risks in different colors on a map in real time so that the user can grasp the risks at a glance.

[1073] The "means by which the user can report the effects of measures taken and new local information" refers to a method or device for the user to report the effects of measures taken and newly collected local information to the system.

[1074] This invention is a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts specific pest infestation risks, and proposes countermeasures based on the predictions. This system is composed of a server, terminals, and users.

[1075] Data collection

[1076] The server obtains pest sighting information from each local government or organization. Specifically, it periodically obtains pest sighting information (date and time of sighting, location, type, and damage details) using APIs provided by the local government or organization and stores it in a database.

[1077] The server also uses an API to obtain climate change data (temperature, precipitation, air pressure, etc.) from a weather information service, periodically obtains this data, and stores it in a database.

[1078] In addition, the server obtains Satoyama management and development information (such as new logging plans and construction projects) from local governments and stores this information in a database as well.

[1079] Data linkage and analysis

[1080] The server links and analyzes the information stored in the database, including pest infestation information, climate change data, and development information. Specifically, it organizes this information in chronological order and evaluates its relevance.

[1081] The server then uses a predictive algorithm to score each area's risk of pest infestation. For example, an area experiencing both rising temperatures and new logging projects could be predicted to be at increased risk of pest infestation. The risk scores are stored in a database and visualized on a map.

[1082] Real-time map display

[1083] The application installed on the user's device (smartphone or PC) retrieves the latest risk score from the server and displays risk areas in color on a map in real time, allowing the user to see high-risk areas at a glance.

[1084] Countermeasure proposals and user notifications

[1085] Furthermore, the server generates countermeasure suggestions based on the prediction results and notifies the user via in-app notifications or emails. The countermeasure suggestions include installing protective nets, strengthening on-site inspections, and promptly implementing pest control activities in case of emergency.

[1086] Feedback and improved prediction accuracy

[1087] Users can report the effectiveness of the measures they have implemented and new local information they have acquired within the system. The server receives this feedback information and uses it to improve the accuracy of the prediction algorithm.

[1088] Examples and prompts

[1089] For example, if there are plans for new logging in a certain area of ​​Akita Prefecture and temperatures have recently risen, the system will determine that the area is a high-risk area and notify the local government to "strengthen on-site inspections and install pest nets."

[1090] Example prompt sentence:

[1091] You are given datasets from [ANIMAL_API], [WEATHER_API], and [DEV_API] containing wildlife occurrence, weather changes, and development plans. Use these datasets to create a script to assess and visualize wildlife risk areas. Generate countermeasures if the risk score exceeds a certain threshold.

[1092] The system aims to prevent damage caused by pests and promote the sustainable development of agriculture and forestry.

[1093] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1094] Step 1: Data collection

[1095] The server obtains information on pest sightings from local governments and organizations. The data obtained includes the date and time of sighting, location (latitude and longitude), species, and details of damage. Here, information is collected periodically using an API and stored in a database. Similarly, climate change data (temperature, rainfall, air pressure, etc.) is obtained from a weather information provider via API and stored in the database. In addition, information on satoyama management and development (new logging plans, large-scale construction projects, etc.) is collected from local governments and this is also stored in the database. In this way, pest sighting information, climate change data, and development information are accumulated in the database.

[1096] Step 2: Data integration and analysis

[1097] The server chronologically organizes the pest infestation information, climate change data, and development information stored in the database and evaluates their correlations. To do this, it uses an algorithm that links each data set and analyzes correlations. For example, it analyzes patterns such as an increase in the risk of infestation in an area when a rise in temperature occurs simultaneously with a new logging plan. It uses pest infestation information, climate data, and development information as input data and generates a risk score as output.

[1098] Step 3: Risk prediction

[1099] The server uses a predictive algorithm based on the linked and analyzed data to calculate the risk of pest infestation for each region. A risk score is set for each region and saved in the database. For example, an area where rising temperatures coincide with planned logging will be scored as high risk. The input data is the results of the linkage and analysis, and the output is a risk score.

[1100] Step 4: Visualization

[1101] The device (smartphone or PC) retrieves the latest risk score from the server and displays the risk area on a map in real time, color-coding it. This visualized information helps users understand high-risk areas at a glance. The input data is the risk score, and the output is a color-coded map.

[1102] Step 5: Propose a solution

[1103] The server generates appropriate countermeasure proposals based on the prediction results. For example, it recommends installing protective nets, strengthening on-site inspections, and promptly exterminating pests in emergencies. The generated countermeasure proposals are stored in a database and sent to relevant users (e.g., hunting license holders and local government officials) via in-app notifications or email. The input data is a risk score, and the output is a countermeasure proposal.

[1104] Step 6: Notify users and implement countermeasures

[1105] Users access the dashboard using their devices to check the latest risk information and proposed countermeasures. By viewing risk areas color-coded on a map, they can quickly implement necessary countermeasures, such as conducting on-site surveys or installing protective nets. The input data is countermeasure proposals, and the output is countermeasure implementation.

[1106] Step 7: Gather feedback and improve the algorithm

[1107] Users report the effectiveness of the measures they have implemented and new local information within the application. The server incorporates this feedback information into a database and uses it to improve the accuracy of the prediction algorithm. The input data is user feedback, and the output is an improved prediction algorithm and its results.

[1108] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1109] This invention combines a system that links and mutually analyzes pest infestation information, climate change data, and development information, predicts the risk of pest infestation, and proposes countermeasures based on that, with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[1110] Data collection

[1111] First, the server obtains information on pest infestations from each local government or organization. This information includes the date and time of the infestation, location (latitude and longitude), species, and damage details. The server periodically obtains this data using a web API and stores it in a database.

[1112] Next, the server retrieves climate change data from a weather information service. This data includes information about climate changes, such as temperature, precipitation, and air pressure. The server periodically retrieves the data using the weather information API and stores it in a database.

[1113] Additionally, the server obtains Satoyama management and development information from local governments, including new logging plans and large-scale construction projects, using a web API to retrieve this information and store it in a database.

[1114] Data linkage and analysis

[1115] The server connects and cross-analyzes pest infestation information, climate change data, and development information stored in the database. Specifically, it organizes this information in chronological order and evaluates the correlation between each piece of information. The server analyzes how climatic conditions and development plans in a specific area affect the risk of pest infestation.

[1116] The server then uses a predictive algorithm to score each region's risk of pest infestation. For example, if a region experiences both rising temperatures and new logging projects, it predicts that the region's risk of infestation will increase. The server stores these predictions in a database and visualizes them on a dashboard.

[1117] Incorporating an emotion engine

[1118] The server incorporates an emotion engine that recognizes the user's emotions when generating the countermeasure proposal. The emotion engine can recognize the user's emotions by analyzing the user's reactions and feedback when receiving the countermeasure proposal.

[1119] For example, if the user expresses positive feelings toward the proposed measure, the server will evaluate the suggestion as appropriate and use this as a reference for future similar suggestions. On the other hand, if the user expresses negative feelings, the server will reevaluate the suggestion and consider alternative measures that are more suitable for the user.

[1120] Countermeasure proposals and notifications

[1121] The server generates countermeasure proposals based on the prediction results and the analysis results of the emotion engine. For example, it may recommend the installation of pest netting and strengthening of on-site inspections in high-risk areas. In an emergency, it may also request the prompt implementation of pest control activities.

[1122] The generated action proposals are notified to relevant users (e.g., hunting license holders or local government officials). The server does this using email or in-app notifications. The emotion engine can adjust the notification method and content based on the user's emotions.

[1123] User notification and countermeasure implementation

[1124] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[1125] Based on the information provided, users conduct on-site surveys and implement specific countermeasures, such as installing protective nets and capturing the animals. Users also provide feedback to the system on the results of the measures they have implemented and on new on-site information. The server collects this feedback information and uses it to improve the accuracy of the prediction algorithm and emotion engine.

[1126] Through the above process, this system takes into consideration the user's feelings, prevents damage caused by pests, and contributes to the sustainable development of agriculture and forestry.

[1127] The processing flow will be explained below.

[1128] Step 1:

[1129] The server runs a daily script and sends requests to each local government's Web API endpoint. The server obtains pest sighting information (sighting date and time, location, species, and damage details), converts the format as needed, and saves it in a database.

[1130] Step 2:

[1131] The server calls the API of a weather service to obtain climate change data such as temperature, precipitation, and air pressure. The server stores this climate data in a database. The weather data often includes daily, weekly, and monthly statistics.

[1132] Step 3:

[1133] The server periodically accesses the local government's Web API endpoint to obtain information on Satoyama management and mountain area development (new logging plans, large-scale construction projects, etc.), and stores this development information in a database.

[1134] Step 4:

[1135] The server extracts pest infestation information, climate change data, and development information from the database, executes queries to combine these data, organizes the combined data in chronological order, and evaluates the relevance of each piece of information.

[1136] Step 5:

[1137] The server applies predictive algorithms to analyze the combined data retrieved from the database. The server scores each region's pest infestation risk and generates a pest infestation forecast. For example, it predicts that areas experiencing a combination of rising temperatures and drought will be at increased risk of pest infestation.

[1138] Step 6:

[1139] Based on the prediction results, the server generates specific countermeasure proposals for high-risk areas, such as "Due to the increasing risk of overhunting in this mountainous area over the next three months, we recommend that the hunting association strengthen patrols."

[1140] Step 7:

[1141] Before notifying the user of the proposed measures, the server adds a means to recognize the user's emotions using an emotion engine. The emotion engine analyzes the user's emotions based on the user's past feedback and reaction data.

[1142] Step 8:

[1143] The server adjusts the content of the proposed measures and notification method based on the results of the emotion engine. For example, if the user expressed negative emotions about the previous proposed measures, the server reevaluates the proposed measures and considers a different solution. On the other hand, if the user expressed positive emotions, the server maintains the same proposal.

[1144] Step 9:

[1145] The server stores the adjusted countermeasure suggestions in a database, reflects them on the dashboard, and distributes the countermeasure suggestions to relevant users via email and in-app notifications.

[1146] Step 10:

[1147] Users can access the dashboard from their devices to check the latest forecast information and countermeasure proposals. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance.

[1148] Step 11:

[1149] Based on the information obtained from the dashboard, users can conduct on-site surveys and implement specific measures such as installing necessary protective nets and conducting capture activities.

[1150] Step 12:

[1151] Users provide feedback to the system on the results of the measures they have implemented and any new local information. The server collects this feedback and uses it to improve the accuracy of the prediction algorithm and emotion engine. This feedback is used to make more accurate predictions in the future.

[1152] Example 2

[1153] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1154] Conventional pest infestation prediction systems collect individual data or make predictions from a single data source, but lack the ability to comprehensively analyze a wide variety of influencing factors. Furthermore, they often propose countermeasures without taking the user's emotions into consideration, which can result in low user satisfaction. The present invention aims to realize highly accurate predictions of pest infestation risk and effective countermeasure proposals based on the user's emotions.

[1155] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring animal sighting information from each administrative agency or organization, a means for acquiring climate change data from a weather information providing system, and a means for acquiring environmental management and development information from local administrative agencies. This makes it possible to predict the risk of animal sightings with high accuracy and propose appropriate and effective countermeasures based on the user's emotions.

[1156] "Each government agency or organization" refers to public organizations and related non-profit organizations that provide animal sighting information.

[1157] "Animal sighting information" refers to information including the date and time, location (latitude and longitude), type, and damage details of wild animals that have appeared in a specific area.

[1158] "Weather information providing system" refers to a system that provides climate change data such as temperature, precipitation, and air pressure.

[1159] "Climate change data" refers to information about weather conditions such as temperature, rainfall, and air pressure.

[1160] "Local government agencies" refer to local government bodies that provide information on environmental management and urban development in a particular area.

[1161] "Environmental management information" refers to information about activities that affect the natural environment, such as new logging plans or large-scale construction projects.

[1162] "Development information" refers to information about urban planning and construction projects.

[1163] "Data Storage" means a database or other recording means for storing collected data.

[1164] "Linked / mutual analysis" refers to an analytical method that integrates multiple types of data and evaluates their mutual relevance.

[1165] "Animal sighting risk" refers to a risk index that assesses the likelihood of future wild animal sightings in a particular area.

[1166] "Predictive algorithms" refer to mathematical methods and models used to calculate future risk of animal appearances based on collected data.

[1167] "Countermeasure proposals" refer to recommended measures and methods for preventing animal damage based on the prediction results.

[1168] "Relevant users" refers to users who need the collected data and countermeasure proposals, such as farmers and local government officials.

[1169] "Notification" refers to the means of communication used to inform relevant users of proposed measures, such as email or in-app notification.

[1170] An "emotion engine" refers to technology or software that analyzes a user's emotions and makes suggestions or responses accordingly.

[1171] This invention is a system that predicts the risk of animal sightings with high accuracy by linking and mutually analyzing animal sighting information from various government agencies and organizations, climate change data from weather information systems, and environmental management and development information from local government agencies.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose measures that are optimal for the user.

[1172] Data collection

[1173] The server collects animal sighting information from various government agencies and organizations. This information includes the date and time of the animal's appearance, its location (latitude and longitude), its species, and the details of the damage. Specifically, the server periodically obtains this data using a web API and saves it in data storage. For example, the server obtains information such as "Wild boars destroyed fields in a specific area on October 10, 2023."

[1174] Next, the server obtains climate change data from the weather information system. This data includes information such as temperature, precipitation, and air pressure. The server periodically obtains the data using the Japan Meteorological Agency's API and saves it in data storage. For example, the server collects data such as "The maximum temperature in a specific area on October 10, 2023 will be 30°C and the amount of precipitation will be 10mm."

[1175] In addition, the server obtains environmental management and development information from local government agencies. This information includes new logging plans and large-scale construction projects. The server obtains this information using urban planning APIs and stores it in data storage. For example, the server obtains information such as "Plans for the construction of a new highway in a specific area are underway."

[1176] Data linkage and analysis

[1177] The server integrates and analyzes the collected animal sighting information, climate change data, and environmental management and development information. These three types of information are organized chronologically and their correlations are evaluated. The server analyzes how climatic conditions and development plans in a specific area affect the risk of animal sightings. For example, it evaluates correlations such as "rising temperatures in a specific area and new logging plans increase the risk of wild boar sightings."

[1178] Risk prediction and countermeasure proposals

[1179] The server uses a predictive algorithm to score the risk of animal infestation in each area. For example, areas with rising temperatures and ongoing logging plans are assigned a high risk score. The results are stored in data storage and simultaneously visualized on a dashboard.

[1180] The server then generates countermeasure suggestions based on the prediction results and notifies relevant users, for example, recommending the installation of pest netting or strengthening on-site inspections in high-risk areas. Notifications are sent to relevant users via email or in-app notifications.

[1181] Using the Emotion Engine

[1182] The server uses an emotion engine to recognize the user's emotions. For example, if the user rates a proposed measure as "very satisfied," the server evaluates the measure as appropriate. On the other hand, if the user expresses negative emotions, the server reevaluates the proposal and considers alternative measures.

[1183] User feedback and system accuracy improvement

[1184] Users access the dashboard using their devices to check the latest forecast information and proposed countermeasures. For example, risk areas are color-coded on a map, allowing users to identify high-risk areas at a glance. Based on the information provided, users conduct on-site surveys and implement specific countermeasures, such as installing protective nets and conducting capture activities. After that, users provide feedback on the results of their surveys and any new on-site information to the server. The server collects this feedback information and uses it to improve the accuracy of the forecast algorithm and emotion engine.

[1185] Specific examples and input prompts for the generative AI model

[1186] Example: "What are the risks of animal sightings in a specific area during the second week of October and what are the appropriate countermeasures?"

[1187] This system takes into consideration the user's feelings through the above process, preventing animal damage before it occurs and contributing to the sustainable development of agriculture and forestry.

[1188] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1189] Step 1:

[1190] The server obtains animal sighting information from various government agencies and organizations. This information includes the date and time of the animal's appearance, its location (latitude and longitude), its species, and the details of the damage. The server periodically obtains the data using a web API and saves it in data storage. For example, the server obtains information such as "Field damage caused by wild boars in a specific area on October 10, 2023." The input is the data obtained from the API, and the output is the animal sighting information saved in data storage.

[1191] Step 2:

[1192] The server obtains climate change data from the weather information system. Climate change data includes temperature, precipitation, and air pressure, and this data is also obtained periodically using an API and stored in data storage. For example, information such as "the maximum temperature in a specific area on October 10, 2023 will be 30°C and the amount of precipitation will be 10mm" is collected. The input is weather information obtained from the API, and the output is climate change data stored in data storage.

[1193] Step 3:

[1194] The server obtains environmental management and development information from local government agencies. The information obtained includes new logging plans and large-scale construction projects. The server uses urban planning APIs to periodically obtain and store data. For example, it collects information such as "a new highway construction plan is underway in a specific area." The input is the development information obtained from the API, and the output is the environmental management and development information stored in data storage.

[1195] Step 4:

[1196] The server connects collected animal sighting information, climate change data, and environmental management and development information from data storage and analyzes them mutually. The server organizes each piece of information in chronological order and evaluates their correlation. For example, it analyzes whether "rising temperatures in a specific area and new logging plans increase the risk of wild boar sightings." The input is various types of stored data, and the output is the results of mutual analysis.

[1197] Step 5:

[1198] The server uses a predictive algorithm to score the risk of animal infestation in each area. The input is the cross-analyzed data, and the output is a risk score. The server assigns a high risk score to areas with rising temperatures and planned logging, saves the results in data storage, and visualizes them on a dashboard. For example, a specific area may be assessed with a risk score of 70 / 100.

[1199] Step 6:

[1200] The server uses an emotion engine to recognize the user's emotions. For example, if a user rates a proposed measure as "very satisfied," it is assumed that the proposal is appropriate. The input is the user's feedback information, and the output is the analysis result through the emotion engine.

[1201] Step 7:

[1202] The server generates countermeasure proposals based on the prediction results and the emotion engine's analysis results. For example, it may recommend "installing pest nets" or "strengthening on-site inspections" in high-risk areas. The generated countermeasure proposals are sent to relevant users via email or in-app notifications. The input is the risk score and the emotion engine's analysis results, and the output is specific countermeasure proposals.

[1203] Step 8:

[1204] Users access the dashboard from their devices to check the latest forecast information and proposed countermeasures. The dashboard displays risk areas on a map in different colors, allowing users to identify high-risk areas at a glance. For example, specific areas are displayed in red. The input is risk and countermeasure proposal data, and the output is visualized risk information.

[1205] Step 9:

[1206] Users implement the proposed measures, such as installing protective nets and carrying out capture activities. They then provide feedback on the results of their actions and any new on-site information to the server. The server collects this information and uses it to improve the accuracy of the prediction algorithm and emotion engine. The input is feedback information from users, and the output is data used to improve the system's accuracy.

[1207] (Application example 2)

[1208] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1209] In modern times, damage caused by pest infestations has a significant impact on agriculture, forest management, and the lives of residents. Furthermore, as climate change and changes in development status affect the behavioral patterns of pests, risk prediction is becoming increasingly complex. Conventional countermeasure systems that use pest infestation information and weather data lack the ability to link these data for analysis, and do not take user emotions or feedback into account, making it difficult to provide optimal countermeasures tailored to real-world situations.

[1210] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring vermin infestation information from local governments and organizations, means for acquiring climate change data from a weather information service, means for acquiring satoyama management and development information from local governments, means for storing the vermin infestation information, climate change data, and development information in a database, means for linking and mutually analyzing the various stored data, means for predicting the risk of vermin infestation, means for generating countermeasure proposals based on the prediction results, means for notifying relevant users of the generated countermeasure proposals, means for recognizing user emotions and customizing the countermeasure proposals based on feedback, and means for providing users with a smartphone app that displays the vermin infestation risk in a color-coded map. This enables improved accuracy in predicting vermin infestation risk and countermeasure proposals, and individual responses based on user feedback.

[1211] "Municipalities" are local public organizations that provide information on pest infestations, Satoyama management, and development information.

[1212] "Organization" refers to an organization other than a local government that has the role of collecting and providing information on pest infestations.

[1213] "Vermin sighting information" is data relating to the date and time of a vermin sighting, its location, type, and the details of the damage.

[1214] "Climate change data" is information about weather fluctuations, such as temperature, rainfall, and air pressure.

[1215] "Satoyama management information" refers to information related to the management of Satoyama, such as logging plans and conservation plans.

[1216] "Development information" is data about local land use and development plans, including new construction projects.

[1217] A "database" is a system for storing various types of information and for linking and mutual analysis.

[1218] "Collaboration and cross-analysis" is the process of combining multiple pieces of information, evaluating their relevance, and deriving comprehensive insights.

[1219] A "predictive algorithm" is a mathematical model for calculating and predicting the risk of pest infestation.

[1220] "Countermeasure proposals" are proposals that provide specific protective measures and guidelines for action based on the risk of pest infestation.

[1221] "Notification means" is a function that notifies the user of the generated countermeasure proposals via email or in-app notification.

[1222] The "emotion engine" is a system that analyzes user feedback and recognizes emotions.

[1223] "Customization" means individually adjusting the content of suggestions and notification methods based on the user's emotions and feedback.

[1224] The "smartphone app" is an application for mobile devices that provides users with information on the risk of pest infestations and suggestions for countermeasures.

[1225] "Color-coded display" is a display method that uses different colors on a map to visually distinguish risk levels.

[1226] This invention provides a system that links and analyzes pest infestation information, climate change data, and development information to predict the risk of pest infestation and propose countermeasures. This system collects and integrates data from various municipalities and organizations, weather information providers, and local governments, and generates countermeasure proposals by combining a predictive algorithm and an emotion engine.

[1227] Data collection

[1228] The server obtains pest sighting information from local governments and organizations via API and stores it in a database. This pest sighting information includes the date and time of sighting, location (latitude and longitude), species, and details of damage. Next, it obtains climate change data from a weather information provider and stores it in the same database. This includes weather information such as temperature, rainfall, and air pressure. Furthermore, it obtains satoyama management and development information, such as logging plans and construction projects, from local governments and stores it in the database.

[1229] Data linkage and analysis

[1230] The server links and organizes the accumulated pest infestation information, climate change data, and development information in chronological order, and evaluates the correlation between each piece of information. This linking and analysis is performed using a Python program and libraries such as Scikit-learn. The server also uses a predictive algorithm to score the pest infestation risk for each area and stores the results in a database.

[1231] Incorporating an emotion engine

[1232] The server incorporates an emotion engine that recognizes the user's emotions when generating countermeasure proposals. This emotion engine analyzes user feedback using, for example, IBM Watson NLP API to recognize emotions. If the user expresses positive or negative emotions toward a countermeasure proposal, the appropriateness of the proposal is evaluated and reflected in the next proposal generation.

[1233] Countermeasure proposals and notifications

[1234] The server generates countermeasure proposals based on the prediction results and the analysis results of the emotion engine. The generated countermeasure proposals include recommendations for rapid pest control activities in emergencies and the installation of protective nets. These countermeasure proposals are notified to relevant users (e.g., hunting license holders and local government officials) via a smartphone app. Notifications are sent via email or in-app push notifications, and the method and content of the notifications are adjusted based on the user's emotions.

[1235] Feedback and improved prediction accuracy

[1236] Users can check the latest forecast information and countermeasure suggestions through the dashboard of the smartphone app. Feedback information provided by users is sent to the server and used to improve the accuracy of the prediction algorithm and emotion engine. This allows the system to evolve over time and make more appropriate countermeasure suggestions.

[1237] Implementation example

[1238] For example, if a region experiences rising temperatures and new development projects at the same time, the system can predict an increased risk of pest infestations and send notifications to users in the area recommending the installation of protective netting. If users provide positive feedback on the suggestions, the system will consider the suggestions effective and will make similar suggestions in future similar situations.

[1239] Prompt Sentence Examples

[1240] markdown

[1241] Invention Contents

[1242] Develop a new application that combines the emotion engine of a system that links and cross-analyzes pest infestation information, climate change data, and development information to predict the risk of pest infestations. As an example, we are looking for ideas for a security app for smartphones that suggests personalized measures based on the user's emotions, allowing residents to respond appropriately.

[1243] input

[1244] The system collects and analyzes information on pest infestations, climate change data, and development information from local governments, organizations, and weather information providers, and predicts the risk of pest infestations.

[1245] Uses an emotion engine to customize countermeasure suggestions based on user feedback.

[1246] Example output

[1247] 1. A smartphone app that displays the risk of pest infestations in a color-coded manner on a map.

[1248] 2. Notify and display customized countermeasure suggestions based on the user's emotions.

[1249] 3. It has a dashboard function that allows you to understand risk areas at a glance.

[1250] 4. Specific implementation methods include clearly indicating the APIs and libraries to be used (e.g., Google Maps API, Scikit-learn, IBM Watson NLP API).

[1251] It is expected that the system for implementing this invention will effectively prevent damage caused by pests and contribute to the sustainability of agricultural and forest management.

[1252] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1253] System program processing steps

[1254] Step 1:

[1255] Information on pest sightings is obtained from each local government or organization. The server accesses the API of the local government or organization to obtain the date and time of sighting, location (latitude and longitude), type, and details of damage. The obtained data is stored in the server's database. The input is pest sighting information from the API, and the output is the information stored in the database.

[1256] Step 2:

[1257] Climate change data is obtained from a weather information service. The server uses the weather information service's API to obtain weather information such as temperature, precipitation, and air pressure. The obtained data is stored in a database. The input is climate change data from the API, and the output is the information stored in the database.

[1258] Step 3:

[1259] The server obtains Satoyama management and development information from local governments. Using the local government's API, the server obtains information such as new logging plans and construction projects. The obtained data is stored in a database. The input is Satoyama management and development information from the API, and the output is the information stored in the database.

[1260] Step 4:

[1261] The various types of stored data are linked and cross-analyzed. The server uses a Python program to link and cross-analyze the pest infestation information, climate change data, and development information stored in the database. This is the process of organizing data in chronological order and evaluating the relevance between each piece of information. The input is the information stored in the database, and the output is the results of the linkage and cross-analysis.

[1262] Step 5:

[1263] Predict the risk of pest infestation. The server uses the Scikit-learn library to apply a prediction algorithm and score the risk of pest infestation for each area. The input is the results of collaboration and cross-analysis, and the output is the risk score for each area.

[1264] Step 6:

[1265] Generate countermeasure proposals. Based on the prediction results, the server generates specific countermeasure proposals such as pest control activities and the installation of protective nets. The input is the risk score, and the output is the countermeasure proposals.

[1266] Step 7:

[1267] The emotion engine is used to customize countermeasure suggestions. The server uses IBM Watson NLP API to analyze user feedback and recognize the user's emotions. Based on the results, countermeasure suggestions are adjusted and customized. The input is user feedback, and the output is customized countermeasure suggestions.

[1268] Step 8:

[1269] The generated countermeasure proposals are notified to the user. The server notifies the user of the countermeasure proposals via a smartphone app via push notification or email. The input is the customized countermeasure proposals, and the output is a notification to the user.

[1270] Step 9:

[1271] Receives user feedback and uses it to improve the accuracy of the system. Users send feedback on proposed measures via a smartphone app. The server receives this feedback and uses it to improve the accuracy of the prediction algorithm and emotion engine. The input is user feedback, and the output is an improved prediction algorithm and emotion engine.

[1272] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1273] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1274] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1275] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1276] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1277] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1278] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1279] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1280] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1281] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1282] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1283] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1284] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1285] 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.

[1286] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1287] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by e...

Claims

1. A means of obtaining information on pest infestations from local governments and organizations, and a means for obtaining climate change data from a weather information service; A means of obtaining information on Satoyama management and development from local governments; means for storing the pest infestation information, climate change data and development information in a database; A means to link and mutually analyze various stored data, A means of predicting the risk of pest infestation; A means for generating countermeasure proposals based on the prediction results; The system includes a means for notifying relevant users of the generated countermeasure proposals.

2. 2. The system of claim 1, further comprising means for combining the pest infestation information, climate change data and development information and applying a predictive algorithm to score pest infestation risk by region.

3. means for displaying the proposed measures via a user interface; 10. The system of claim 1, further comprising means for receiving feedback information from relevant users and using it to improve the accuracy of the prediction algorithm.

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