system

Abstract

We provide the system. [Solution] A means of acquiring user land information and weather data, A means of analyzing acquired land information and weather data to propose optimal fertilizers and cultivation methods, A means of providing the proposed fertilizer and cultivation method to the user, A method for collecting environmental data of crops in real time using sensors, Means for adjusting environmental conditions based on collected environmental data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in order to improve the production efficiency of crops and realize sustainable agriculture friendly to the environment, it is important to find agricultural methods suitable for the characteristics of the land and weather conditions. However, efficiently analyzing individual land conditions and weather data and performing optimal cultivation management is a burdensome task for many farmers. In addition, the lack of technology to quickly respond to real-time environmental changes is also a factor hindering sustainability.

Means for Solving the Problems

[0005] To address the above challenges, this invention provides an AI-driven system that acquires user land information and weather data, analyzes them, and proposes optimal fertilizers and cultivation methods. Furthermore, it uses sensor technology to collect environmental data around crops in real time and automatically adjusts environmental conditions based on that data to optimize crop growth. This system enables users to engage in effective and sustainable agricultural activities while reducing their burden.

[0006] A "user" is an individual or organization that uses the system to manage their own land information and cultivation activities.

[0007] "Land information" refers to information that includes location information, soil characteristics, area, and other data related to cultivation.

[0008] "Weather data" refers to information about past and present weather in a specific region, including elements such as temperature, precipitation, and solar radiation.

[0009] A "sensor" is an electronic device used to collect environmental data in real time, and includes temperature sensors, humidity sensors, and others.

[0010] "Environmental data" refers to data related to the growing environment of crops, including information such as temperature and humidity.

[0011] "Real-time" is a concept that indicates data processing and responses are performed in a time frame that is almost instantaneous.

[0012] "Analysis" is the process of analyzing data and deriving meaning from it in order to determine the optimal fertilizer and cultivation method based on the input information.

[0013] "Fertilizer" refers to a substance containing nutrients that is added to the soil to promote the growth of crops.

[0014] "Cultivation method" refers to the technical methods and processes for growing specific crops, including a series of operations such as sowing, thinning, and harvesting.

[0015] "Automatic adjustment" is a function that operates without human intervention to optimize conditions based on the data collected by the system.

Brief Description of the Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiment for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that proposes optimal fertilizers and cultivation methods based on the user's land information and weather data, and manages the crop growth environment in real time. Specific embodiments for carrying out this invention are described below.

[0038] First, users input information about the land they manage through a dedicated app. This land information includes location, soil pH value, area, and type of crop to be cultivated. This information is sent to a server and used as basic data for analysis. The server accesses an external weather database to obtain historical and current weather data for the specified area. This allows for a detailed understanding of cultivation conditions for a particular plot of land.

[0039] The server uses an AI model to analyze acquired land information and weather data. This analysis determines the optimal type of fertilizer and its application schedule. It also suggests appropriate cultivation methods for each crop type. These suggestions are notified to the user via a dedicated app for confirmation. This allows the user to select the appropriate fertilizer and implement effective cultivation methods.

[0040] Furthermore, various sensors are attached to the terminal to collect environmental data such as temperature, humidity, and sunlight in real time. This environmental data is transferred to a server and analyzed by AI. If the analysis reveals that the environmental conditions are not optimal, a command is sent to the terminal, and adjustments are made automatically. For example, if the temperature exceeds the appropriate range, the cooling system will activate, and if the humidity is too low, the humidifier will start.

[0041] As a concrete example, let's consider the case of cultivating tomatoes in Hokkaido. The user registers with a dedicated app and provides land information and tomato cultivation information. The server analyzes the previous year's weather data for Hokkaido combined with current weather data. Based on the analysis, appropriate fertilizers are suggested to adjust the soil acidity, and cultivation methods adapted to the cool climate are indicated. In addition, based on environmental data monitored by sensors, automatic adjustments are made to maintain the optimal temperature and humidity range for tomato growth.

[0042] Thus, the system of the present invention uses a data-driven approach to streamline agricultural activities and support environmentally friendly and sustainable crop cultivation.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input their land information using a dedicated app. This information includes location, soil pH value, area, and type of crop to be cultivated. This data is then transmitted to a server via the internet.

[0046] Step 2:

[0047] The server connects to an external weather database to retrieve historical and current weather data for a specified location. The retrieved data includes temperature, precipitation, and solar radiation, and the data is prepared for analysis.

[0048] Step 3:

[0049] The server uses an AI model to analyze the transmitted land information and acquired weather data. Based on the analysis results, it determines the optimal type of fertilizer and cultivation method, and suggests the timing and amount of fertilizer application. This information is sent in real time to the user's dedicated app.

[0050] Step 4:

[0051] Users receive suggestions through the app and apply fertilizer and set cultivation conditions according to those suggestions. Users can either refer to the AI's recommendations or make adjustments based on their own judgment.

[0052] Step 5:

[0053] Sensors attached to the device monitor the temperature, humidity, and sunlight levels of the cultivation environment in real time. The collected data is transmitted from the device to a server, and changes in the environment are tracked.

[0054] Step 6:

[0055] The server analyzes environmental data in real time and sends appropriate control instructions to terminals if it detects environmental changes that fall outside the standard range. For example, this can activate a cooling system if the temperature is high or start a humidifier if the humidity is low.

[0056] Step 7:

[0057] Users can use the app to check crop growth status and environmental data in real time. The visualized data allows users to determine if adjustments to cultivation conditions are necessary and take action as needed.

[0058] This processing flow enables users to efficiently cultivate crops in an environment suited to their needs, thereby realizing sustainable agriculture.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Modern agriculture requires selecting appropriate nutrients and cultivation methods in response to changing weather conditions and soil characteristics to optimize crop growth. However, this requires specialized knowledge and considerable effort, making efficient farming difficult. Furthermore, the lack of real-time environmental adjustments can negatively impact crop growth.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for users to input and receive information about the land, means for acquiring past and present atmospheric condition data for the region from an external atmospheric condition information source, and means for analyzing the acquired land information and atmospheric condition data using generation AI technology to propose optimal nutrients and cultivation methods. As a result, users can perform efficient and effective farming work even without specialized knowledge, and can further optimize the growth environment by adjusting the surrounding conditions of the crops in real time.

[0064] "A means by which users input and receive information about land" refers to a process in which users input data related to the land they manage through a dedicated interface, and a server, which is a central processing unit, receives that data.

[0065] "Means of obtaining historical and current atmospheric condition data for a region from external atmospheric condition information sources" refers to the process of obtaining historical and current weather-related data for a specified region from external weather data providers or databases.

[0066] "A means of analyzing acquired land information and atmospheric condition data using generative AI technology to propose optimal nutrients and cultivation methods" refers to a process of analyzing collected land-related information and weather condition data using generative artificial intelligence, and based on this, providing the most appropriate fertilizer selection and cultivation techniques.

[0067] "Means of informing users of proposed nutrients and cultivation methods" refers to the process of notifying users of the proposed fertilizers and cultivation techniques obtained as a result of the analysis.

[0068] "Methods for collecting real-time data on the surrounding conditions of crops using various measuring devices" refers to the process of collecting environmental data related to crop growth in real time using various sensors.

[0069] "Means for analyzing collected ambient situation data and generating automated commands for situation adjustment" refers to a process that analyzes collected environmental data and generates automated commands for necessary environmental adjustments based on the results.

[0070] This invention is a system that uses the user's land information and weather data to automatically suggest optimal nutrients and cultivation techniques in agriculture, and manages the crop growth environment in real time. The following describes specific embodiments of this invention.

[0071] First, users use a dedicated application to input information about the land they manage. This information includes the land's location coordinates, soil acidity, area, and the type of crops cultivated. This entered data is then transmitted to the server via the terminal.

[0072] The server makes API requests to external weather databases to retrieve historical and current weather information for the user-specified area. This process incorporates various weather parameters such as temperature, precipitation, and sunshine. The server then inputs this data into a generating AI model for data analysis.

[0073] This AI model learns from a vast amount of historical data and generates optimal farming methods under specific conditions. The server then determines the optimal nutrients, their application schedule, and even the best cultivation methods for different crop types. This determined information is then communicated to the user through a dedicated app.

[0074] The user's device is equipped with various sensors that monitor environmental data such as temperature, humidity, and sunlight in real time. This data is periodically sent to a server and analyzed by an AI model. If the analysis determines that the environment is not optimal for crops, the server sends an automatic adjustment command to the device. This command controls cooling and humidifying devices as needed.

[0075] As a concrete example, consider growing tomatoes in Hokkaido. The user inputs land and cultivation information into the app, and the server analyzes the data by combining weather data from Hokkaido in the previous year with current data. Based on the analysis results, appropriate fertilizers are suggested, and cultivation methods suitable for the cool climate are indicated. At the same time, based on data collected by sensors, the server automatically adjusts to maintain the temperature and humidity within the optimal range.

[0076] An example of a prompt message for a generative AI model is as follows:

[0077] "Based on tomato cultivation information in Hokkaido provided by the user, along with historical and current weather data, propose the optimal fertilizer and cultivation method. Furthermore, please explain the procedures and methods for building a system that automatically adjusts the growing environment based on real-time environmental data."

[0078] Thus, the system of the present invention employs a data-driven approach to streamline agricultural activities and support environmentally friendly and sustainable agriculture.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users input land information through a dedicated app. Specifically, they obtain land location information using GPS, measure and input soil pH values, and select the land area and the type of crop they are considering cultivating. This data is sent from the app to a server. The entered information is stored on the server as numerical data and serves as the basis for analysis.

[0082] Step 2:

[0083] Based on the received land information, the server sends an API request to an external weather database. This retrieves weather data for the past 10 years and current weather data for the specified area. The retrieved data is stored on the server and structured as numerical data of atmospheric conditions such as temperature, precipitation, and sunshine.

[0084] Step 3:

[0085] The server uses an AI model that generates data based on acquired land information and weather data as input, and performs data analysis. The AI ​​model processes this data to generate the optimal combination of nutrients, application schedule, and appropriate cultivation methods. The analysis results are ready to be provided to the user as text data.

[0086] Step 4:

[0087] The server notifies the user of suggestions generated by the AI ​​model via a dedicated app. The user reviews the suggested nutrients and cultivation methods and incorporates them into their farming plan as needed. The suggestions are displayed in a calendar or list format tailored to the user's work schedule.

[0088] Step 5:

[0089] The terminal is equipped with various sensors to measure temperature, humidity, sunlight, and other parameters. These sensors collect environmental data in real time and send the generated data to a server. The environmental data is updated every five minutes.

[0090] Step 6:

[0091] The server performs real-time analysis based on the collected environmental data. If the analysis reveals that the environmental conditions are not optimal, the system generates automated commands for adjustment. For example, if the temperature exceeds a set range, it sends a command to the terminal to activate the cooling system. This ensures that optimal environmental conditions are maintained.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] In traditional agricultural practices, optimizing crop growth environments requires considerable effort and experience, and this limitation is particularly pronounced when dealing with vast farmlands. As a result, problems such as reduced yields and decreased quality due to the inability to apply fertilizers and adjust environmental conditions in a timely manner have arisen.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for acquiring user land information and weather data, means for analyzing the acquired land information and weather data and proposing optimal fertilizers and cultivation methods, means for collecting crop environmental data in real time using sensors, and means for automatically performing fertilizer application and environmental adjustment via a control device installed on the machinery. This enables real-time environmental management and the execution of optimal cultivation processes even over a wide area of ​​farmland.

[0097] "User land information" refers to information about the geographical area managed by the user, including details such as its location, soil properties, area, and the types of crops cultivated there.

[0098] "Weather data" refers to information about past and present weather conditions in a specific region, including data such as temperature, precipitation, sunshine, and wind speed.

[0099] "Means for proposing fertilizers and cultivation methods" refers to a system that analyzes acquired land information and weather data and, based on the results, indicates the optimal fertilizers and cultivation methods.

[0100] A "sensor" is a measuring device used to collect environmental information such as temperature, humidity, sunlight intensity, and soil pH values ​​in real time.

[0101] "Means for adjusting environmental conditions" refer to devices and systems that control machinery and systems based on environmental data collected by sensors to maintain optimal environmental conditions.

[0102] A "control device" is a device installed in machinery that automatically performs tasks such as fertilizer application and temperature / humidity control.

[0103] "Means of visualizing and providing information to users in real time" refers to a function that instantly displays crop growth status and environmental data visually, providing it in a state where users can easily check it.

[0104] To implement this invention, a terminal is first required to acquire information about the land owned by the user. The user uses this terminal to input detailed data such as location information, soil pH value, area, and type of crop to be cultivated. This information is transmitted to a server and used as basic data for analysis.

[0105] The server accesses external weather databases to collect historical and current weather data for the relevant region. This data includes temperature, precipitation, sunshine, and other information. Based on this data, the server uses an AI model to analyze it and suggest optimal fertilizers and cultivation methods.

[0106] The analysis results are provided to the user through prompts generated using a generative AI model. For example, a prompt such as "Generate the optimal scenario for tomato cultivation based on the given weather data" is used.

[0107] Mechanical devices equipped with sensors monitor crop growth environments in real time. Data such as temperature, humidity, and sunlight are collected and transmitted to a server. The server analyzes this data and sends instructions to a control unit to maintain optimal environmental conditions. This control unit operates the automatic application of fertilizer and environmental adjustments.

[0108] As a concrete example, consider the case of cultivating tomatoes on a farm in Hokkaido. The user provides land information and tomato cultivation information through an app, and the server analyzes this information in combination with weather data. Appropriate fertilizers and cultivation methods are suggested, and environmental sensors monitor the temperature and humidity range and make adjustments as needed. This enables the user to achieve efficient and sustainable agriculture.

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

[0110] Step 1:

[0111] Users use their devices to input information about the land they manage. Specifically, they record location information, soil pH value, area, and types of crops cultivated in a dedicated app. This input data is sent to a server and used as basic data for analysis.

[0112] Step 2:

[0113] The server accesses an external weather database to retrieve historical and current weather data for the target area. The retrieved data includes temperature, precipitation, and sunshine, which are then input into the AI ​​model along with land information.

[0114] Step 3:

[0115] The server analyzes acquired land information and weather data using an AI model. As part of data processing, weather data is converted into time-series features and combined with land information for analysis. This analysis determines the optimal type of fertilizer, its application schedule, and cultivation method.

[0116] Step 4:

[0117] The server uses an AI model to generate analysis results in the form of prompts, which are then sent to the user. An example of a prompt might be, "Generate the optimal scenario for tomato cultivation based on the given weather data." The user checks this notification using a dedicated app.

[0118] Step 5:

[0119] Sensors attached to the terminal collect environmental data such as temperature, humidity, and sunlight in real time, and continuously transmit this data to a server. The server collects the real-time data and analyzes whether the environmental conditions are optimal.

[0120] Step 6:

[0121] If the server determines that environmental conditions need to be adjusted as needed, it sends instructions to the control device attached to the machinery. This triggers the automatic application of fertilizer, and the operation of cooling and humidifying systems, maintaining the optimal cultivation environment.

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

[0123] This invention is a system that not only proposes optimal fertilizers and cultivation methods based on the user's land information and weather data, but also incorporates an emotion engine that recognizes the user's emotions. This system optimizes the user's work environment by adjusting advice based on their emotional state, thereby improving the work experience.

[0124] First, the user inputs land information (location, soil characteristics, type of crop to be grown, etc.) through a dedicated app. The server retrieves necessary weather data from an external weather database and uses AI to analyze this information, suggesting the optimal fertilizer and cultivation method to the user. This allows the user to understand which fertilizer to use, when to use it, and how to grow their crops.

[0125] Furthermore, the user's smart device is equipped with emotion recognition sensors such as a camera and microphone, allowing the emotion engine to analyze the user's emotions in real time. For example, if the user is feeling stressed, the emotion engine sends that information to the server, which can then suggest cultivation methods and schedules that are less burdensome for the user. This information is only obtained with the user's explicit consent, and their privacy is protected.

[0126] As a concrete example, consider a user who is growing tomatoes and is experiencing a busy period at work. Suppose the system detects that the user is experiencing high stress levels based on information entered using an emotion recognition sensor. In this case, the server uses the information from the emotion engine to provide a cultivation plan that, for example, increases the number of tasks that can be automated. Furthermore, the frequency and content of app notifications are adjusted according to the user's situation. This allows the user to grow their crops under appropriate management without feeling pressured.

[0127] Thus, the system of the present invention analyzes the user's emotional state in addition to land and weather condition data to provide appropriate feedback. Therefore, as a comprehensive agricultural management platform, it contributes to improving efficiency and usability.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user launches a dedicated app and inputs land information (location, soil pH, cultivated crops, etc.) to send it to the server. At the same time, the user's smart device prepares to capture their emotional state in real time using its camera and microphone.

[0131] Step 2:

[0132] The server accesses an external weather database based on the received land information and retrieves weather data for the specified area. The retrieved data includes historical temperature, precipitation, and solar radiation.

[0133] Step 3:

[0134] The server uses an AI model to analyze received land information and weather data to determine the optimal fertilizer and cultivation methods. The results of this analysis are notified to the user through a dedicated app.

[0135] Step 4:

[0136] The emotion engine built into the device recognizes the user's emotional state in real time based on data from the user's camera and microphone. The results of the emotion recognition are sent to a server.

[0137] Step 5:

[0138] The server analyzes the user's emotional data from the emotion engine and, based on their emotional state (e.g., stress level), dynamically adjusts the cultivation schedule and fertilizer application methods to provide new suggestions. The user is notified of the changed suggestions through their dedicated app.

[0139] Step 6:

[0140] Users can review new cultivation suggestions that are sensitive to their feelings and modify their work plans as needed. Furthermore, by adjusting the notification content, users can accept feasible cultivation methods that reduce their burden.

[0141] Step 7:

[0142] The sensors on the device continuously monitor the crop's growing environment and transmit environmental data (temperature, humidity, solar radiation, etc.) to the server. The server then analyzes the environmental conditions in real time and adjusts them remotely or automatically as needed.

[0143] This series of processes allows users to cultivate crops efficiently, comfortably, and with consideration for emotions and the environment.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] Conventional agricultural management systems offer cultivation method suggestions based on land information and weather data, but they lack the ability to adaptively modify plans that take into account the emotional state of the workers, resulting in a failure to alleviate the psychological burden on users. Furthermore, they lacked adequate visualization based on crop growth status and changes in the emotional state of the workers, making it difficult for users to manage their operations flexibly according to the situation.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for acquiring user land information and weather data, means for analyzing the acquired land information and weather data and proposing optimal fertilizers and cultivation methods, and means for acquiring and analyzing worker emotional data using sensors. This enables users to flexibly and efficiently manage agriculture by adjusting cultivation plans and schedules according to the emotional state of the workers and through visualization.

[0149] A "user" is an entity that uses this system to provide land information and sentiment data, and receives suggested cultivation methods and schedules.

[0150] "Land information" refers to physical and environmental data necessary for agricultural activities, such as location, soil characteristics, and the types of crops to be cultivated.

[0151] "Weather data" refers to data related to current and predicted weather conditions obtained from external databases.

[0152] "Fertilizer" refers to nutrients that are given to the soil and plants to promote plant growth.

[0153] "Cultivation methods" refer to the specific procedures and techniques used for growing and managing plants.

[0154] A "sensor" is a device used to detect the environment or the emotional state of workers, and this system uses cameras and microphones.

[0155] "Emotional data" refers to data that reflects the emotional state of workers, including stress, joy, and other factors.

[0156] A "generative AI model" refers to a form of artificial intelligence that analyzes large amounts of data and generates new proposals or conclusions based on that information.

[0157] A "prompt" refers to an instruction or question used when inputting information into a generative AI model.

[0158] The embodiment of this invention is configured as an integrated system for optimizing the user's agricultural experience. Its specific elements are described below.

[0159] Users input land information using a dedicated application. This information includes location data, soil characteristics, and the type of crops they plan to cultivate. Furthermore, users can provide emotional data through a smart device. This device is equipped with emotion recognition sensors such as a camera and microphone, which analyze the user's facial expressions and voice to detect their emotional state.

[0160] After receiving land information from the user, the server retrieves the latest weather data from an external weather database. This weather database could be, for example, a public weather information service. The server then analyzes this information using a generative AI model to generate optimal fertilizers and cultivation methods. This analysis determines which fertilizer is most effective and how crops should be cultivated.

[0161] Regarding emotional data, the emotion engine analyzes information collected from the device to evaluate the user's real-time mental state. Based on this emotional state, the server prompts the generative AI model, suggesting adjusted cultivation methods and schedules to reduce the user's psychological burden. An example of a prompt used here might be, "If the user's emotions are stress-based, please provide the optimal work procedure."

[0162] This system provides users with flexible and personalized advice, enabling them to improve the efficiency of their farm management. Furthermore, users can visually review the feedback provided through the app, allowing for quick and appropriate action based on the situation.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] Users input land information (location, soil characteristics, crop type, etc.) using a dedicated app. This input data is sent to the server. Upon receiving this information, the server stores it in a database and uses it as basic data necessary for obtaining subsequent weather data.

[0166] Step 2:

[0167] The server retrieves current and predicted weather data from an external weather database. The input here is the user's land information obtained in step 1. The server uses this data to request weather information for the relevant area via an API. The output includes temperature, precipitation, sunshine hours, etc.

[0168] Step 3:

[0169] The server processes land information and weather data in a unified manner and performs analysis using a generated AI model. The input data consists of land information and weather data, and the output is suggestions regarding the optimal type and timing of fertilizer application and cultivation procedures. Specifically, the AI ​​model analyzes the data and performs calculations based on past success stories and scientific evidence.

[0170] Step 4:

[0171] The device uses a camera and microphone to collect user emotional data in real time. Input includes the user's facial expressions and voice data, which are then analyzed by an emotion engine. Output includes stress levels and emotional tone. The emotional state is then transmitted to a server.

[0172] Step 5:

[0173] The server considers emotional data and previously generated cultivation suggestions, inputs prompt sentences into the AI ​​model, and adjusts the suggestions for the user. The inputs at this time are the emotional state and cultivation suggestions. As output, a cultivation plan and schedule adjusted to reduce the user's psychological burden are generated.

[0174] Step 6:

[0175] The server sends the final suggestions to the user's device, making them available for review within the app. The input is the server's refined suggestion data, and the output is visual feedback presented in the user interface. This allows users to take immediate, practical action.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In recent years, the optimization of cultivation methods using land and weather information has become increasingly important in the agricultural sector. However, these systems cannot adjust work processes while considering the emotional state of workers, which can lead to increased workload. Similarly, in factories, there is a demand for systems that enable efficient production while reducing worker fatigue and stress. Furthermore, the difficulty in continuously adjusting fertilization and cultivation suggestions necessitates real-time environmental adaptation.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for acquiring user area information and weather information, means for analyzing the acquired area information and weather information and suggesting optimal nutrients and cultivation methods, and means for analyzing the worker's emotional state and dynamically adjusting the work process. This makes it possible for workers to work in an environment that is less stressful for them while simultaneously implementing the optimal cultivation method.

[0181] "User domain information" refers to information about a specific user's geographical location and its characteristics.

[0182] "Weather information" refers to information about environmental conditions such as weather, temperature, and humidity, obtained from external databases.

[0183] "Nutrient solutions" are materials used to promote plant growth and are suggested as part of fertilization.

[0184] "Cultivation methods" refer to the specific processes and procedures for growing crops and other plants, which are adjusted to achieve the optimal growing environment.

[0185] A "sensing device" refers to a device used to collect specific environmental information or emotional states, and may include cameras, microphones, and sensors.

[0186] "Worker's emotional state" refers to information about the psychological state of workers, analyzed using machine learning models or similar methods.

[0187] A "work process" refers to a series of tasks required in industrial or agricultural settings, and is subject to dynamic adjustment.

[0188] The system for realizing this application primarily consists of a server, a user's smart terminal, and sensing devices within the factory. The server analyzes area information provided by the user and weather information obtained from an external database. This analysis utilizes a database management system (e.g., PostgreSQL) and a machine learning framework (e.g., TENSORFLOW®). Based on the analysis results, the system calculates and presents the optimal nutrients and cultivation methods to the user.

[0189] The user's smart device is equipped with a camera and microphone to analyze their emotional state, and these are used as sensing devices. The data obtained from these sensing devices is processed by an emotion recognition API (e.g., Affectiva API) to analyze the worker's emotional state in real time. Through smart glasses, the user can receive and implement adjustments to suggested cultivation methods and work processes.

[0190] For example, if the system detects that a factory worker is fatigued, the server will automate the work process and make adjustments to reduce the burden. It will also suggest break times and provide support to reduce worker stress.

[0191] An example of a prompt for a generative AI model could be an instruction such as, "Based on the generated data, please suggest an appropriate work process based on the worker's emotional state and work environment data." This prompt could then be used to adjust the actual work process and improve cultivation methods.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server receives area information from the user's smart device. This includes location information, soil characteristics, and the types of plants being cultivated. The received area information is stored in a database on the server.

[0195] Step 2:

[0196] The server accesses an external weather database to retrieve relevant weather information. Based on the retrieved weather information and regional information, it uses a machine learning framework (e.g., TensorFlow) to calculate the optimal nutrients and cultivation methods, and presents the results to the user. At this stage, data analysis and model calculations are performed.

[0197] Step 3:

[0198] The user's smart device uses sensing devices to detect the worker's emotional state in real time. This involves using a camera and microphone to analyze emotional data through an emotion recognition API (e.g., Affectiva API). The analysis results are then transmitted to a processing system within the device.

[0199] Step 4:

[0200] The terminal sends data on the worker's emotional state to the server. Based on this data, the server uses an AI model to generate appropriate work process adjustments and creates an optimized work schedule for the worker. The generated adjustments are then fed back to the user's smart terminal.

[0201] Step 5:

[0202] The user reviews the work process and cultivation methods presented through the terminal and performs the tasks as needed. Data is presented to the user visually and interactively on the terminal, including suggestions for automated procedures and break times.

[0203] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] This invention is a system that proposes optimal fertilizers and cultivation methods based on the user's land information and weather data, and manages the crop growth environment in real time. Specific embodiments for carrying out this invention are described below.

[0220] First, users input information about the land they manage through a dedicated app. This land information includes location, soil pH value, area, and type of crop to be cultivated. This information is sent to a server and used as basic data for analysis. The server accesses an external weather database to obtain historical and current weather data for the specified area. This allows for a detailed understanding of cultivation conditions for a particular plot of land.

[0221] The server uses an AI model to analyze acquired land information and weather data. This analysis determines the optimal type of fertilizer and its application schedule. It also suggests appropriate cultivation methods for each crop type. These suggestions are notified to the user via a dedicated app for confirmation. This allows the user to select the appropriate fertilizer and implement effective cultivation methods.

[0222] Furthermore, various sensors are attached to the terminal to collect environmental data such as temperature, humidity, and sunlight in real time. This environmental data is transferred to a server and analyzed by AI. If the analysis reveals that the environmental conditions are not optimal, a command is sent to the terminal, and adjustments are made automatically. For example, if the temperature exceeds the appropriate range, the cooling system will activate, and if the humidity is too low, the humidifier will start.

[0223] As a concrete example, let's consider the case of cultivating tomatoes in Hokkaido. The user registers with a dedicated app and provides land information and tomato cultivation information. The server analyzes the previous year's weather data for Hokkaido combined with current weather data. Based on the analysis, appropriate fertilizers are suggested to adjust the soil acidity, and cultivation methods adapted to the cool climate are indicated. In addition, based on environmental data monitored by sensors, automatic adjustments are made to maintain the optimal temperature and humidity range for tomato growth.

[0224] Thus, the system of the present invention uses a data-driven approach to streamline agricultural activities and support environmentally friendly and sustainable crop cultivation.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] Users input their land information using a dedicated app. This information includes location, soil pH value, area, and type of crop to be cultivated. This data is then transmitted to a server via the internet.

[0228] Step 2:

[0229] The server connects to an external weather database to retrieve historical and current weather data for a specified location. The retrieved data includes temperature, precipitation, and solar radiation, and the data is prepared for analysis.

[0230] Step 3:

[0231] The server uses an AI model to analyze the transmitted land information and acquired weather data. Based on the analysis results, it determines the optimal type of fertilizer and cultivation method, and suggests the timing and amount of fertilizer application. This information is sent in real time to the user's dedicated app.

[0232] Step 4:

[0233] Users receive suggestions through the app and apply fertilizer and set cultivation conditions according to those suggestions. Users can either refer to the AI's recommendations or make adjustments based on their own judgment.

[0234] Step 5:

[0235] Sensors attached to the device monitor the temperature, humidity, and sunlight levels of the cultivation environment in real time. The collected data is transmitted from the device to a server, and changes in the environment are tracked.

[0236] Step 6:

[0237] The server analyzes environmental data in real time and sends appropriate control instructions to terminals if it detects environmental changes that fall outside the standard range. For example, this can activate a cooling system if the temperature is high or start a humidifier if the humidity is low.

[0238] Step 7:

[0239] Users can use the app to check crop growth status and environmental data in real time. The visualized data allows users to determine if adjustments to cultivation conditions are necessary and take action as needed.

[0240] This processing flow enables users to efficiently cultivate crops in an environment suited to their needs, thereby realizing sustainable agriculture.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0243] Modern agriculture requires selecting appropriate nutrients and cultivation methods in response to changing weather conditions and soil characteristics to optimize crop growth. However, this requires specialized knowledge and considerable effort, making efficient farming difficult. Furthermore, the lack of real-time environmental adjustments can negatively impact crop growth.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes means for users to input and receive information about the land, means for acquiring past and present atmospheric condition data for the region from an external atmospheric condition information source, and means for analyzing the acquired land information and atmospheric condition data using generation AI technology to propose optimal nutrients and cultivation methods. As a result, users can perform efficient and effective farming work even without specialized knowledge, and can further optimize the growth environment by adjusting the surrounding conditions of the crops in real time.

[0246] "A means by which users input and receive information about land" refers to a process in which users input data related to the land they manage through a dedicated interface, and a server, which is a central processing unit, receives that data.

[0247] "Means of obtaining historical and current atmospheric condition data for a region from external atmospheric condition information sources" refers to the process of obtaining historical and current weather-related data for a specified region from external weather data providers or databases.

[0248] "A means of analyzing acquired land information and atmospheric condition data using generative AI technology to propose optimal nutrients and cultivation methods" refers to a process of analyzing collected land-related information and weather condition data using generative artificial intelligence, and based on this, providing the most appropriate fertilizer selection and cultivation techniques.

[0249] "Means of informing users of proposed nutrients and cultivation methods" refers to the process of notifying users of the proposed fertilizers and cultivation techniques obtained as a result of the analysis.

[0250] "Methods for collecting real-time data on the surrounding conditions of crops using various measuring devices" refers to the process of collecting environmental data related to crop growth in real time using various sensors.

[0251] "Means for analyzing collected ambient situation data and generating automated commands for situation adjustment" refers to a process that analyzes collected environmental data and generates automated commands for necessary environmental adjustments based on the results.

[0252] This invention is a system that uses the user's land information and weather data to automatically suggest optimal nutrients and cultivation techniques in agriculture, and manages the crop growth environment in real time. The following describes specific embodiments of this invention.

[0253] First, users use a dedicated application to input information about the land they manage. This information includes the land's location coordinates, soil acidity, area, and the type of crops cultivated. This entered data is then transmitted to the server via the terminal.

[0254] The server makes API requests to external weather databases to retrieve historical and current weather information for the user-specified area. This process incorporates various weather parameters such as temperature, precipitation, and sunshine. The server then inputs this data into a generating AI model for data analysis.

[0255] This AI model learns from a vast amount of historical data and generates optimal farming methods under specific conditions. The server then determines the optimal nutrients, their application schedule, and even the best cultivation methods for different crop types. This determined information is then communicated to the user through a dedicated app.

[0256] The user's device is equipped with various sensors that monitor environmental data such as temperature, humidity, and sunlight in real time. This data is periodically sent to a server and analyzed by an AI model. If the analysis determines that the environment is not optimal for crops, the server sends an automatic adjustment command to the device. This command controls cooling and humidifying devices as needed.

[0257] As a concrete example, consider growing tomatoes in Hokkaido. The user inputs land and cultivation information into the app, and the server analyzes the data by combining weather data from Hokkaido in the previous year with current data. Based on the analysis results, appropriate fertilizers are suggested, and cultivation methods suitable for the cool climate are indicated. At the same time, based on data collected by sensors, the server automatically adjusts to maintain the temperature and humidity within the optimal range.

[0258] An example of a prompt message for a generative AI model is as follows:

[0259] "Based on tomato cultivation information in Hokkaido provided by the user, along with historical and current weather data, propose the optimal fertilizer and cultivation method. Furthermore, please explain the procedures and methods for building a system that automatically adjusts the growing environment based on real-time environmental data."

[0260] Thus, the system of the present invention employs a data-driven approach to streamline agricultural activities and support environmentally friendly and sustainable agriculture.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] Users input land information through a dedicated app. Specifically, they obtain land location information using GPS, measure and input soil pH values, and select the land area and the type of crop they are considering cultivating. This data is sent from the app to a server. The entered information is stored on the server as numerical data and serves as the basis for analysis.

[0264] Step 2:

[0265] Based on the received land information, the server sends an API request to an external weather database. This retrieves weather data for the past 10 years and current weather data for the specified area. The retrieved data is stored on the server and structured as numerical data of atmospheric conditions such as temperature, precipitation, and sunshine.

[0266] Step 3:

[0267] The server uses an AI model that generates data based on acquired land information and weather data as input, and performs data analysis. The AI ​​model processes this data to generate the optimal combination of nutrients, application schedule, and appropriate cultivation methods. The analysis results are ready to be provided to the user as text data.

[0268] Step 4:

[0269] The server notifies the user of suggestions generated by the AI ​​model via a dedicated app. The user reviews the suggested nutrients and cultivation methods and incorporates them into their farming plan as needed. The suggestions are displayed in a calendar or list format tailored to the user's work schedule.

[0270] Step 5:

[0271] The terminal is equipped with various sensors to measure temperature, humidity, sunlight, and other parameters. These sensors collect environmental data in real time and send the generated data to a server. The environmental data is updated every five minutes.

[0272] Step 6:

[0273] The server performs real-time analysis based on the collected environmental data. If the analysis reveals that the environmental conditions are not optimal, the system generates automated commands for adjustment. For example, if the temperature exceeds a set range, it sends a command to the terminal to activate the cooling system. This ensures that optimal environmental conditions are maintained.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In traditional agricultural practices, optimizing crop growth environments requires considerable effort and experience, and this limitation is particularly pronounced when dealing with vast farmlands. As a result, problems such as reduced yields and decreased quality due to the inability to apply fertilizers and adjust environmental conditions in a timely manner have arisen.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for acquiring user land information and weather data, means for analyzing the acquired land information and weather data and proposing optimal fertilizers and cultivation methods, means for collecting crop environmental data in real time using sensors, and means for automatically performing fertilizer application and environmental adjustment via a control device installed on the machinery. This enables real-time environmental management and the execution of optimal cultivation processes even over a wide area of ​​farmland.

[0279] "User land information" refers to information about the geographical area managed by the user, including details such as its location, soil properties, area, and the types of crops cultivated there.

[0280] "Weather data" refers to information about past and present weather conditions in a specific region, including data such as temperature, precipitation, sunshine, and wind speed.

[0281] "Means for proposing fertilizers and cultivation methods" refers to a system that analyzes acquired land information and weather data and, based on the results, indicates the optimal fertilizers and cultivation methods.

[0282] A "sensor" is a measuring device used to collect environmental information such as temperature, humidity, sunlight exposure, and soil pH value in real time.

[0283] The "means for adjusting environmental conditions" is something that has the function of controlling mechanical devices or systems based on the environmental data collected by sensors to maintain optimal environmental conditions.

[0284] A "control device" is a device installed in a mechanical device that automatically executes operations such as fertilizer spraying and temperature and humidity adjustment.

[0285] The "means for visualizing in real time and providing to the user" is something that has the function of immediately visually displaying the growth status of crops and environmental data and providing them in a state where the user can view them.

[0286] To implement this invention, first, a terminal for obtaining information about the land owned by the user is required. The user uses this terminal to input detailed data such as location information, soil pH value, area, and the type of crop to be cultivated. These information are transmitted to the server and used as basic data for analysis.

[0287] The server accesses an external weather database and collects past and current weather data of the corresponding region. This data includes temperature, precipitation, sunlight exposure, etc. Based on this data, the server performs analysis using an AI model and proposes optimal fertilizers and cultivation methods.

[0288] The analysis results are provided to the user through prompt texts created using the generated AI model. For example, a prompt text such as "Please generate an optimal scenario for tomato cultivation based on the given weather data." is used.

[0289] Mechanical devices equipped with sensors monitor crop growth environments in real time. Data such as temperature, humidity, and sunlight are collected and transmitted to a server. The server analyzes this data and sends instructions to a control unit to maintain optimal environmental conditions. This control unit operates the automatic application of fertilizer and environmental adjustments.

[0290] As a concrete example, consider the case of cultivating tomatoes on a farm in Hokkaido. The user provides land information and tomato cultivation information through an app, and the server analyzes this information in combination with weather data. Appropriate fertilizers and cultivation methods are suggested, and environmental sensors monitor the temperature and humidity range and make adjustments as needed. This enables the user to achieve efficient and sustainable agriculture.

[0291] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0292] Step 1:

[0293] Users use their devices to input information about the land they manage. Specifically, they record location information, soil pH value, area, and types of crops cultivated in a dedicated app. This input data is sent to a server and used as basic data for analysis.

[0294] Step 2:

[0295] The server accesses an external weather database to retrieve historical and current weather data for the target area. The retrieved data includes temperature, precipitation, and sunshine, which are then input into the AI ​​model along with land information.

[0296] Step 3:

[0297] The server analyzes acquired land information and weather data using an AI model. As part of data processing, weather data is converted into time-series features and combined with land information for analysis. This analysis determines the optimal type of fertilizer, its application schedule, and cultivation method.

[0298] Step 4:

[0299] The server uses an AI model to generate analysis results in the form of prompts, which are then sent to the user. An example of a prompt might be, "Generate the optimal scenario for tomato cultivation based on the given weather data." The user checks this notification using a dedicated app.

[0300] Step 5:

[0301] Sensors attached to the terminal collect environmental data such as temperature, humidity, and sunlight in real time, and continuously transmit this data to a server. The server collects the real-time data and analyzes whether the environmental conditions are optimal.

[0302] Step 6:

[0303] If the server determines that environmental conditions need to be adjusted as needed, it sends instructions to the control device attached to the machinery. This triggers the automatic application of fertilizer, and the operation of cooling and humidifying systems, maintaining the optimal cultivation environment.

[0304] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0305] This invention is a system that not only proposes optimal fertilizers and cultivation methods based on the user's land information and weather data, but also incorporates an emotion engine that recognizes the user's emotions. This system optimizes the user's work environment by adjusting advice based on their emotional state, thereby improving the work experience.

[0306] First, the user inputs land information (location, soil characteristics, types of crops to be cultivated, etc.) through a dedicated app. The server obtains the necessary meteorological data from an external meteorological database, performs analysis using AI based on this information, and proposes the optimal fertilizer and cultivation method to the user. As a result, the user can know which fertilizer to use when and how to grow the crops.

[0307] Furthermore, the user's smart terminal is equipped with emotion recognition sensors such as a camera and a microphone, and the emotion engine can analyze the user's emotion in real time. For example, when the user is feeling stressed, the emotion engine sends that information to the server, and the server can re - propose a cultivation method and schedule that are less burdensome for the user. Note that this information is obtained only with the explicit consent of the user, and privacy is protected.

[0308] As a specific example, consider the case where a user growing tomatoes is in a busy work period. Suppose it is recognized that the stress level has increased from the information input by the user using the emotion recognition sensor. At this time, the server provides a cultivation plan that increases, for example, automated work procedures based on the information from the emotion engine. Also, the notification frequency and content of the app are adjusted according to the user's situation. As a result, the user can advance the growth of the crops under appropriate management without feeling pressured.

[0309] In this way, the system of the present invention analyzes the user's emotional state in addition to the data of land and meteorological conditions and provides appropriate feedback. Therefore, as an integrated agricultural management platform, it contributes to the improvement of efficiency and usability.

[0310] The following describes the processing flow.

[0311] Step 1:

[0312] The user launches a dedicated app and inputs land information (location, soil pH, cultivated crops, etc.) to send it to the server. At the same time, the user's smart device prepares to capture their emotional state in real time using its camera and microphone.

[0313] Step 2:

[0314] The server accesses an external weather database based on the received land information and retrieves weather data for the specified area. The retrieved data includes historical temperature, precipitation, and solar radiation.

[0315] Step 3:

[0316] The server uses an AI model to analyze received land information and weather data to determine the optimal fertilizer and cultivation methods. The results of this analysis are notified to the user through a dedicated app.

[0317] Step 4:

[0318] The emotion engine built into the device recognizes the user's emotional state in real time based on data from the user's camera and microphone. The results of the emotion recognition are sent to a server.

[0319] Step 5:

[0320] The server analyzes the user's emotional data from the emotion engine and, based on their emotional state (e.g., stress level), dynamically adjusts the cultivation schedule and fertilizer application methods to provide new suggestions. The user is notified of the changed suggestions through their dedicated app.

[0321] Step 6:

[0322] Users can review new cultivation suggestions that are sensitive to their feelings and modify their work plans as needed. Furthermore, by adjusting the notification content, users can accept feasible cultivation methods that reduce their burden.

[0323] Step 7:

[0324] The sensors on the device continuously monitor the crop's growing environment and transmit environmental data (temperature, humidity, solar radiation, etc.) to the server. The server then analyzes the environmental conditions in real time and adjusts them remotely or automatically as needed.

[0325] This series of processes allows users to cultivate crops efficiently, comfortably, and with consideration for emotions and the environment.

[0326] (Example 2)

[0327] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0328] Conventional agricultural management systems offer cultivation method suggestions based on land information and weather data, but they lack the ability to adaptively modify plans that take into account the emotional state of the workers, resulting in a failure to alleviate the psychological burden on users. Furthermore, they lacked adequate visualization based on crop growth status and changes in the emotional state of the workers, making it difficult for users to manage their operations flexibly according to the situation.

[0329] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0330] In this invention, the server includes means for acquiring user land information and weather data, means for analyzing the acquired land information and weather data and proposing optimal fertilizers and cultivation methods, and means for acquiring and analyzing worker emotional data using sensors. This enables users to flexibly and efficiently manage agriculture by adjusting cultivation plans and schedules according to the emotional state of the workers and through visualization.

[0331] A "user" is an entity that uses this system to provide land information and sentiment data, and receives suggested cultivation methods and schedules.

[0332] "Land information" refers to physical and environmental data necessary for agricultural activities, such as location, soil characteristics, and the types of crops to be cultivated.

[0333] "Weather data" refers to data related to current and predicted weather conditions obtained from external databases.

[0334] "Fertilizer" refers to nutrients that are given to the soil and plants to promote plant growth.

[0335] "Cultivation methods" refer to the specific procedures and techniques used for growing and managing plants.

[0336] A "sensor" is a device used to detect the environment or the emotional state of workers, and this system uses cameras and microphones.

[0337] "Emotional data" refers to data that reflects the emotional state of workers, including stress, joy, and other factors.

[0338] A "generative AI model" refers to a form of artificial intelligence that analyzes large amounts of data and generates new proposals or conclusions based on that information.

[0339] A "prompt" refers to an instruction or question used when inputting information into a generative AI model.

[0340] The embodiment of this invention is configured as an integrated system for optimizing the user's agricultural experience. Its specific elements are described below.

[0341] Users input land information using a dedicated application. This information includes location data, soil characteristics, and the type of crops they plan to cultivate. Furthermore, users can provide emotional data through a smart device. This device is equipped with emotion recognition sensors such as a camera and microphone, which analyze the user's facial expressions and voice to detect their emotional state.

[0342] After receiving land information from the user, the server retrieves the latest weather data from an external weather database. This weather database could be, for example, a public weather information service. The server then analyzes this information using a generative AI model to generate optimal fertilizers and cultivation methods. This analysis determines which fertilizer is most effective and how crops should be cultivated.

[0343] Regarding emotional data, the emotion engine analyzes information collected from the device to evaluate the user's real-time mental state. Based on this emotional state, the server prompts the generative AI model, suggesting adjusted cultivation methods and schedules to reduce the user's psychological burden. An example of a prompt used here might be, "If the user's emotions are stress-based, please provide the optimal work procedure."

[0344] This system provides users with flexible and personalized advice, enabling them to improve the efficiency of their farm management. Furthermore, users can visually review the feedback provided through the app, allowing for quick and appropriate action based on the situation.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] Users input land information (location, soil characteristics, crop type, etc.) using a dedicated app. This input data is sent to the server. Upon receiving this information, the server stores it in a database and uses it as basic data necessary for obtaining subsequent weather data.

[0348] Step 2:

[0349] The server retrieves current and predicted weather data from an external weather database. The input here is the user's land information obtained in step 1. The server uses this data to request weather information for the relevant area via an API. The output includes temperature, precipitation, sunshine hours, etc.

[0350] Step 3:

[0351] The server processes land information and weather data in a unified manner and performs analysis using a generated AI model. The input data consists of land information and weather data, and the output is suggestions regarding the optimal type and timing of fertilizer application and cultivation procedures. Specifically, the AI ​​model analyzes the data and performs calculations based on past success stories and scientific evidence.

[0352] Step 4:

[0353] The device uses a camera and microphone to collect user emotional data in real time. Input includes the user's facial expressions and voice data, which are then analyzed by an emotion engine. Output includes stress levels and emotional tone. The emotional state is then transmitted to a server.

[0354] Step 5:

[0355] The server considers emotional data and previously generated cultivation suggestions, inputs prompt sentences into the AI ​​model, and adjusts the suggestions for the user. The inputs at this time are the emotional state and cultivation suggestions. As output, a cultivation plan and schedule adjusted to reduce the user's psychological burden are generated.

[0356] Step 6:

[0357] The server sends the final suggestions to the user's device, making them available for review within the app. The input is the server's refined suggestion data, and the output is visual feedback presented in the user interface. This allows users to take immediate, practical action.

[0358] (Application Example 2)

[0359] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0360] In recent years, the optimization of cultivation methods using land and weather information has become increasingly important in the agricultural sector. However, these systems cannot adjust work processes while considering the emotional state of workers, which can lead to increased workload. Similarly, in factories, there is a demand for systems that enable efficient production while reducing worker fatigue and stress. Furthermore, the difficulty in continuously adjusting fertilization and cultivation suggestions necessitates real-time environmental adaptation.

[0361] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0362] In this invention, the server includes means for acquiring user area information and weather information, means for analyzing the acquired area information and weather information and suggesting optimal nutrients and cultivation methods, and means for analyzing the worker's emotional state and dynamically adjusting the work process. This makes it possible for workers to work in an environment that is less stressful for them while simultaneously implementing the optimal cultivation method.

[0363] "User domain information" refers to information about a specific user's geographical location and its characteristics.

[0364] "Weather information" refers to information about environmental conditions such as weather, temperature, and humidity, obtained from external databases.

[0365] "Nutrient solutions" are materials used to promote plant growth and are suggested as part of fertilization.

[0366] "Cultivation methods" refer to the specific processes and procedures for growing crops and other plants, which are adjusted to achieve the optimal growing environment.

[0367] A "sensing device" refers to a device used to collect specific environmental information or emotional states, and may include cameras, microphones, and sensors.

[0368] "Worker's emotional state" refers to information about the psychological state of workers, analyzed using machine learning models or similar methods.

[0369] A "work process" refers to a series of tasks required in industrial or agricultural settings, and is subject to dynamic adjustment.

[0370] The system for realizing this application primarily consists of a server, a user's smart terminal, and sensing devices within the factory. The server analyzes area information provided by the user and weather information obtained from an external database. This analysis utilizes a database management system (e.g., PostgreSQL) and a machine learning framework (e.g., TensorFlow). Based on the analysis results, the system calculates and presents the optimal nutrients and cultivation methods to the user.

[0371] The user's smart device is equipped with a camera and microphone to analyze their emotional state, and these are used as sensing devices. The data obtained from these sensing devices is processed by an emotion recognition API (e.g., Affectiva API) to analyze the worker's emotional state in real time. Through smart glasses, the user can receive and implement adjustments to suggested cultivation methods and work processes.

[0372] For example, if the system detects that a factory worker is fatigued, the server will automate the work process and make adjustments to reduce the burden. It will also suggest break times and provide support to reduce worker stress.

[0373] An example of a prompt for a generative AI model could be an instruction such as, "Based on the generated data, please suggest an appropriate work process based on the worker's emotional state and work environment data." This prompt could then be used to adjust the actual work process and improve cultivation methods.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server receives area information from the user's smart device. This includes location information, soil characteristics, and the types of plants being cultivated. The received area information is stored in a database on the server.

[0377] Step 2:

[0378] The server accesses an external weather database to retrieve relevant weather information. Based on the retrieved weather information and regional information, it uses a machine learning framework (e.g., TensorFlow) to calculate the optimal nutrients and cultivation methods, and presents the results to the user. At this stage, data analysis and model calculations are performed.

[0379] Step 3:

[0380] The user's smart device uses sensing devices to detect the worker's emotional state in real time. This involves using a camera and microphone to analyze emotional data through an emotion recognition API (e.g., Affectiva API). The analysis results are then transmitted to a processing system within the device.

[0381] Step 4:

[0382] The terminal sends data on the worker's emotional state to the server. Based on this data, the server uses an AI model to generate appropriate work process adjustments and creates an optimized work schedule for the worker. The generated adjustments are then fed back to the user's smart terminal.

[0383] Step 5:

[0384] The user reviews the work process and cultivation methods presented through the terminal and performs the tasks as needed. Data is presented to the user visually and interactively on the terminal, including suggestions for automated procedures and break times.

[0385] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0386] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0387] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0391] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0392] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0393] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0394] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0395] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0396] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0397] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0398] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0399] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0400] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0401] This invention is a system that proposes optimal fertilizers and cultivation methods based on the user's land information and weather data, and manages the crop growth environment in real time. Specific embodiments for carrying out this invention are described below.

[0402] First, users input information about the land they manage through a dedicated app. This land information includes location, soil pH value, area, and type of crop to be cultivated. This information is sent to a server and used as basic data for analysis. The server accesses an external weather database to obtain historical and current weather data for the specified area. This allows for a detailed understanding of cultivation conditions for a particular plot of land.

[0403] The server uses an AI model to analyze acquired land information and weather data. This analysis determines the optimal type of fertilizer and its application schedule. It also suggests appropriate cultivation methods for each crop type. These suggestions are notified to the user via a dedicated app for confirmation. This allows the user to select the appropriate fertilizer and implement effective cultivation methods.

[0404] Furthermore, various sensors are attached to the terminal to collect environmental data such as temperature, humidity, and sunlight in real time. This environmental data is transferred to a server and analyzed by AI. If the analysis reveals that the environmental conditions are not optimal, a command is sent to the terminal, and adjustments are made automatically. For example, if the temperature exceeds the appropriate range, the cooling system will activate, and if the humidity is too low, the humidifier will start.

[0405] As a concrete example, let's consider the case of cultivating tomatoes in Hokkaido. The user registers with a dedicated app and provides land information and tomato cultivation information. The server analyzes the previous year's weather data for Hokkaido combined with current weather data. Based on the analysis, appropriate fertilizers are suggested to adjust the soil acidity, and cultivation methods adapted to the cool climate are indicated. In addition, based on environmental data monitored by sensors, automatic adjustments are made to maintain the optimal temperature and humidity range for tomato growth.

[0406] Thus, the system of the present invention uses a data-driven approach to streamline agricultural activities and support environmentally friendly and sustainable crop cultivation.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] Users input their land information using a dedicated app. This information includes location, soil pH value, area, and type of crop to be cultivated. This data is then transmitted to a server via the internet.

[0410] Step 2:

[0411] The server connects to an external weather database to retrieve historical and current weather data for a specified location. The retrieved data includes temperature, precipitation, and solar radiation, and the data is prepared for analysis.

[0412] Step 3:

[0413] The server uses an AI model to analyze the transmitted land information and acquired weather data. Based on the analysis results, it determines the optimal type of fertilizer and cultivation method, and suggests the timing and amount of fertilizer application. This information is sent in real time to the user's dedicated app.

[0414] Step 4:

[0415] Users receive suggestions through the app and apply fertilizer and set cultivation conditions according to those suggestions. Users can either refer to the AI's recommendations or make adjustments based on their own judgment.

[0416] Step 5:

[0417] Sensors attached to the device monitor the temperature, humidity, and sunlight levels of the cultivation environment in real time. The collected data is transmitted from the device to a server, and changes in the environment are tracked.

[0418] Step 6:

[0419] The server analyzes environmental data in real time and sends appropriate control instructions to terminals if it detects environmental changes that fall outside the standard range. For example, this can activate a cooling system if the temperature is high or start a humidifier if the humidity is low.

[0420] Step 7:

[0421] Users can use the app to check crop growth status and environmental data in real time. The visualized data allows users to determine if adjustments to cultivation conditions are necessary and take action as needed.

[0422] This processing flow enables users to efficiently cultivate crops in an environment suited to their needs, thereby realizing sustainable agriculture.

[0423] (Example 1)

[0424] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0425] Modern agriculture requires selecting appropriate nutrients and cultivation methods in response to changing weather conditions and soil characteristics to optimize crop growth. However, this requires specialized knowledge and considerable effort, making efficient farming difficult. Furthermore, the lack of real-time environmental adjustments can negatively impact crop growth.

[0426] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0427] In this invention, the server includes means for users to input and receive information about the land, means for acquiring past and present atmospheric condition data for the region from an external atmospheric condition information source, and means for analyzing the acquired land information and atmospheric condition data using generation AI technology to propose optimal nutrients and cultivation methods. As a result, users can perform efficient and effective farming work even without specialized knowledge, and can further optimize the growth environment by adjusting the surrounding conditions of the crops in real time.

[0428] "A means by which users input and receive information about land" refers to a process in which users input data related to the land they manage through a dedicated interface, and a server, which is a central processing unit, receives that data.

[0429] "Means of obtaining historical and current atmospheric condition data for a region from external atmospheric condition information sources" refers to the process of obtaining historical and current weather-related data for a specified region from external weather data providers or databases.

[0430] "A means of analyzing acquired land information and atmospheric condition data using generative AI technology to propose optimal nutrients and cultivation methods" refers to a process of analyzing collected land-related information and weather condition data using generative artificial intelligence, and based on this, providing the most appropriate fertilizer selection and cultivation techniques.

[0431] "Means of informing users of proposed nutrients and cultivation methods" refers to the process of notifying users of the proposed fertilizers and cultivation techniques obtained as a result of the analysis.

[0432] "Methods for collecting real-time data on the surrounding conditions of crops using various measuring devices" refers to the process of collecting environmental data related to crop growth in real time using various sensors.

[0433] "Means for analyzing collected ambient situation data and generating automated commands for situation adjustment" refers to a process that analyzes collected environmental data and generates automated commands for necessary environmental adjustments based on the results.

[0434] This invention is a system that uses the user's land information and weather data to automatically suggest optimal nutrients and cultivation techniques in agriculture, and manages the crop growth environment in real time. The following describes specific embodiments of this invention.

[0435] First, users use a dedicated application to input information about the land they manage. This information includes the land's location coordinates, soil acidity, area, and the type of crops cultivated. This entered data is then transmitted to the server via the terminal.

[0436] The server makes API requests to external weather databases to retrieve historical and current weather information for the user-specified area. This process incorporates various weather parameters such as temperature, precipitation, and sunshine. The server then inputs this data into a generating AI model for data analysis.

[0437] This AI model learns from a vast amount of historical data and generates optimal farming methods under specific conditions. The server then determines the optimal nutrients, their application schedule, and even the best cultivation methods for different crop types. This determined information is then communicated to the user through a dedicated app.

[0438] The user's device is equipped with various sensors that monitor environmental data such as temperature, humidity, and sunlight in real time. This data is periodically sent to a server and analyzed by an AI model. If the analysis determines that the environment is not optimal for crops, the server sends an automatic adjustment command to the device. This command controls cooling and humidifying devices as needed.

[0439] As a concrete example, consider growing tomatoes in Hokkaido. The user inputs land and cultivation information into the app, and the server analyzes the data by combining weather data from Hokkaido in the previous year with current data. Based on the analysis results, appropriate fertilizers are suggested, and cultivation methods suitable for the cool climate are indicated. At the same time, based on data collected by sensors, the server automatically adjusts to maintain the temperature and humidity within the optimal range.

[0440] An example of a prompt message for a generative AI model is as follows:

[0441] "Based on tomato cultivation information in Hokkaido provided by the user, along with historical and current weather data, propose the optimal fertilizer and cultivation method. Furthermore, please explain the procedures and methods for building a system that automatically adjusts the growing environment based on real-time environmental data."

[0442] Thus, the system of the present invention employs a data-driven approach to streamline agricultural activities and support environmentally friendly and sustainable agriculture.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] Users input land information through a dedicated app. Specifically, they obtain land location information using GPS, measure and input soil pH values, and select the land area and the type of crop they are considering cultivating. This data is sent from the app to a server. The entered information is stored on the server as numerical data and serves as the basis for analysis.

[0446] Step 2:

[0447] Based on the received land information, the server sends an API request to an external weather database. This retrieves weather data for the past 10 years and current weather data for the specified area. The retrieved data is stored on the server and structured as numerical data of atmospheric conditions such as temperature, precipitation, and sunshine.

[0448] Step 3:

[0449] The server uses an AI model that generates data based on acquired land information and weather data as input, and performs data analysis. The AI ​​model processes this data to generate the optimal combination of nutrients, application schedule, and appropriate cultivation methods. The analysis results are ready to be provided to the user as text data.

[0450] Step 4:

[0451] The server notifies the user of suggestions generated by the AI ​​model via a dedicated app. The user reviews the suggested nutrients and cultivation methods and incorporates them into their farming plan as needed. The suggestions are displayed in a calendar or list format tailored to the user's work schedule.

[0452] Step 5:

[0453] The terminal is equipped with various sensors to measure temperature, humidity, sunlight, and other parameters. These sensors collect environmental data in real time and send the generated data to a server. The environmental data is updated every five minutes.

[0454] Step 6:

[0455] The server performs real-time analysis based on the collected environmental data. If the analysis reveals that the environmental conditions are not optimal, the system generates automated commands for adjustment. For example, if the temperature exceeds a set range, it sends a command to the terminal to activate the cooling system. This ensures that optimal environmental conditions are maintained.

[0456] (Application Example 1)

[0457] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0458] In traditional agricultural practices, optimizing crop growth environments requires considerable effort and experience, and this limitation is particularly pronounced when dealing with vast farmlands. As a result, problems such as reduced yields and decreased quality due to the inability to apply fertilizers and adjust environmental conditions in a timely manner have arisen.

[0459] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0460] In this invention, the server includes means for acquiring user land information and weather data, means for analyzing the acquired land information and weather data and proposing optimal fertilizers and cultivation methods, means for collecting crop environmental data in real time using sensors, and means for automatically performing fertilizer application and environmental adjustment via a control device installed on the machinery. This enables real-time environmental management and the execution of optimal cultivation processes even over a wide area of ​​farmland.

[0461] "User land information" refers to information about the geographical area managed by the user, including details such as its location, soil properties, area, and the types of crops cultivated there.

[0462] "Weather data" refers to information about past and present weather conditions in a specific region, including data such as temperature, precipitation, sunshine, and wind speed.

[0463] "Means for proposing fertilizers and cultivation methods" refers to a system that analyzes acquired land information and weather data and, based on the results, indicates the optimal fertilizers and cultivation methods.

[0464] A "sensor" is a measuring device used to collect environmental information such as temperature, humidity, sunlight intensity, and soil pH values ​​in real time.

[0465] "Means for adjusting environmental conditions" refer to devices and systems that control machinery and systems based on environmental data collected by sensors to maintain optimal environmental conditions.

[0466] A "control device" is a device installed in machinery that automatically performs tasks such as fertilizer application and temperature / humidity control.

[0467] "Means of visualizing and providing information to users in real time" refers to a function that instantly displays crop growth status and environmental data visually, providing it in a state where users can easily check it.

[0468] To implement this invention, a terminal is first required to acquire information about the land owned by the user. The user uses this terminal to input detailed data such as location information, soil pH value, area, and type of crop to be cultivated. This information is transmitted to a server and used as basic data for analysis.

[0469] The server accesses external weather databases to collect historical and current weather data for the relevant region. This data includes temperature, precipitation, sunshine, and other information. Based on this data, the server uses an AI model to analyze it and suggest optimal fertilizers and cultivation methods.

[0470] The analysis results are provided to the user through prompts generated using a generative AI model. For example, a prompt such as "Generate the optimal scenario for tomato cultivation based on the given weather data" is used.

[0471] Mechanical devices equipped with sensors monitor crop growth environments in real time. Data such as temperature, humidity, and sunlight are collected and transmitted to a server. The server analyzes this data and sends instructions to a control unit to maintain optimal environmental conditions. This control unit operates the automatic application of fertilizer and environmental adjustments.

[0472] As a concrete example, consider the case of cultivating tomatoes on a farm in Hokkaido. The user provides land information and tomato cultivation information through an app, and the server analyzes this information in combination with weather data. Appropriate fertilizers and cultivation methods are suggested, and environmental sensors monitor the temperature and humidity range and make adjustments as needed. This enables the user to achieve efficient and sustainable agriculture.

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] Users use their devices to input information about the land they manage. Specifically, they record location information, soil pH value, area, and types of crops cultivated in a dedicated app. This input data is sent to a server and used as basic data for analysis.

[0476] Step 2:

[0477] The server accesses an external weather database to retrieve historical and current weather data for the target area. The retrieved data includes temperature, precipitation, and sunshine, which are then input into the AI ​​model along with land information.

[0478] Step 3:

[0479] The server analyzes acquired land information and weather data using an AI model. As part of data processing, weather data is converted into time-series features and combined with land information for analysis. This analysis determines the optimal type of fertilizer, its application schedule, and cultivation method.

[0480] Step 4:

[0481] The server uses an AI model to generate analysis results in the form of prompts, which are then sent to the user. An example of a prompt might be, "Generate the optimal scenario for tomato cultivation based on the given weather data." The user checks this notification using a dedicated app.

[0482] Step 5:

[0483] Sensors attached to the terminal collect environmental data such as temperature, humidity, and sunlight in real time, and continuously transmit this data to a server. The server collects the real-time data and analyzes whether the environmental conditions are optimal.

[0484] Step 6:

[0485] If the server determines that environmental conditions need to be adjusted as needed, it sends instructions to the control device attached to the machinery. This triggers the automatic application of fertilizer, and the operation of cooling and humidifying systems, maintaining the optimal cultivation environment.

[0486] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0487] This invention is a system that not only proposes optimal fertilizers and cultivation methods based on the user's land information and weather data, but also incorporates an emotion engine that recognizes the user's emotions. This system optimizes the user's work environment by adjusting advice based on their emotional state, thereby improving the work experience.

[0488] First, the user inputs land information (location, soil characteristics, type of crop to be grown, etc.) through a dedicated app. The server retrieves necessary weather data from an external weather database and uses AI to analyze this information, suggesting the optimal fertilizer and cultivation method to the user. This allows the user to understand which fertilizer to use, when to use it, and how to grow their crops.

[0489] Furthermore, the user's smart device is equipped with emotion recognition sensors such as a camera and microphone, allowing the emotion engine to analyze the user's emotions in real time. For example, if the user is feeling stressed, the emotion engine sends that information to the server, which can then suggest cultivation methods and schedules that are less burdensome for the user. This information is only obtained with the user's explicit consent, and their privacy is protected.

[0490] As a concrete example, consider a user who is growing tomatoes and is experiencing a busy period at work. Suppose the system detects that the user is experiencing high stress levels based on information entered using an emotion recognition sensor. In this case, the server uses the information from the emotion engine to provide a cultivation plan that, for example, increases the number of tasks that can be automated. Furthermore, the frequency and content of app notifications are adjusted according to the user's situation. This allows the user to grow their crops under appropriate management without feeling pressured.

[0491] Thus, the system of the present invention analyzes the user's emotional state in addition to land and weather condition data to provide appropriate feedback. Therefore, as a comprehensive agricultural management platform, it contributes to improving efficiency and usability.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The user launches a dedicated app and inputs land information (location, soil pH, cultivated crops, etc.) to send it to the server. At the same time, the user's smart device prepares to capture their emotional state in real time using its camera and microphone.

[0495] Step 2:

[0496] The server accesses an external weather database based on the received land information and retrieves weather data for the specified area. The retrieved data includes historical temperature, precipitation, and solar radiation.

[0497] Step 3:

[0498] The server uses an AI model to analyze received land information and weather data to determine the optimal fertilizer and cultivation methods. The results of this analysis are notified to the user through a dedicated app.

[0499] Step 4:

[0500] The emotion engine built into the device recognizes the user's emotional state in real time based on data from the user's camera and microphone. The results of the emotion recognition are sent to a server.

[0501] Step 5:

[0502] The server analyzes the user's emotional data from the emotion engine and, based on their emotional state (e.g., stress level), dynamically adjusts the cultivation schedule and fertilizer application methods to provide new suggestions. The user is notified of the changed suggestions through their dedicated app.

[0503] Step 6:

[0504] Users can review new cultivation suggestions that are sensitive to their feelings and modify their work plans as needed. Furthermore, by adjusting the notification content, users can accept feasible cultivation methods that reduce their burden.

[0505] Step 7:

[0506] The sensors on the device continuously monitor the crop's growing environment and transmit environmental data (temperature, humidity, solar radiation, etc.) to the server. The server then analyzes the environmental conditions in real time and adjusts them remotely or automatically as needed.

[0507] This series of processes allows users to cultivate crops efficiently, comfortably, and with consideration for emotions and the environment.

[0508] (Example 2)

[0509] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0510] Conventional agricultural management systems offer cultivation method suggestions based on land information and weather data, but they lack the ability to adaptively modify plans that take into account the emotional state of the workers, resulting in a failure to alleviate the psychological burden on users. Furthermore, they lacked adequate visualization based on crop growth status and changes in the emotional state of the workers, making it difficult for users to manage their operations flexibly according to the situation.

[0511] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0512] In this invention, the server includes means for acquiring user land information and weather data, means for analyzing the acquired land information and weather data and proposing optimal fertilizers and cultivation methods, and means for acquiring and analyzing worker emotional data using sensors. This enables users to flexibly and efficiently manage agriculture by adjusting cultivation plans and schedules according to the emotional state of the workers and through visualization.

[0513] A "user" is an entity that uses this system to provide land information and sentiment data, and receives suggested cultivation methods and schedules.

[0514] "Land information" refers to physical and environmental data necessary for agricultural activities, such as location, soil characteristics, and the types of crops to be cultivated.

[0515] "Weather data" refers to data related to current and predicted weather conditions obtained from external databases.

[0516] "Fertilizer" refers to nutrients that are given to the soil and plants to promote plant growth.

[0517] "Cultivation methods" refer to the specific procedures and techniques used for growing and managing plants.

[0518] A "sensor" is a device used to detect the environment or the emotional state of workers, and this system uses cameras and microphones.

[0519] "Emotional data" refers to data that reflects the emotional state of workers, including stress, joy, and other factors.

[0520] A "generative AI model" refers to a form of artificial intelligence that analyzes large amounts of data and generates new proposals or conclusions based on that information.

[0521] A "prompt" refers to an instruction or question used when inputting information into a generative AI model.

[0522] The embodiment of this invention is configured as an integrated system for optimizing the user's agricultural experience. Its specific elements are described below.

[0523] Users input land information using a dedicated application. This information includes location data, soil characteristics, and the type of crops they plan to cultivate. Furthermore, users can provide emotional data through a smart device. This device is equipped with emotion recognition sensors such as a camera and microphone, which analyze the user's facial expressions and voice to detect their emotional state.

[0524] After receiving land information from the user, the server retrieves the latest weather data from an external weather database. This weather database could be, for example, a public weather information service. The server then analyzes this information using a generative AI model to generate optimal fertilizers and cultivation methods. This analysis determines which fertilizer is most effective and how crops should be cultivated.

[0525] Regarding emotional data, the emotion engine analyzes information collected from the device to evaluate the user's real-time mental state. Based on this emotional state, the server prompts the generative AI model, suggesting adjusted cultivation methods and schedules to reduce the user's psychological burden. An example of a prompt used here might be, "If the user's emotions are stress-based, please provide the optimal work procedure."

[0526] This system provides users with flexible and personalized advice, enabling them to improve the efficiency of their farm management. Furthermore, users can visually review the feedback provided through the app, allowing for quick and appropriate action based on the situation.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] Users input land information (location, soil characteristics, crop type, etc.) using a dedicated app. This input data is sent to the server. Upon receiving this information, the server stores it in a database and uses it as basic data necessary for obtaining subsequent weather data.

[0530] Step 2:

[0531] The server retrieves current and predicted weather data from an external weather database. The input here is the user's land information obtained in step 1. The server uses this data to request weather information for the relevant area via an API. The output includes temperature, precipitation, sunshine hours, etc.

[0532] Step 3:

[0533] The server processes land information and weather data in a unified manner and performs analysis using a generated AI model. The input data consists of land information and weather data, and the output is suggestions regarding the optimal type and timing of fertilizer application and cultivation procedures. Specifically, the AI ​​model analyzes the data and performs calculations based on past success stories and scientific evidence.

[0534] Step 4:

[0535] The device uses a camera and microphone to collect user emotional data in real time. Input includes the user's facial expressions and voice data, which are then analyzed by an emotion engine. Output includes stress levels and emotional tone. The emotional state is then transmitted to a server.

[0536] Step 5:

[0537] The server considers emotional data and previously generated cultivation suggestions, inputs prompt sentences into the AI ​​model, and adjusts the suggestions for the user. The inputs at this time are the emotional state and cultivation suggestions. As output, a cultivation plan and schedule adjusted to reduce the user's psychological burden are generated.

[0538] Step 6:

[0539] The server sends the final suggestions to the user's device, making them available for review within the app. The input is the server's refined suggestion data, and the output is visual feedback presented in the user interface. This allows users to take immediate, practical action.

[0540] (Application Example 2)

[0541] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0542] In recent years, the optimization of cultivation methods using land and weather information has become increasingly important in the agricultural sector. However, these systems cannot adjust work processes while considering the emotional state of workers, which can lead to increased workload. Similarly, in factories, there is a demand for systems that enable efficient production while reducing worker fatigue and stress. Furthermore, the difficulty in continuously adjusting fertilization and cultivation suggestions necessitates real-time environmental adaptation.

[0543] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0544] In this invention, the server includes means for acquiring user area information and weather information, means for analyzing the acquired area information and weather information and suggesting optimal nutrients and cultivation methods, and means for analyzing the worker's emotional state and dynamically adjusting the work process. This makes it possible for workers to work in an environment that is less stressful for them while simultaneously implementing the optimal cultivation method.

[0545] "User domain information" refers to information about a specific user's geographical location and its characteristics.

[0546] "Weather information" refers to information about environmental conditions such as weather, temperature, and humidity, obtained from external databases.

[0547] "Nutrient solutions" are materials used to promote plant growth and are suggested as part of fertilization.

[0548] "Cultivation methods" refer to the specific processes and procedures for growing crops and other plants, which are adjusted to achieve the optimal growing environment.

[0549] A "sensing device" refers to a device used to collect specific environmental information or emotional states, and may include cameras, microphones, and sensors.

[0550] "Worker's emotional state" refers to information about the psychological state of workers, analyzed using machine learning models or similar methods.

[0551] A "work process" refers to a series of tasks required in industrial or agricultural settings, and is subject to dynamic adjustment.

[0552] The system for realizing this application primarily consists of a server, a user's smart terminal, and sensing devices within the factory. The server analyzes area information provided by the user and weather information obtained from an external database. This analysis utilizes a database management system (e.g., PostgreSQL) and a machine learning framework (e.g., TensorFlow). Based on the analysis results, the system calculates and presents the optimal nutrients and cultivation methods to the user.

[0553] The user's smart device is equipped with a camera and microphone to analyze their emotional state, and these are used as sensing devices. The data obtained from these sensing devices is processed by an emotion recognition API (e.g., Affectiva API) to analyze the worker's emotional state in real time. Through smart glasses, the user can receive and implement adjustments to suggested cultivation methods and work processes.

[0554] For example, if the system detects that a factory worker is fatigued, the server will automate the work process and make adjustments to reduce the burden. It will also suggest break times and provide support to reduce worker stress.

[0555] An example of a prompt for a generative AI model could be an instruction such as, "Based on the generated data, please suggest an appropriate work process based on the worker's emotional state and work environment data." This prompt could then be used to adjust the actual work process and improve cultivation methods.

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The server receives area information from the user's smart device. This includes location information, soil characteristics, and the types of plants being cultivated. The received area information is stored in a database on the server.

[0559] Step 2:

[0560] The server accesses an external weather database to retrieve relevant weather information. Based on the retrieved weather information and regional information, it uses a machine learning framework (e.g., TensorFlow) to calculate the optimal nutrients and cultivation methods, and presents the results to the user. At this stage, data analysis and model calculations are performed.

[0561] Step 3:

[0562] The user's smart device uses sensing devices to detect the worker's emotional state in real time. This involves using a camera and microphone to analyze emotional data through an emotion recognition API (e.g., Affectiva API). The analysis results are then transmitted to a processing system within the device.

[0563] Step 4:

[0564] The terminal sends data on the worker's emotional state to the server. Based on this data, the server uses an AI model to generate appropriate work process adjustments and creates an optimized work schedule for the worker. The generated adjustments are then fed back to the user's smart terminal.

[0565] Step 5:

[0566] The user reviews the work process and cultivation methods presented through the terminal and performs the tasks as needed. Data is presented to the user visually and interactively on the terminal, including suggestions for automated procedures and break times.

[0567] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0568] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0569] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0573] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0574] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0575] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0576] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0577] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0578] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0579] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0580] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0581] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0582] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0583] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0584] This invention is a system that proposes optimal fertilizers and cultivation methods based on the user's land information and weather data, and manages the crop growth environment in real time. Specific embodiments for carrying out this invention are described below.

[0585] First, users input information about the land they manage through a dedicated app. This land information includes location, soil pH value, area, and type of crop to be cultivated. This information is sent to a server and used as basic data for analysis. The server accesses an external weather database to obtain historical and current weather data for the specified area. This allows for a detailed understanding of cultivation conditions for a particular plot of land.

[0586] The server uses an AI model to analyze acquired land information and weather data. This analysis determines the optimal type of fertilizer and its application schedule. It also suggests appropriate cultivation methods for each crop type. These suggestions are notified to the user via a dedicated app for confirmation. This allows the user to select the appropriate fertilizer and implement effective cultivation methods.

[0587] Furthermore, various sensors are attached to the terminal to collect environmental data such as temperature, humidity, and sunlight in real time. This environmental data is transferred to a server and analyzed by AI. If the analysis reveals that the environmental conditions are not optimal, a command is sent to the terminal, and adjustments are made automatically. For example, if the temperature exceeds the appropriate range, the cooling system will activate, and if the humidity is too low, the humidifier will start.

[0588] As a concrete example, let's consider the case of cultivating tomatoes in Hokkaido. The user registers with a dedicated app and provides land information and tomato cultivation information. The server analyzes the previous year's weather data for Hokkaido combined with current weather data. Based on the analysis, appropriate fertilizers are suggested to adjust the soil acidity, and cultivation methods adapted to the cool climate are indicated. In addition, based on environmental data monitored by sensors, automatic adjustments are made to maintain the optimal temperature and humidity range for tomato growth.

[0589] Thus, the system of the present invention uses a data-driven approach to streamline agricultural activities and support environmentally friendly and sustainable crop cultivation.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] Users input their land information using a dedicated app. This information includes location, soil pH value, area, and type of crop to be cultivated. This data is then transmitted to a server via the internet.

[0593] Step 2:

[0594] The server connects to an external weather database to retrieve historical and current weather data for a specified location. The retrieved data includes temperature, precipitation, and solar radiation, and the data is prepared for analysis.

[0595] Step 3:

[0596] The server uses an AI model to analyze the transmitted land information and acquired weather data. Based on the analysis results, it determines the optimal type of fertilizer and cultivation method, and suggests the timing and amount of fertilizer application. This information is sent in real time to the user's dedicated app.

[0597] Step 4:

[0598] Users receive suggestions through the app and apply fertilizer and set cultivation conditions according to those suggestions. Users can either refer to the AI's recommendations or make adjustments based on their own judgment.

[0599] Step 5:

[0600] Sensors attached to the device monitor the temperature, humidity, and sunlight levels of the cultivation environment in real time. The collected data is transmitted from the device to a server, and changes in the environment are tracked.

[0601] Step 6:

[0602] The server analyzes environmental data in real time and sends appropriate control instructions to terminals if it detects environmental changes that fall outside the standard range. For example, this can activate a cooling system if the temperature is high or start a humidifier if the humidity is low.

[0603] Step 7:

[0604] Users can use the app to check crop growth status and environmental data in real time. The visualized data allows users to determine if adjustments to cultivation conditions are necessary and take action as needed.

[0605] This processing flow enables users to efficiently cultivate crops in an environment suited to their needs, thereby realizing sustainable agriculture.

[0606] (Example 1)

[0607] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0608] Modern agriculture requires selecting appropriate nutrients and cultivation methods in response to changing weather conditions and soil characteristics to optimize crop growth. However, this requires specialized knowledge and considerable effort, making efficient farming difficult. Furthermore, the lack of real-time environmental adjustments can negatively impact crop growth.

[0609] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0610] In this invention, the server includes means for users to input and receive information about the land, means for acquiring past and present atmospheric condition data for the region from an external atmospheric condition information source, and means for analyzing the acquired land information and atmospheric condition data using generation AI technology to propose optimal nutrients and cultivation methods. As a result, users can perform efficient and effective farming work even without specialized knowledge, and can further optimize the growth environment by adjusting the surrounding conditions of the crops in real time.

[0611] "A means by which users input and receive information about land" refers to a process in which users input data related to the land they manage through a dedicated interface, and a server, which is a central processing unit, receives that data.

[0612] "Means of obtaining historical and current atmospheric condition data for a region from external atmospheric condition information sources" refers to the process of obtaining historical and current weather-related data for a specified region from external weather data providers or databases.

[0613] "A means of analyzing acquired land information and atmospheric condition data using generative AI technology to propose optimal nutrients and cultivation methods" refers to a process of analyzing collected land-related information and weather condition data using generative artificial intelligence, and based on this, providing the most appropriate fertilizer selection and cultivation techniques.

[0614] "Means of informing users of proposed nutrients and cultivation methods" refers to the process of notifying users of the proposed fertilizers and cultivation techniques obtained as a result of the analysis.

[0615] "Methods for collecting real-time data on the surrounding conditions of crops using various measuring devices" refers to the process of collecting environmental data related to crop growth in real time using various sensors.

[0616] "Means for analyzing collected ambient situation data and generating automated commands for situation adjustment" refers to a process that analyzes collected environmental data and generates automated commands for necessary environmental adjustments based on the results.

[0617] This invention is a system that uses the user's land information and weather data to automatically suggest optimal nutrients and cultivation techniques in agriculture, and manages the crop growth environment in real time. The following describes specific embodiments of this invention.

[0618] First, users use a dedicated application to input information about the land they manage. This information includes the land's location coordinates, soil acidity, area, and the type of crops cultivated. This entered data is then transmitted to the server via the terminal.

[0619] The server makes API requests to external weather databases to retrieve historical and current weather information for the user-specified area. This process incorporates various weather parameters such as temperature, precipitation, and sunshine. The server then inputs this data into a generating AI model for data analysis.

[0620] This AI model learns from a vast amount of historical data and generates optimal farming methods under specific conditions. The server then determines the optimal nutrients, their application schedule, and even the best cultivation methods for different crop types. This determined information is then communicated to the user through a dedicated app.

[0621] The user's device is equipped with various sensors that monitor environmental data such as temperature, humidity, and sunlight in real time. This data is periodically sent to a server and analyzed by an AI model. If the analysis determines that the environment is not optimal for crops, the server sends an automatic adjustment command to the device. This command controls cooling and humidifying devices as needed.

[0622] As a concrete example, consider growing tomatoes in Hokkaido. The user inputs land and cultivation information into the app, and the server analyzes the data by combining weather data from Hokkaido in the previous year with current data. Based on the analysis results, appropriate fertilizers are suggested, and cultivation methods suitable for the cool climate are indicated. At the same time, based on data collected by sensors, the server automatically adjusts to maintain the temperature and humidity within the optimal range.

[0623] An example of a prompt message for a generative AI model is as follows:

[0624] "Based on tomato cultivation information in Hokkaido provided by the user, along with historical and current weather data, propose the optimal fertilizer and cultivation method. Furthermore, please explain the procedures and methods for building a system that automatically adjusts the growing environment based on real-time environmental data."

[0625] Thus, the system of the present invention employs a data-driven approach to streamline agricultural activities and support environmentally friendly and sustainable agriculture.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] Users input land information through a dedicated app. Specifically, they obtain land location information using GPS, measure and input soil pH values, and select the land area and the type of crop they are considering cultivating. This data is sent from the app to a server. The entered information is stored on the server as numerical data and serves as the basis for analysis.

[0629] Step 2:

[0630] Based on the received land information, the server sends an API request to an external weather database. This retrieves weather data for the past 10 years and current weather data for the specified area. The retrieved data is stored on the server and structured as numerical data of atmospheric conditions such as temperature, precipitation, and sunshine.

[0631] Step 3:

[0632] The server uses an AI model that generates data based on acquired land information and weather data as input, and performs data analysis. The AI ​​model processes this data to generate the optimal combination of nutrients, application schedule, and appropriate cultivation methods. The analysis results are ready to be provided to the user as text data.

[0633] Step 4:

[0634] The server notifies the user of suggestions generated by the AI ​​model via a dedicated app. The user reviews the suggested nutrients and cultivation methods and incorporates them into their farming plan as needed. The suggestions are displayed in a calendar or list format tailored to the user's work schedule.

[0635] Step 5:

[0636] The terminal is equipped with various sensors to measure temperature, humidity, sunlight, and other parameters. These sensors collect environmental data in real time and send the generated data to a server. The environmental data is updated every five minutes.

[0637] Step 6:

[0638] The server performs real-time analysis based on the collected environmental data. If the analysis reveals that the environmental conditions are not optimal, the system generates automated commands for adjustment. For example, if the temperature exceeds a set range, it sends a command to the terminal to activate the cooling system. This ensures that optimal environmental conditions are maintained.

[0639] (Application Example 1)

[0640] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0641] In traditional agricultural practices, optimizing crop growth environments requires considerable effort and experience, and this limitation is particularly pronounced when dealing with vast farmlands. As a result, problems such as reduced yields and decreased quality due to the inability to apply fertilizers and adjust environmental conditions in a timely manner have arisen.

[0642] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0643] In this invention, the server includes means for acquiring user land information and weather data, means for analyzing the acquired land information and weather data and proposing optimal fertilizers and cultivation methods, means for collecting crop environmental data in real time using sensors, and means for automatically performing fertilizer application and environmental adjustment via a control device installed on the machinery. This enables real-time environmental management and the execution of optimal cultivation processes even over a wide area of ​​farmland.

[0644] "User land information" refers to information about the geographical area managed by the user, including details such as its location, soil properties, area, and the types of crops cultivated there.

[0645] "Weather data" refers to information about past and present weather conditions in a specific region, including data such as temperature, precipitation, sunshine, and wind speed.

[0646] "Means for proposing fertilizers and cultivation methods" refers to a system that analyzes acquired land information and weather data and, based on the results, indicates the optimal fertilizers and cultivation methods.

[0647] A "sensor" is a measuring device used to collect environmental information such as temperature, humidity, sunlight intensity, and soil pH values ​​in real time.

[0648] "Means for adjusting environmental conditions" refer to devices and systems that control machinery and systems based on environmental data collected by sensors to maintain optimal environmental conditions.

[0649] A "control device" is a device installed in machinery that automatically performs tasks such as fertilizer application and temperature / humidity control.

[0650] "Means of visualizing and providing information to users in real time" refers to a function that instantly displays crop growth status and environmental data visually, providing it in a state where users can easily check it.

[0651] To implement this invention, a terminal is first required to acquire information about the land owned by the user. The user uses this terminal to input detailed data such as location information, soil pH value, area, and type of crop to be cultivated. This information is transmitted to a server and used as basic data for analysis.

[0652] The server accesses external weather databases to collect historical and current weather data for the relevant region. This data includes temperature, precipitation, sunshine, and other information. Based on this data, the server uses an AI model to analyze it and suggest optimal fertilizers and cultivation methods.

[0653] The analysis results are provided to the user through prompts generated using a generative AI model. For example, a prompt such as "Generate the optimal scenario for tomato cultivation based on the given weather data" is used.

[0654] Mechanical devices equipped with sensors monitor crop growth environments in real time. Data such as temperature, humidity, and sunlight are collected and transmitted to a server. The server analyzes this data and sends instructions to a control unit to maintain optimal environmental conditions. This control unit operates the automatic application of fertilizer and environmental adjustments.

[0655] As a concrete example, consider the case of cultivating tomatoes on a farm in Hokkaido. The user provides land information and tomato cultivation information through an app, and the server analyzes this information in combination with weather data. Appropriate fertilizers and cultivation methods are suggested, and environmental sensors monitor the temperature and humidity range and make adjustments as needed. This enables the user to achieve efficient and sustainable agriculture.

[0656] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0657] Step 1:

[0658] Users use their devices to input information about the land they manage. Specifically, they record location information, soil pH value, area, and types of crops cultivated in a dedicated app. This input data is sent to a server and used as basic data for analysis.

[0659] Step 2:

[0660] The server accesses an external weather database to retrieve historical and current weather data for the target area. The retrieved data includes temperature, precipitation, and sunshine, which are then input into the AI ​​model along with land information.

[0661] Step 3:

[0662] The server analyzes acquired land information and weather data using an AI model. As part of data processing, weather data is converted into time-series features and combined with land information for analysis. This analysis determines the optimal type of fertilizer, its application schedule, and cultivation method.

[0663] Step 4:

[0664] The server uses an AI model to generate analysis results in the form of prompts, which are then sent to the user. An example of a prompt might be, "Generate the optimal scenario for tomato cultivation based on the given weather data." The user checks this notification using a dedicated app.

[0665] Step 5:

[0666] Sensors attached to the terminal collect environmental data such as temperature, humidity, and sunlight in real time, and continuously transmit this data to a server. The server collects the real-time data and analyzes whether the environmental conditions are optimal.

[0667] Step 6:

[0668] If the server determines that environmental conditions need to be adjusted as needed, it sends instructions to the control device attached to the machinery. This triggers the automatic application of fertilizer, and the operation of cooling and humidifying systems, maintaining the optimal cultivation environment.

[0669] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0670] This invention is a system that not only proposes optimal fertilizers and cultivation methods based on the user's land information and weather data, but also incorporates an emotion engine that recognizes the user's emotions. This system optimizes the user's work environment by adjusting advice based on their emotional state, thereby improving the work experience.

[0671] First, the user inputs land information (location, soil characteristics, type of crop to be grown, etc.) through a dedicated app. The server retrieves necessary weather data from an external weather database and uses AI to analyze this information, suggesting the optimal fertilizer and cultivation method to the user. This allows the user to understand which fertilizer to use, when to use it, and how to grow their crops.

[0672] Furthermore, the user's smart device is equipped with emotion recognition sensors such as a camera and microphone, allowing the emotion engine to analyze the user's emotions in real time. For example, if the user is feeling stressed, the emotion engine sends that information to the server, which can then suggest cultivation methods and schedules that are less burdensome for the user. This information is only obtained with the user's explicit consent, and their privacy is protected.

[0673] As a concrete example, consider a user who is growing tomatoes and is experiencing a busy period at work. Suppose the system detects that the user is experiencing high stress levels based on information entered using an emotion recognition sensor. In this case, the server uses the information from the emotion engine to provide a cultivation plan that, for example, increases the number of tasks that can be automated. Furthermore, the frequency and content of app notifications are adjusted according to the user's situation. This allows the user to grow their crops under appropriate management without feeling pressured.

[0674] Thus, the system of the present invention analyzes the user's emotional state in addition to land and weather condition data to provide appropriate feedback. Therefore, as a comprehensive agricultural management platform, it contributes to improving efficiency and usability.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] The user launches a dedicated app and inputs land information (location, soil pH, cultivated crops, etc.) to send it to the server. At the same time, the user's smart device prepares to capture their emotional state in real time using its camera and microphone.

[0678] Step 2:

[0679] The server accesses an external weather database based on the received land information and retrieves weather data for the specified area. The retrieved data includes historical temperature, precipitation, and solar radiation.

[0680] Step 3:

[0681] The server uses an AI model to analyze received land information and weather data to determine the optimal fertilizer and cultivation methods. The results of this analysis are notified to the user through a dedicated app.

[0682] Step 4:

[0683] The emotion engine built into the device recognizes the user's emotional state in real time based on data from the user's camera and microphone. The results of the emotion recognition are sent to a server.

[0684] Step 5:

[0685] The server analyzes the user's emotional data from the emotion engine and, based on their emotional state (e.g., stress level), dynamically adjusts the cultivation schedule and fertilizer application methods to provide new suggestions. The user is notified of the changed suggestions through their dedicated app.

[0686] Step 6:

[0687] Users can review new cultivation suggestions that are sensitive to their feelings and modify their work plans as needed. Furthermore, by adjusting the notification content, users can accept feasible cultivation methods that reduce their burden.

[0688] Step 7:

[0689] The sensors on the device continuously monitor the crop's growing environment and transmit environmental data (temperature, humidity, solar radiation, etc.) to the server. The server then analyzes the environmental conditions in real time and adjusts them remotely or automatically as needed.

[0690] This series of processes allows users to cultivate crops efficiently, comfortably, and with consideration for emotions and the environment.

[0691] (Example 2)

[0692] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0693] Conventional agricultural management systems offer cultivation method suggestions based on land information and weather data, but they lack the ability to adaptively modify plans that take into account the emotional state of the workers, resulting in a failure to alleviate the psychological burden on users. Furthermore, they lacked adequate visualization based on crop growth status and changes in the emotional state of the workers, making it difficult for users to manage their operations flexibly according to the situation.

[0694] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0695] In this invention, the server includes means for acquiring user land information and weather data, means for analyzing the acquired land information and weather data and proposing optimal fertilizers and cultivation methods, and means for acquiring and analyzing worker emotional data using sensors. This enables users to flexibly and efficiently manage agriculture by adjusting cultivation plans and schedules according to the emotional state of the workers and through visualization.

[0696] A "user" is an entity that uses this system to provide land information and sentiment data, and receives suggested cultivation methods and schedules.

[0697] "Land information" refers to physical and environmental data necessary for agricultural activities, such as location, soil characteristics, and the types of crops to be cultivated.

[0698] "Weather data" refers to data related to current and predicted weather conditions obtained from external databases.

[0699] "Fertilizer" refers to nutrients that are given to the soil and plants to promote plant growth.

[0700] "Cultivation methods" refer to the specific procedures and techniques used for growing and managing plants.

[0701] A "sensor" is a device used to detect the environment or the emotional state of workers, and this system uses cameras and microphones.

[0702] "Emotional data" refers to data that reflects the emotional state of workers, including stress, joy, and other factors.

[0703] A "generative AI model" refers to a form of artificial intelligence that analyzes large amounts of data and generates new proposals or conclusions based on that information.

[0704] A "prompt" refers to an instruction or question used when inputting information into a generative AI model.

[0705] The embodiment of this invention is configured as an integrated system for optimizing the user's agricultural experience. Its specific elements are described below.

[0706] Users input land information using a dedicated application. This information includes location data, soil characteristics, and the type of crops they plan to cultivate. Furthermore, users can provide emotional data through a smart device. This device is equipped with emotion recognition sensors such as a camera and microphone, which analyze the user's facial expressions and voice to detect their emotional state.

[0707] After receiving land information from the user, the server retrieves the latest weather data from an external weather database. This weather database could be, for example, a public weather information service. The server then analyzes this information using a generative AI model to generate optimal fertilizers and cultivation methods. This analysis determines which fertilizer is most effective and how crops should be cultivated.

[0708] Regarding emotional data, the emotion engine analyzes information collected from the device to evaluate the user's real-time mental state. Based on this emotional state, the server prompts the generative AI model, suggesting adjusted cultivation methods and schedules to reduce the user's psychological burden. An example of a prompt used here might be, "If the user's emotions are stress-based, please provide the optimal work procedure."

[0709] This system provides users with flexible and personalized advice, enabling them to improve the efficiency of their farm management. Furthermore, users can visually review the feedback provided through the app, allowing for quick and appropriate action based on the situation.

[0710] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0711] Step 1:

[0712] Users input land information (location, soil characteristics, crop type, etc.) using a dedicated app. This input data is sent to the server. Upon receiving this information, the server stores it in a database and uses it as basic data necessary for obtaining subsequent weather data.

[0713] Step 2:

[0714] The server retrieves current and predicted weather data from an external weather database. The input here is the user's land information obtained in step 1. The server uses this data to request weather information for the relevant area via an API. The output includes temperature, precipitation, sunshine hours, etc.

[0715] Step 3:

[0716] The server processes land information and weather data in a unified manner and performs analysis using a generated AI model. The input data consists of land information and weather data, and the output is suggestions regarding the optimal type and timing of fertilizer application and cultivation procedures. Specifically, the AI ​​model analyzes the data and performs calculations based on past success stories and scientific evidence.

[0717] Step 4:

[0718] The device uses a camera and microphone to collect user emotional data in real time. Input includes the user's facial expressions and voice data, which are then analyzed by an emotion engine. Output includes stress levels and emotional tone. The emotional state is then transmitted to a server.

[0719] Step 5:

[0720] The server considers emotional data and previously generated cultivation suggestions, inputs prompt sentences into the AI ​​model, and adjusts the suggestions for the user. The inputs at this time are the emotional state and cultivation suggestions. As output, a cultivation plan and schedule adjusted to reduce the user's psychological burden are generated.

[0721] Step 6:

[0722] The server sends the final suggestions to the user's device, making them available for review within the app. The input is the server's refined suggestion data, and the output is visual feedback presented in the user interface. This allows users to take immediate, practical action.

[0723] (Application Example 2)

[0724] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] In recent years, the optimization of cultivation methods using land and weather information has become increasingly important in the agricultural sector. However, these systems cannot adjust work processes while considering the emotional state of workers, which can lead to increased workload. Similarly, in factories, there is a demand for systems that enable efficient production while reducing worker fatigue and stress. Furthermore, the difficulty in continuously adjusting fertilization and cultivation suggestions necessitates real-time environmental adaptation.

[0726] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0727] In this invention, the server includes means for acquiring user area information and weather information, means for analyzing the acquired area information and weather information and suggesting optimal nutrients and cultivation methods, and means for analyzing the worker's emotional state and dynamically adjusting the work process. This makes it possible for workers to work in an environment that is less stressful for them while simultaneously implementing the optimal cultivation method.

[0728] "User domain information" refers to information about a specific user's geographical location and its characteristics.

[0729] "Weather information" refers to information about environmental conditions such as weather, temperature, and humidity, obtained from external databases.

[0730] "Nutrient solutions" are materials used to promote plant growth and are suggested as part of fertilization.

[0731] "Cultivation methods" refer to the specific processes and procedures for growing crops and other plants, which are adjusted to achieve the optimal growing environment.

[0732] A "sensing device" refers to a device used to collect specific environmental information or emotional states, and may include cameras, microphones, and sensors.

[0733] "Worker's emotional state" refers to information about the psychological state of workers, analyzed using machine learning models or similar methods.

[0734] A "work process" refers to a series of tasks required in industrial or agricultural settings, and is subject to dynamic adjustment.

[0735] The system for realizing this application primarily consists of a server, a user's smart terminal, and sensing devices within the factory. The server analyzes area information provided by the user and weather information obtained from an external database. This analysis utilizes a database management system (e.g., PostgreSQL) and a machine learning framework (e.g., TensorFlow). Based on the analysis results, the system calculates and presents the optimal nutrients and cultivation methods to the user.

[0736] The user's smart device is equipped with a camera and microphone to analyze their emotional state, and these are used as sensing devices. The data obtained from these sensing devices is processed by an emotion recognition API (e.g., Affectiva API) to analyze the worker's emotional state in real time. Through smart glasses, the user can receive and implement adjustments to suggested cultivation methods and work processes.

[0737] For example, if the system detects that a factory worker is fatigued, the server will automate the work process and make adjustments to reduce the burden. It will also suggest break times and provide support to reduce worker stress.

[0738] An example of a prompt for a generative AI model could be an instruction such as, "Based on the generated data, please suggest an appropriate work process based on the worker's emotional state and work environment data." This prompt could then be used to adjust the actual work process and improve cultivation methods.

[0739] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0740] Step 1:

[0741] The server receives area information from the user's smart device. This includes location information, soil characteristics, and the types of plants being cultivated. The received area information is stored in a database on the server.

[0742] Step 2:

[0743] The server accesses an external weather database to retrieve relevant weather information. Based on the retrieved weather information and regional information, it uses a machine learning framework (e.g., TensorFlow) to calculate the optimal nutrients and cultivation methods, and presents the results to the user. At this stage, data analysis and model calculations are performed.

[0744] Step 3:

[0745] The user's smart device uses sensing devices to detect the worker's emotional state in real time. This involves using a camera and microphone to analyze emotional data through an emotion recognition API (e.g., Affectiva API). The analysis results are then transmitted to a processing system within the device.

[0746] Step 4:

[0747] The terminal sends data on the worker's emotional state to the server. Based on this data, the server uses an AI model to generate appropriate work process adjustments and creates an optimized work schedule for the worker. The generated adjustments are then fed back to the user's smart terminal.

[0748] Step 5:

[0749] The user reviews the work process and cultivation methods presented through the terminal and performs the tasks as needed. Data is presented to the user visually and interactively on the terminal, including suggestions for automated procedures and break times.

[0750] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0751] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0752] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0753] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0754] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0755] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0756] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0757] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0758] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0759] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0760] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0761] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0762] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0763] 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.

[0764] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0765] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0766] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0767] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0768] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0769] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0770] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0771] The following is further disclosed regarding the embodiments described above.

[0772] (Claim 1)

[0773] A means of acquiring user land information and weather data,

[0774] A means of analyzing acquired land information and weather data to propose optimal fertilizers and cultivation methods,

[0775] A means of providing the proposed fertilizer and cultivation method to the user,

[0776] A method for collecting environmental data of crops in real time using sensors,

[0777] Means for adjusting environmental conditions based on collected environmental data,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, further comprising means for adjusting fertilizers and cultivation methods based on suggestions provided to the user.

[0781] (Claim 3)

[0782] The system according to claim 1, further comprising means for visualizing the growth status of crops in real time and providing it to the user.

[0783] "Example 1"

[0784] (Claim 1)

[0785] A means by which a user inputs information about land and receives that information,

[0786] A means of obtaining historical and current atmospheric condition data for a region from external atmospheric condition information sources,

[0787] A means of analyzing acquired land information and atmospheric condition data using AI technology to propose optimal nutrients and cultivation methods,

[0788] A means of informing users of the proposed nutrients and cultivation methods,

[0789] A means of collecting data on the surrounding conditions of crops in real time using various measuring devices,

[0790] A means for analyzing collected surrounding situation data and generating automatic commands for situation adjustment,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, further comprising means for the user to adjust the nutrients and cultivation method based on the generated suggestions.

[0794] (Claim 3)

[0795] The system according to claim 1, further comprising means for displaying the growth status of crops in real time and visually informing the user.

[0796] "Application Example 1"

[0797] (Claim 1)

[0798] A means of acquiring user land information and weather data,

[0799] A means of analyzing acquired land information and weather data to propose optimal fertilizers and cultivation methods,

[0800] A means of providing the proposed fertilizer and cultivation method to the user,

[0801] A method for collecting environmental data of crops in real time using sensors,

[0802] Means for adjusting environmental conditions based on collected environmental data,

[0803] A means for automatically performing fertilizer spreading and environmental adjustment via a control device installed in the machine,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for adjusting fertilizers and cultivation methods based on suggestions provided to the user.

[0807] (Claim 3)

[0808] The system according to claim 1, further comprising means for visualizing the growth status of crops in real time and providing it to the user.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] A means of acquiring user land information and weather data,

[0812] A means of analyzing acquired land information and weather data to propose optimal fertilizers and cultivation methods,

[0813] A means of providing the proposed fertilizer and cultivation method to the user,

[0814] A means of acquiring and analyzing worker emotional data using sensors,

[0815] A means of adjusting cultivation methods and schedules based on acquired emotional data,

[0816] Means for adjusting environmental conditions based on collected environmental data,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising means for adjusting fertilizers and cultivation methods using analysis results based on user sentiment data, and providing suggestions to reduce the user's psychological burden.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising means for visualizing and providing to the user the crop growth status and changes to the cultivation plan based on the user's emotional state.

[0822] "Application example 2 when combining with an emotional engine"

[0823] (Claim 1)

[0824] Means for acquiring user area information and weather information,

[0825] A means for analyzing acquired regional information and meteorological information to suggest optimal nutrients and cultivation methods,

[0826] A means of providing the presented nutrients and cultivation methods to the user,

[0827] A means of collecting environmental information about plants in real time using a sensing device,

[0828] Means for adjusting environmental conditions based on collected environmental information,

[0829] A means of analyzing the emotional state of workers and dynamically adjusting the work process,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising means for adjusting nutrients and cultivation methods based on suggestions provided to the user.

[0833] (Claim 3)

[0834] The system according to claim 1, further comprising means for visualizing the growth status of plants in real time and providing it to the user. [Explanation of Symbols]

[0835] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring user land information and weather data, A means of analyzing acquired land information and weather data to propose optimal fertilizers and cultivation methods, A means of providing the proposed fertilizer and cultivation method to the user, A method for collecting environmental data of crops in real time using sensors, Means for adjusting environmental conditions based on collected environmental data, A system that includes this.

2. The system according to claim 1, further comprising means for adjusting fertilizers and cultivation methods based on suggestions provided to the user.

3. The system according to claim 1, further comprising means for visualizing the growth status of crops in real time and providing it to the user.

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