system

Abstract

We provide the system. [Solution] A means of acquiring image data of the ground surface using an aircraft, A means for analyzing the aforementioned image data to detect the growth state or abnormalities of the plant, A means for generating information that proposes the optimization of agricultural activities based on the analyzed data, Means for providing the generated information to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 agriculture, it is difficult to manage a wide range of farmland and quickly and accurately grasp the growth status and abnormalities of crops. Conventional methods require a great deal of labor and time to determine the situation of agricultural crops, hindering efficient farmland management. In addition, it is difficult to detect diseases and nutrient deficiencies at an early stage, making it difficult to maximize the quality and yield of agricultural crops. Solving such problems is an important requirement for technological innovation in the agricultural field.

Means for Solving the Problems

[0005] The present invention is a system that includes means for acquiring image data of the ground area using an aircraft, means for analyzing the image data to detect the growth status or abnormalities of plants, means for generating information that proposes optimization of agricultural activities based on the analyzed data, and means for providing the generated information to the user. This enables rapid and accurate management of farmland over a wide area, and realizes efficient crop cultivation and quality improvement. In particular, by using an unmanned aerial vehicle as the aircraft, it is possible to efficiently perform detailed scanning and analysis of the growing environment, and to quickly provide specific improvement proposals such as fertilization schedules, irrigation plans, and disease control measures.

[0006] A "flying object" refers to a device that can move through the air and is used for the purpose of acquiring image data of the Earth's surface.

[0007] "Image data" refers to the visual information of the ground surface acquired by an aircraft, represented in digital format.

[0008] "Analysis" refers to the process of identifying specific patterns or anomalies based on acquired image data.

[0009] "Plant growth status" refers to an indicator that shows the health and developmental stage of a plant during its growth process.

[0010] "Abnormal" refers to a condition in which a plant exhibits signs that differ from the normal growth process.

[0011] "Optimizing agricultural activities" refers to efficient and effective management practices aimed at maximizing plant growth and yield.

[0012] "Means of generating information" refers to a process or device that creates specific proposals for improving or streamlining agricultural activities based on analyzed data.

[0013] "Means of providing information to users" refers to methods or devices for presenting generated information in a format that is easily accessible to agricultural workers and managers.

[0014] The term "unmanned aircraft" refers to an aircraft that can fly by remote control or automatic control without a person on board.

[0015] The term "fertilization schedule" refers to a guideline for planning the amount of fertilizer and the application time for a specific plant or farmland.

[0016] The term "irrigation plan" refers to a guideline for planning the amount of water supply and the supply time for the purpose of proper water management of plants.

[0017] The term "disease control" refers to measures for protecting plants from diseases and pests and reducing or preventing damage.

Brief Description of Drawings

[0018] [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 a plurality of emotions are mapped. [Figure 10]Shows an emotion map to which a plurality of 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 an 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 an emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is an integrated system designed to address diverse management needs in agriculture, utilizing aircraft, particularly unmanned aerial vehicles, to achieve efficient farmland management. The specific details of how each component of the system works in conjunction will be explained.

[0040] In this invention, the user first identifies the farmland to be managed and sets its geographical information and flight schedule. This information is input into the system via a terminal and forms the basis of the flight plan executed by the aircraft. The server receives this information and issues appropriate instructions to the aircraft.

[0041] The drone autonomously flies within a designated area and acquires image data of the farmland using a high-resolution camera. Multispectral imaging technology can be used for this, allowing for the capture of crop health and growth conditions across various spectral ranges.

[0042] Image data is transmitted wirelessly to a server, securely stored, and then undergoes preprocessing before proceeding to analysis. During the analysis, a generative AI model processes the image data to understand the growth status and detect abnormalities. For example, it can identify diseases or nutrient deficiencies based on changes in leaf color and pattern.

[0043] Based on the analysis results, the server generates specific information to optimize agricultural activities. This includes suggestions for fertilization schedules, irrigation timing, and guidelines for disease control. The generated information is displayed on terminals through dashboards and applications, making it easily accessible to users.

[0044] For example, if a drone captures images of a cornfield and detects discoloration of the leaves, the server can determine that this is a specific disease and suggest necessary control and preventative measures to the user. Based on this information, the user can effectively manage their farmland.

[0045] This system allows farmers to quickly and effectively understand the condition of their farmland and take appropriate measures to improve productivity.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user sets the geographical information of the farmland and the flight plan on the terminal and inputs the necessary data into the system. This includes the location information of the farmland, the frequency of image acquisition, and the flight altitude.

[0049] Step 2:

[0050] The server transmits the flight plan details to the aircraft and prepares the drone to fly autonomously based on the specified conditions.

[0051] Step 3:

[0052] The drone flies towards its destination, following a pre-set trajectory and capturing images of the farmland with its high-resolution camera. The captured image data is transmitted to a server in real time.

[0053] Step 4:

[0054] The server stores the received image data in a database and performs preprocessing such as noise reduction and image shaping. This prepares the data for analysis.

[0055] Step 5:

[0056] The server uses a generative AI model to analyze pre-processed image data and identify crop growth conditions and abnormalities. This analysis is performed by detecting changes in specific patterns and color indices.

[0057] Step 6:

[0058] The server generates information to optimize agricultural activities based on the analysis results. This includes schedules for fertilization and irrigation, and specific plans for disease control.

[0059] Step 7:

[0060] The server sends the generated information to the terminal, which then presents it to the user using a dashboard. This allows the user to quickly take the necessary actions.

[0061] Step 8:

[0062] Users implement on-site management measures based on the provided information and provide feedback on the results via their devices. This feedback is sent to the server as information to improve the accuracy of data analysis in the future.

[0063] (Example 1)

[0064] 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."

[0065] Managing farmland in agriculture is difficult due to its vast scale. Furthermore, the inability to monitor crop growth and detect diseases early can delay the planning and execution of appropriate agricultural activities, leading to decreased productivity. Solving these problems and improving productivity is therefore essential.

[0066] 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.

[0067] In this invention, the server includes means for acquiring geographical area data using an aircraft, means for analyzing the data using a generative AI model, and means for generating information that proposes improvements to agricultural work based on the analyzed data. This makes it possible to efficiently grasp the conditions of farmland over a wide area and to quickly and effectively optimize agricultural activities.

[0068] "Aircraft" refers to devices that move through the air, such as unmanned aerial vehicles and drones, used to collect data in a geographical area.

[0069] A "generative AI model" refers to an artificial intelligence system that utilizes machine learning algorithms to gain insights and perform analysis from large amounts of data.

[0070] "Wireless communication" refers to a communication method that uses radio waves to send and receive data, and is a technology that transmits data without requiring physical connections such as cables.

[0071] "Map information" refers to data that represents geographical location and terrain, and is used when constructing flight plans for aircraft.

[0072] "Analyzed data" refers to data that has been processed and analyzed by a generative AI model, thereby adding value as information.

[0073] "Improving agricultural practices" refers to reviewing and optimizing plans and methods to increase crop productivity and streamline agricultural activities.

[0074] This invention is a system for streamlining agricultural management, which uses drones (unmanned aerial vehicles) to collect information about farmland, analyzes the data using a generated AI model, and optimizes agricultural activities.

[0075] First, the user sets the farmland to be managed. Using their device, the user inputs geographical information and flight schedules for the farmland they wish to manage. This information is then sent to the server. The device displays map information in an intuitive manner, allowing the user to create a flight plan.

[0076] Next, the server issues flight commands to the drone based on the transmitted data. The drone autonomously flies within the designated area according to the specified flight schedule. During flight, the drone acquires image data of the farmland using a high-resolution camera and a multispectral imaging sensor. The image data is transmitted to the server via wireless communication.

[0077] The server receives the acquired image data and analyzes it using a generative AI model. The AI ​​model processes and analyzes the data to recognize the growth status of crops and signs of abnormalities. This analysis allows for the detection of changes in the color and shape of crop leaves, enabling the early detection of potential diseases.

[0078] The server utilizes the analysis results to generate suggestions for fertilization schedules and irrigation timings, as well as guidelines for disease control. This information is provided to the user via a terminal. Users can then use the information provided on their terminal to improve their agricultural practices.

[0079] For example, if a drone captures images of a cornfield and detects areas where the leaves have turned yellow, the server uses a generative AI model to analyze this and determine that it is due to a nitrogen deficiency. It then proposes an appropriate fertilization plan, enabling the user to take swift action.

[0080] An example of a prompt message is as follows: "Use a generative AI model to analyze the latest multispectral imaging data of a cornfield and evaluate its growth status. If any abnormalities are found, describe them in detail and submit a proposed solution."

[0081] This system enables farmers to quickly and accurately grasp the conditions of farmland across a wide area and implement appropriate agricultural management.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user uses a terminal to input geographical information about the farmland they wish to manage and the flight schedule. The entered information is sent to the server as data including GPS coordinates, flight routes, and scheduled times. This establishes the target area of ​​the farmland to be managed and the basic information of the flight plan.

[0085] Step 2:

[0086] The server receives geographical information and flight schedules sent by the user. Based on this data, it generates specific flight commands for drones, which are unmanned aerial vehicles. As part of the data processing, an algorithm is applied to optimize the transmitted schedule and reflect it in each flight session. The output is instruction data showing the actual flight path and timing.

[0087] Step 3:

[0088] The server transmits flight command data to the drone via wireless communication. The drone receives this command and autonomously flies over the designated farmland. During flight, the onboard high-resolution camera and multispectral imaging sensor are activated to acquire image data of the farmland in real time. Flight command data is the input, and image data of the farmland is the output.

[0089] Step 4:

[0090] The acquired image data is transmitted wirelessly from the drone to the server. Upon receiving this data, the server performs initial data processing, including image file compression / decompression and noise filtering. The pre-processed image data becomes its output.

[0091] Step 5:

[0092] The server analyzes pre-processed image data using a generative AI model. During this process, the AI ​​algorithm detects patterns to identify crop health and growth abnormalities. It identifies leaf color, shape, and other indicators, and records details of any abnormalities found. The input is pre-processed image data, and the output is the anomaly detection result.

[0093] Step 6:

[0094] Based on the analysis results, the server automatically generates specific improvement suggestions, such as fertilization schedules, irrigation timings, and disease control measures. These results are sent to the terminal as suggested information in text format. The output obtained by the server is optimized agricultural guidance information.

[0095] Step 7:

[0096] The terminal receives suggested information sent from the server and displays it in a user-friendly interface on the dashboard. Based on this information, the user plans and implements practical steps for farm management. At this stage, the user is ready to take concrete action based on the outputted improvement instructions.

[0097] (Application Example 1)

[0098] 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."

[0099] When efficiently monitoring and managing production processes in manufacturing facilities, conventional methods have limitations in productivity improvement due to the difficulty in rapidly detecting anomalies and optimizing production activities. In particular, there is a need to immediately detect and address equipment deterioration and abnormal product placement.

[0100] 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.

[0101] In this invention, the server includes means for acquiring image data of a production facility area using an imaging device, means for analyzing the image data to detect production processes or anomalies, and means for generating information that proposes optimization of production activities based on the analyzed data. This enables real-time monitoring of production processes and rapid anomaly detection.

[0102] An "imaging device" is a device used to acquire image data of a production facility area, and it is a device that converts visual information into electrical signals using optical means.

[0103] A "mobile object" is a machine or device that has the function of collecting data while patrolling a specific area, and that can operate according to the situation.

[0104] "Analysis means" refers to a process or apparatus for processing acquired image data using an algorithm or method to extract information or detect anomalies.

[0105] An "optimization proposal" is a set of methods or information for generating and providing to the user work plans and improvement measures to enhance efficiency based on analyzed data.

[0106] A "production facility" refers to a place or equipment where goods are manufactured, processed, or assembled, and encompasses an environment that includes various manufacturing processes.

[0107] To implement this invention, it is necessary to construct a system for monitoring and managing production processes within a production facility. The main components of the system include an imaging device mounted on a mobile device, a server for data processing, and a terminal for users to view this information.

[0108] The imaging device is mounted on a mobile vehicle and patrols the production facility, periodically acquiring image data of the area. This image data is transmitted to a server via wireless communication, where it is analyzed using a high-performance generative AI model. During the data analysis process, the server detects anomalies in the production process and generates suggestions for optimizing production activities based on that information.

[0109] The generated optimization suggestions are provided to the user in real time via a terminal. The user can use the terminal to monitor the production process at their facility and respond quickly if an anomaly occurs. For example, if a misalignment of a product on the same line is detected, the user can immediately begin corrective work.

[0110] An example of a prompt in a generative AI model is, "Check the position of the product on the conveyor belt and determine if there are any abnormalities." By using this prompt, the AI ​​model can process the data appropriately and provide reliable analysis results.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The terminal controls an imaging device mounted on a mobile vehicle, acquiring image data at regular intervals while patrolling the production facility. In this process, the terminal provides navigation instructions to the mobile vehicle, and the imaging device captures images using optical means. The input is visual information of the production facility, and the output is the acquired image data.

[0114] Step 2:

[0115] The server receives the retrieved image data and preprocesses it. Preprocessing includes noise reduction and image resizing. The input is the acquired image data, and the output is preprocessed data in a format suitable for analysis. This prepares the system for efficient analysis by the generative AI model.

[0116] Step 3:

[0117] The server inputs pre-processed data along with the prompt message "Check the product positions on the conveyor belt and determine if there are any abnormalities" into the generated AI model and performs the analysis. The AI ​​model analyzes the data in detail and detects abnormalities in the production process and product placement. The output is the abnormality detection result based on the analysis. At this step, the presence or absence of abnormalities becomes clear.

[0118] Step 4:

[0119] The server generates optimization suggestions based on the analysis results. These suggestions include methods for correcting anomalies and improvements to the production process. The input is the anomaly detection result, and the output is the content of the suggestions. The generated suggestions lead to increased efficiency in the production process and faster problem solving.

[0120] Step 5:

[0121] The terminal provides the user with generated optimization suggestions in real time. The user reviews the suggestions via the terminal and takes action as needed. The input is the content of the suggestions, and the output is information that leads to user action. In this step, the user can start taking action immediately.

[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 enables the more effective provision of information for optimizing agricultural activities to users by incorporating an emotion engine that recognizes user emotions into a farmland management system that utilizes an aerial vehicle. This system aims to improve user satisfaction by providing not only the information necessary for farmland management but also suggestions that reflect the user's emotional state.

[0124] Specifically, the user first inputs information about the farmland to be managed and the flight plan into the system via a terminal. Based on this, the server issues appropriate flight commands to the aircraft. The aircraft, specifically the unmanned aerial vehicle, flies over the designated farmland, and the image data acquired by the high-resolution camera is transmitted to the server in real time.

[0125] The transmitted image data is stored in the server's database and, after preprocessing, is analyzed. The analysis uses a generative AI model to identify crop health and abnormalities, and generates information for optimizing agricultural activities based on the collected data. This information includes specific suggestions such as fertilization schedules, irrigation plans, and disease control measures.

[0126] Furthermore, this system includes an emotion engine, which allows the server to analyze user responses and understand their emotional state. This is done through facial and vocal expression analysis when providing information to the user via the terminal. The emotional information recognized by the emotion engine is used to customize the suggestions provided.

[0127] For example, if a user expresses dissatisfaction with a suggestion, the system re-evaluates the suggestion and improves it by adding new information. If the suggestion meets the user's expectations, the same approach is continued.

[0128] Thus, the present invention enables agricultural workers to receive not only standard farmland management information but also suggestions tailored to their own usage experience. This improves both the efficiency of agricultural activities and user satisfaction.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users configure detailed information about the farmland's geographical location, intended management activities, and flight schedules on their terminals and input this information into the system. This input includes details such as the farmland's coordinates, the frequency of photography, and the flight altitude.

[0132] Step 2:

[0133] The server formulates a flight plan based on the received information and sends specific commands to the aircraft. These commands include the flight path and shooting locations.

[0134] Step 3:

[0135] The drone, acting as the aircraft, receives commands from the server and flies over designated farmland, acquiring image data of the farmland from above using a high-resolution camera. The acquired image data is transmitted to the server in real time.

[0136] Step 4:

[0137] The server securely stores the acquired image data in a database and then performs image preprocessing. This preprocessing includes noise reduction and resolution adjustment.

[0138] Step 5:

[0139] The server inputs pre-processed image data into a generating AI model to analyze crop growth conditions and any abnormalities. Based on the information obtained from this analysis, it generates specific suggestions for optimizing agricultural activities, such as fertilization schedules and irrigation plans.

[0140] Step 6:

[0141] The generated suggestion information is provided to the user via the terminal. Here, the emotion engine recognizes emotions through the user's facial and voice input and analyzes the user's response.

[0142] Step 7:

[0143] The server uses recognized user sentiment information to adjust the suggestions in real time. If the user appears dissatisfied, it re-evaluates the suggestions and provides additional information.

[0144] Step 8:

[0145] Users implement farm management based on the improved suggestions and report the results and feedback to the system via their devices. This feedback helps improve the accuracy of the emotion engine and refine future suggestions.

[0146] (Example 2)

[0147] 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".

[0148] In agriculture, there is a need to quickly and accurately grasp the health and abnormalities of crops and manage them efficiently. However, current technology lacks the ability to provide information that takes into account the user's emotional state. Therefore, the challenge is to provide a system that optimizes agricultural activities while improving user satisfaction.

[0149] 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.

[0150] In this invention, the server includes means for acquiring image data of the ground area using an aircraft, means for analyzing the image data to detect the growth state or abnormalities of plants, means for generating information that proposes optimization of agricultural activities based on the analyzed data, and means for analyzing the user's reactions to recognize the user's emotional state and customizing the optimization proposal information. This makes it possible to provide information that is tailored to the user's emotions, thereby improving the efficiency of agricultural activities and increasing user satisfaction.

[0151] A "flying object" is a mechanical device that can collect information from a designated point while flying through the air.

[0152] "Image data" refers to visual information of the ground surface area acquired from an aircraft, and it forms the basis for analysis and interpretation.

[0153] "Analysis" refers to an information processing technique that involves processing acquired image data to identify the growth status and abnormalities of plants.

[0154] "Plant growth status" refers to information indicating the degree of plant growth and health, and is important in managing agricultural activities.

[0155] "Abnormality" refers to a deviation from the normal state of plant growth, including the effects of diseases and environmental stress.

[0156] "Optimization proposal information" refers to information created based on analysis results with the aim of improving the efficiency of agricultural activities, and includes specific plans such as fertilization and irrigation.

[0157] "Users" refer to individuals or groups who utilize the system in agricultural activities, and their emotional state and needs must be taken into consideration.

[0158] "Emotional state" refers to the user's mental response to the suggested information, and includes states such as satisfaction and dissatisfaction.

[0159] "Analysis means" refers to technical methods or functions for processing information to achieve a specific purpose.

[0160] A "server" refers to a centralized computing device used for storing, processing, analyzing, and providing data results.

[0161] This invention is a farmland management system designed to streamline agricultural activities and improve user satisfaction. Its most distinctive feature is its ability to analyze data collected by unmanned aerial vehicles and provide suggestions that take into account the user's emotional state.

[0162] First, the user uses a device to input information about the farmland to be managed. This device can be a tablet or personal computer and has an intuitive interface for the user. The data entered by the user includes geographical information, crop types, and flight plans.

[0163] Next, the server receives the data and sends flight commands to the unmanned aerial vehicle (UAV). This UAV is equipped with a high-resolution camera and acquires image data while flying over a designated area of ​​the ground. The UAV uses GPS for precise location identification and flies along a planned route. The collected image data is transmitted to the server in real time and stored in a database.

[0164] The server preprocesses this image data and then analyzes it using a generated AI model. This model identifies crop health and abnormalities and generates information to optimize agricultural activities. This optimization information includes fertilization schedules, irrigation plans, and disease control measures.

[0165] Furthermore, the system uses an emotion engine to analyze user reactions. The device captures the user's facial expressions through its camera and records audio with its microphone. This allows the system to understand the user's emotional state and customize suggestions based on that information.

[0166] For example, if a farmer wants to maximize their yield, the emotion engine evaluates the farmer's satisfaction with the suggestions. If dissatisfaction is indicated, the server re-evaluates the timing and amount of fertilizer application and generates new suggestions. This allows users to receive suggestions tailored to their own emotions.

[0167] Example of a prompt:

[0168] "Please explain how the unmanned aerial vehicle receives flight commands and collects data when a user inputs information about farmland. Please describe the specific process by which the analyzed information helps optimize agricultural activities."

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user uses a terminal to input information about the farmland. This input includes the location of the farmland, the type of crop, and the desired flight plan. The terminal sends this information to the server. The input in this process is data from the user, and the output is data sent to the server.

[0172] Step 2:

[0173] The server generates appropriate flight commands for the unmanned aerial vehicle (UAV) based on the data received from the terminal. This process involves calculating GPS coordinates and flight routes. The server then transmits these flight commands to the UAV. The input is data from the terminal, and the output is the flight commands sent to the UAV.

[0174] Step 3:

[0175] The unmanned aerial vehicle receives commands from a server, flies over farmland, and collects image data using a high-resolution camera. This data is transmitted to the server in real time. The input here is flight commands from the server, and the output is image data sent back to the server.

[0176] Step 4:

[0177] The server stores the received image data in a database and performs preprocessing. This preprocessing includes denoising and correcting the images. Then, a generative AI model is used to analyze the data and identify crop health and abnormalities. The input is image data from the unmanned aerial vehicle, and the output is the analysis results.

[0178] Step 5:

[0179] The server generates optimization information for agricultural activities based on the analysis results. This information includes fertilization schedules and irrigation plans. The generated information is sent to the terminal. The input is the analysis results, and the output is the optimization information.

[0180] Step 6:

[0181] The terminal presents the user with optimized information sent from the server. Simultaneously, it collects facial and vocal expressions using the camera and microphone to analyze the user's response using an emotion engine. The input here is the optimized information from the server and the user's response, and the output is the user's emotional state.

[0182] Step 7:

[0183] The server analyzes the user's emotional information obtained from the emotion engine and customizes the suggestions as needed. If the user is dissatisfied, the server re-evaluates the suggestions, adds new information, and makes improvements. The input is the user's emotional state, and the output is customized suggestion information.

[0184] (Application Example 2)

[0185] 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".

[0186] Conventional farmland management systems have a problem in that their suggestions, based on the analysis of terrain data acquired using aircraft, are solely based on the data itself and do not take into account the subjective satisfaction or emotional state of the users, thus failing to adequately meet the actual needs of the users. Similarly, in factories, robot work plans do not reflect the emotional state of the workers, which can lead to decreased work efficiency and satisfaction.

[0187] 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. In this invention, the server includes means for acquiring image information of the terrain area using an aircraft, means for analyzing the image information to detect the health status or abnormalities of plants, means for generating information that proposes improvements to agricultural activities based on the analyzed data, means for identifying the emotional state of the user using an emotion analysis device, and means for adjusting the proposals for the user based on the emotional state. As a result, by making proposals that take into account the emotional state of the user, efficient and highly satisfying management that meets actual needs becomes possible in both agricultural activities and factory work planning.

[0188] A "flying object" is a mechanical device used to fly over a terrain area and acquire image information.

[0189] "Image information" refers to visual data of the terrain area acquired by sensors and cameras mounted on the aircraft.

[0190] "Health status" refers to biological indicators that show whether plants or crops are growing normally.

[0191] An "abnormality" is a condition or symptom that deviates from the standard growth pattern in an organism.

[0192] An "emotion analysis device" is a combination of hardware and software used to identify a user's emotional state.

[0193] "Suggestions" refer to specific instructions or advice given based on collected data and emotional states.

[0194] A "user" is an individual or organization that uses this system to perform tasks or receive information.

[0195] The system that realizes this invention detects the health and abnormalities of plants based on image information of the terrain area collected by an aircraft, and further provides improvement suggestions that take into account the user's emotional state.

[0196] The server receives high-resolution image information transmitted from the aircraft and stores it in a database. After preprocessing, this image data is analyzed using an AI model (e.g., OpenAI® GPT-4®) to identify the health status and abnormalities of plants. Based on the information extracted through the analysis, specific suggestions for improving agricultural activities are generated. At this stage, user emotion data is collected using an emotion analysis device, and the user's emotional state is identified using facial expression analysis software (e.g., Microsoft® Azure® Emotion API).

[0197] The terminal displays and presents suggestions supplied from the server to the user. These suggestions are customized according to the user's emotional state; for example, if the user is stressed, the suggestions may include adjustments to reduce their workload. This system allows users to receive optimal suggestions based on their emotional state, improving work efficiency and satisfaction.

[0198] A concrete example is the operational management of robots in factories. By using unmanned aerial vehicles to monitor the status of equipment from above within the factory and adjusting the robots' work schedules and movements according to the stress levels of the staff, it is expected that the overall efficiency of the work environment will improve.

[0199] An example of a prompt message might be, "If the user's emotional state is stressed, how should the suggestions for improving agricultural activities be adjusted?"

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

[0201] Step 1:

[0202] The server receives terrain image data from the aircraft. The received data is first converted to an appropriate format and stored in the database. The input is image data acquired from the aircraft, which is used to save data to the database. The output is the accumulation of data in the database.

[0203] Step 2:

[0204] The server preprocesses image data stored in the database and uses a generative AI model to analyze the health and abnormalities of plants. The input here is preprocessed image data, and the generative AI model extracts and analyzes features from the data, resulting in output of plant health information. Specifically, it identifies color and shape patterns within the image to evaluate the plant's health.

[0205] Step 3:

[0206] The server generates suggested information aimed at improving agricultural activities based on the analysis results. The input is the plant health information obtained in step 2, and improvement suggestions are generated based on this. The output is specific improvement plans and measures. By using prompts to consider how the suggestions can be implemented, more specific output can be obtained.

[0207] Step 4:

[0208] The terminal displays suggestions received from the server and provides them to the user. Input consists of improvement suggestions sent from the server; these suggestions are presented on the display device, allowing the user to review them. Output is the presentation of suggestion information to the user. Specifically, the data is visualized in a visually easy-to-understand format, making it easy for the user to receive and process.

[0209] Step 5:

[0210] The device uses a camera and microphone to collect the user's face and voice, and an emotion analysis device identifies the user's emotional state. The input consists of the user's facial image and voice data, and the emotion analysis device analyzes this data to determine the emotional state. The output is information about the user's emotional state. Specifically, facial recognition software detects subtle changes in facial expression and determines the emotion based on that.

[0211] Step 6:

[0212] The server takes the user's emotional state into consideration and adjusts the suggested information accordingly. The input consists of the user's emotional state information and initial suggested information, and the adjusted suggestions are output. Specifically, the emotional information is incorporated into the generating AI model via prompt messages, and new suggestions are generated. An example of such a prompt message would be, "If the user's emotional state is stress, how should the suggestions for improving agricultural activities be adjusted?"

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention is an integrated system designed to address diverse management needs in agriculture, utilizing aircraft, particularly unmanned aerial vehicles, to achieve efficient farmland management. The specific details of how each component of the system works in conjunction will be explained.

[0230] In this invention, the user first identifies the farmland to be managed and sets its geographical information and flight schedule. This information is input into the system via a terminal and forms the basis of the flight plan executed by the aircraft. The server receives this information and issues appropriate instructions to the aircraft.

[0231] The drone autonomously flies within a designated area and acquires image data of the farmland using a high-resolution camera. Multispectral imaging technology can be used for this, allowing for the capture of crop health and growth conditions across various spectral ranges.

[0232] Image data is transmitted wirelessly to a server, securely stored, and then undergoes preprocessing before proceeding to analysis. During the analysis, a generative AI model processes the image data to understand the growth status and detect abnormalities. For example, it can identify diseases or nutrient deficiencies based on changes in leaf color and pattern.

[0233] Based on the analysis results, the server generates specific information to optimize agricultural activities. This includes suggestions for fertilization schedules, irrigation timing, and guidelines for disease control. The generated information is displayed on terminals through dashboards and applications, making it easily accessible to users.

[0234] For example, if a drone captures images of a cornfield and detects discoloration of the leaves, the server can determine that this is a specific disease and suggest necessary control and preventative measures to the user. Based on this information, the user can effectively manage their farmland.

[0235] This system allows farmers to quickly and effectively understand the condition of their farmland and take appropriate measures to improve productivity.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The user sets the geographical information of the farmland and the flight plan on the terminal and inputs the necessary data into the system. This includes the location information of the farmland, the frequency of image acquisition, and the flight altitude.

[0239] Step 2:

[0240] The server transmits the flight plan details to the aircraft and prepares the drone to fly autonomously based on the specified conditions.

[0241] Step 3:

[0242] The drone flies towards its destination, following a pre-set trajectory and capturing images of the farmland with its high-resolution camera. The captured image data is transmitted to a server in real time.

[0243] Step 4:

[0244] The server stores the received image data in a database and performs preprocessing such as noise reduction and image shaping. This prepares the data for analysis.

[0245] Step 5:

[0246] The server uses a generative AI model to analyze pre-processed image data and identify crop growth conditions and abnormalities. This analysis is performed by detecting changes in specific patterns and color indices.

[0247] Step 6:

[0248] The server generates information to optimize agricultural activities based on the analysis results. This includes schedules for fertilization and irrigation, and specific plans for disease control.

[0249] Step 7:

[0250] The server sends the generated information to the terminal, which then presents it to the user using a dashboard. This allows the user to quickly take the necessary actions.

[0251] Step 8:

[0252] Users implement on-site management measures based on the provided information and provide feedback on the results via their devices. This feedback is sent to the server as information to improve the accuracy of data analysis in the future.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] Managing farmland in agriculture is difficult due to its vast scale. Furthermore, the inability to monitor crop growth and detect diseases early can delay the planning and execution of appropriate agricultural activities, leading to decreased productivity. Solving these problems and improving productivity is therefore essential.

[0256] 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.

[0257] In this invention, the server includes means for acquiring geographical area data using an aircraft, means for analyzing the data using a generative AI model, and means for generating information that proposes improvements to agricultural work based on the analyzed data. This makes it possible to efficiently grasp the conditions of farmland over a wide area and to quickly and effectively optimize agricultural activities.

[0258] "Aircraft" refers to devices that move through the air, such as unmanned aerial vehicles and drones, used to collect data in a geographical area.

[0259] A "generative AI model" refers to an artificial intelligence system that utilizes machine learning algorithms to gain insights and perform analysis from large amounts of data.

[0260] "Wireless communication" refers to a communication method that uses radio waves to send and receive data, and is a technology that transmits data without requiring physical connections such as cables.

[0261] "Map information" refers to data that represents geographical location and terrain, and is used when constructing flight plans for aircraft.

[0262] "Analyzed data" refers to data that has been processed and analyzed by a generative AI model, thereby adding value as information.

[0263] "Improving agricultural practices" refers to reviewing and optimizing plans and methods to increase crop productivity and streamline agricultural activities.

[0264] This invention is a system for streamlining agricultural management, which uses drones (unmanned aerial vehicles) to collect information about farmland, analyzes the data using a generated AI model, and optimizes agricultural activities.

[0265] First, the user sets the farmland to be managed. Using their device, the user inputs geographical information and flight schedules for the farmland they wish to manage. This information is then sent to the server. The device displays map information in an intuitive manner, allowing the user to create a flight plan.

[0266] Next, the server issues flight commands to the drone based on the transmitted data. The drone autonomously flies within the designated area according to the specified flight schedule. During flight, the drone acquires image data of the farmland using a high-resolution camera and a multispectral imaging sensor. The image data is transmitted to the server via wireless communication.

[0267] The server receives the acquired image data and analyzes it using a generative AI model. The AI ​​model processes and analyzes the data to recognize the growth status of crops and signs of abnormalities. This analysis allows for the detection of changes in the color and shape of crop leaves, enabling the early detection of potential diseases.

[0268] The server utilizes the analysis results to generate suggestions for fertilization schedules and irrigation timings, as well as guidelines for disease control. This information is provided to the user via a terminal. Users can then use the information provided on their terminal to improve their agricultural practices.

[0269] For example, if a drone captures images of a cornfield and detects areas where the leaves have turned yellow, the server uses a generative AI model to analyze this and determine that it is due to a nitrogen deficiency. It then proposes an appropriate fertilization plan, enabling the user to take swift action.

[0270] An example of a prompt message is as follows: "Use a generative AI model to analyze the latest multispectral imaging data of a cornfield and evaluate its growth status. If any abnormalities are found, describe them in detail and submit a proposed solution."

[0271] This system enables farmers to quickly and accurately grasp the conditions of farmland across a wide area and implement appropriate agricultural management.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user uses a terminal to input geographical information about the farmland they wish to manage and the flight schedule. The entered information is sent to the server as data including GPS coordinates, flight routes, and scheduled times. This establishes the target area of ​​the farmland to be managed and the basic information of the flight plan.

[0275] Step 2:

[0276] The server receives geographical information and flight schedules sent by the user. Based on this data, it generates specific flight commands for drones, which are unmanned aerial vehicles. As part of the data processing, an algorithm is applied to optimize the transmitted schedule and reflect it in each flight session. The output is instruction data showing the actual flight path and timing.

[0277] Step 3:

[0278] The server transmits flight command data to the drone via wireless communication. The drone receives this command and autonomously flies over the designated farmland. During flight, the onboard high-resolution camera and multispectral imaging sensor are activated to acquire image data of the farmland in real time. Flight command data is the input, and image data of the farmland is the output.

[0279] Step 4:

[0280] The acquired image data is transmitted wirelessly from the drone to the server. Upon receiving this data, the server performs initial data processing, including image file compression / decompression and noise filtering. The pre-processed image data becomes its output.

[0281] Step 5:

[0282] The server analyzes pre-processed image data using a generative AI model. During this process, the AI ​​algorithm detects patterns to identify crop health and growth abnormalities. It identifies leaf color, shape, and other indicators, and records details of any abnormalities found. The input is pre-processed image data, and the output is the anomaly detection result.

[0283] Step 6:

[0284] Based on the analysis results, the server automatically generates specific improvement plans such as fertilization schedules, irrigation timings, and disease control measures. These results are transmitted to the terminal as proposed information in text form. The output obtained by the server is optimized agricultural guidance information.

[0285] Step 7:

[0286] The terminal receives the proposed information sent from the server and displays it on the user-friendly interface of the dashboard. Based on this information, the user plans and implements practical steps for farm management. At this stage, based on the output improvement instruction information, the user is ready to take specific actions.

[0287] (Application Example 1)

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

[0289] When efficiently monitoring and managing the production process in a production facility, there is a problem in the conventional method that it is difficult to quickly detect abnormalities and optimize production activities, and there are limitations in improving productivity. In particular, it is required to immediately detect and address device deterioration and product placement abnormalities.

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

[0291] In this invention, the server includes means for acquiring image data of the area of the production facility using an imaging device, means for analyzing the image data to detect a production process or an abnormality, and means for generating information for proposing optimization of production activities based on the analyzed data. Thereby, real-time monitoring of the production process and rapid abnormality detection become possible.

[0292] An "imaging device" is a device used to acquire image data of a production facility area, and it is a device that converts visual information into electrical signals using optical means.

[0293] A "mobile object" is a machine or device that has the function of collecting data while patrolling a specific area, and that can operate according to the situation.

[0294] "Analysis means" refers to a process or apparatus for processing acquired image data using an algorithm or method to extract information or detect anomalies.

[0295] An "optimization proposal" is a set of methods or information for generating and providing to the user work plans and improvement measures to enhance efficiency based on analyzed data.

[0296] A "production facility" refers to a place or equipment where goods are manufactured, processed, or assembled, and encompasses an environment that includes various manufacturing processes.

[0297] To implement this invention, it is necessary to construct a system for monitoring and managing production processes within a production facility. The main components of the system include an imaging device mounted on a mobile device, a server for data processing, and a terminal for users to view this information.

[0298] The imaging device is mounted on a mobile vehicle and patrols the production facility, periodically acquiring image data of the area. This image data is transmitted to a server via wireless communication, where it is analyzed using a high-performance generative AI model. During the data analysis process, the server detects anomalies in the production process and generates suggestions for optimizing production activities based on that information.

[0299] The generated optimization suggestions are provided to the user in real time via a terminal. The user can use the terminal to monitor the production process at their facility and respond quickly if an anomaly occurs. For example, if a misalignment of a product on the same line is detected, the user can immediately begin corrective work.

[0300] An example of a prompt in a generative AI model is, "Check the position of the product on the conveyor belt and determine if there are any abnormalities." By using this prompt, the AI ​​model can process the data appropriately and provide reliable analysis results.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The terminal controls an imaging device mounted on a mobile vehicle, acquiring image data at regular intervals while patrolling the production facility. In this process, the terminal provides navigation instructions to the mobile vehicle, and the imaging device captures images using optical means. The input is visual information of the production facility, and the output is the acquired image data.

[0304] Step 2:

[0305] The server receives the retrieved image data and preprocesses it. Preprocessing includes noise reduction and image resizing. The input is the acquired image data, and the output is preprocessed data in a format suitable for analysis. This prepares the system for efficient analysis by the generative AI model.

[0306] Step 3:

[0307] The server inputs the preprocessed data into the generative AI model together with the prompt sentence "Please check the product position on the conveyor and determine if there are any abnormalities", and performs analysis. The AI model analyzes the data in detail and detects abnormalities in the production process and product placement. The output is the abnormality detection result based on the analysis. The presence or absence of abnormalities becomes clear at this step.

[0308] Step 4:

[0309] The server generates an optimization proposal based on the analysis results. This proposal includes methods for correcting abnormalities and points for improving the production process. The input is the abnormality detection result, and the output is the content of the proposal. The generated proposal leads to the improvement of the efficiency of the production process and the rapid solution of problems.

[0310] Step 5:

[0311] The terminal provides the generated optimization proposal to the user in real time. The user checks the content of the proposal via the terminal and takes countermeasures as necessary. The input is the content of the proposal, and the output is information leading to the user's actions. At this step, the user can start corresponding immediately.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0313] The present invention enables more effectively providing the user with optimization information for agricultural activities by incorporating an emotion engine for recognizing the user's emotion into the farmland management system using a flying object. In this system, not only the information necessary for farmland management but also proposals reflecting the user's emotional state are made to improve the user's satisfaction.

[0314] Specifically, the user first inputs information about the farmland to be managed and the flight plan into the system via a terminal. Based on this, the server issues appropriate flight commands to the aircraft. The aircraft, specifically the unmanned aerial vehicle, flies over the designated farmland, and the image data acquired by the high-resolution camera is transmitted to the server in real time.

[0315] The transmitted image data is stored in the server's database and, after preprocessing, is analyzed. The analysis uses a generative AI model to identify crop health and abnormalities, and generates information for optimizing agricultural activities based on the collected data. This information includes specific suggestions such as fertilization schedules, irrigation plans, and disease control measures.

[0316] Furthermore, this system includes an emotion engine, which allows the server to analyze user responses and understand their emotional state. This is done through facial and vocal expression analysis when providing information to the user via the terminal. The emotional information recognized by the emotion engine is used to customize the suggestions provided.

[0317] For example, if a user expresses dissatisfaction with a suggestion, the system re-evaluates the suggestion and improves it by adding new information. If the suggestion meets the user's expectations, the same approach is continued.

[0318] Thus, the present invention enables agricultural workers to receive not only standard farmland management information but also suggestions tailored to their own usage experience. This improves both the efficiency of agricultural activities and user satisfaction.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] Users configure detailed information about the farmland's geographical location, intended management activities, and flight schedules on their terminals and input this information into the system. This input includes details such as the farmland's coordinates, the frequency of photography, and the flight altitude.

[0322] Step 2:

[0323] The server formulates a flight plan based on the received information and sends specific commands to the aircraft. These commands include the flight path and shooting locations.

[0324] Step 3:

[0325] The drone, acting as the aircraft, receives commands from the server and flies over designated farmland, acquiring image data of the farmland from above using a high-resolution camera. The acquired image data is transmitted to the server in real time.

[0326] Step 4:

[0327] The server securely stores the acquired image data in a database and then performs image preprocessing. This preprocessing includes noise reduction and resolution adjustment.

[0328] Step 5:

[0329] The server inputs pre-processed image data into a generating AI model to analyze crop growth conditions and any abnormalities. Based on the information obtained from this analysis, it generates specific suggestions for optimizing agricultural activities, such as fertilization schedules and irrigation plans.

[0330] Step 6:

[0331] The generated suggestion information is provided to the user via the terminal. Here, the emotion engine recognizes emotions through the user's facial and voice input and analyzes the user's response.

[0332] Step 7:

[0333] The server uses recognized user sentiment information to adjust the suggestions in real time. If the user appears dissatisfied, it re-evaluates the suggestions and provides additional information.

[0334] Step 8:

[0335] Users implement farm management based on the improved suggestions and report the results and feedback to the system via their devices. This feedback helps improve the accuracy of the emotion engine and refine future suggestions.

[0336] (Example 2)

[0337] 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".

[0338] In agriculture, there is a need to quickly and accurately grasp the health and abnormalities of crops and manage them efficiently. However, current technology lacks the ability to provide information that takes into account the user's emotional state. Therefore, the challenge is to provide a system that optimizes agricultural activities while improving user satisfaction.

[0339] 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.

[0340] In this invention, the server includes means for acquiring image data of the ground area using an aircraft, means for analyzing the image data to detect the growth state or abnormalities of plants, means for generating information that proposes optimization of agricultural activities based on the analyzed data, and means for analyzing the user's reactions to recognize the user's emotional state and customizing the optimization proposal information. This makes it possible to provide information that is tailored to the user's emotions, thereby improving the efficiency of agricultural activities and increasing user satisfaction.

[0341] A "flying object" is a mechanical device that can collect information from a designated point while flying through the air.

[0342] "Image data" refers to visual information of the ground surface area acquired from an aircraft, and it forms the basis for analysis and interpretation.

[0343] "Analysis" refers to an information processing technique that involves processing acquired image data to identify the growth status and abnormalities of plants.

[0344] "Plant growth status" refers to information indicating the degree of plant growth and health, and is important in managing agricultural activities.

[0345] "Abnormality" refers to a deviation from the normal state of plant growth, including the effects of diseases and environmental stress.

[0346] "Optimization proposal information" refers to information created based on analysis results with the aim of improving the efficiency of agricultural activities, and includes specific plans such as fertilization and irrigation.

[0347] "Users" refer to individuals or groups who utilize the system in agricultural activities, and their emotional state and needs must be taken into consideration.

[0348] "Emotional state" refers to the user's mental response to the suggested information, and includes states such as satisfaction and dissatisfaction.

[0349] "Analysis means" refers to technical methods or functions for processing information to achieve a specific purpose.

[0350] A "server" refers to a centralized computing device used for storing, processing, analyzing, and providing data results.

[0351] This invention is a farmland management system designed to streamline agricultural activities and improve user satisfaction. Its most distinctive feature is its ability to analyze data collected by unmanned aerial vehicles and provide suggestions that take into account the user's emotional state.

[0352] First, the user uses a device to input information about the farmland to be managed. This device can be a tablet or personal computer and has an intuitive interface for the user. The data entered by the user includes geographical information, crop types, and flight plans.

[0353] Next, the server receives the data and sends flight commands to the unmanned aerial vehicle (UAV). This UAV is equipped with a high-resolution camera and acquires image data while flying over a designated area of ​​the ground. The UAV uses GPS for precise location identification and flies along a planned route. The collected image data is transmitted to the server in real time and stored in a database.

[0354] The server preprocesses this image data and then analyzes it using a generated AI model. This model identifies crop health and abnormalities and generates information to optimize agricultural activities. This optimization information includes fertilization schedules, irrigation plans, and disease control measures.

[0355] Furthermore, the system uses an emotion engine to analyze user reactions. The device captures the user's facial expressions through its camera and records audio with its microphone. This allows the system to understand the user's emotional state and customize suggestions based on that information.

[0356] For example, if a farmer wants to maximize their yield, the emotion engine evaluates the farmer's satisfaction with the suggestions. If dissatisfaction is indicated, the server re-evaluates the timing and amount of fertilizer application and generates new suggestions. This allows users to receive suggestions tailored to their own emotions.

[0357] Example of a prompt:

[0358] "Please explain how the unmanned aerial vehicle receives flight commands and collects data when a user inputs information about farmland. Please describe the specific process by which the analyzed information helps optimize agricultural activities."

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] The user uses a terminal to input information about the farmland. This input includes the location of the farmland, the type of crop, and the desired flight plan. The terminal sends this information to the server. The input in this process is data from the user, and the output is data sent to the server.

[0362] Step 2:

[0363] The server generates appropriate flight commands for the unmanned aerial vehicle (UAV) based on the data received from the terminal. This process involves calculating GPS coordinates and flight routes. The server then transmits these flight commands to the UAV. The input is data from the terminal, and the output is the flight commands sent to the UAV.

[0364] Step 3:

[0365] The unmanned aerial vehicle receives commands from a server, flies over farmland, and collects image data using a high-resolution camera. This data is transmitted to the server in real time. The input here is flight commands from the server, and the output is image data sent back to the server.

[0366] Step 4:

[0367] The server stores the received image data in a database and performs preprocessing. This preprocessing includes denoising and correcting the images. Then, a generative AI model is used to analyze the data and identify crop health and abnormalities. The input is image data from the unmanned aerial vehicle, and the output is the analysis results.

[0368] Step 5:

[0369] The server generates optimization information for agricultural activities based on the analysis results. This information includes fertilization schedules and irrigation plans. The generated information is sent to the terminal. The input is the analysis results, and the output is the optimization information.

[0370] Step 6:

[0371] The terminal presents the user with optimized information sent from the server. Simultaneously, it collects facial and vocal expressions using the camera and microphone to analyze the user's response using an emotion engine. The input here is the optimized information from the server and the user's response, and the output is the user's emotional state.

[0372] Step 7:

[0373] The server analyzes the user's emotional information obtained from the emotion engine and customizes the suggestions as needed. If the user is dissatisfied, the server re-evaluates the suggestions, adds new information, and makes improvements. The input is the user's emotional state, and the output is customized suggestion information.

[0374] (Application Example 2)

[0375] 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."

[0376] Conventional farmland management systems have a problem in that their suggestions, based on the analysis of terrain data acquired using aircraft, are solely based on the data itself and do not take into account the subjective satisfaction or emotional state of the users, thus failing to adequately meet the actual needs of the users. Similarly, in factories, robot work plans do not reflect the emotional state of the workers, which can lead to decreased work efficiency and satisfaction.

[0377] 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. In this invention, the server includes means for acquiring image information of the terrain area using an aircraft, means for analyzing the image information to detect the health status or abnormalities of plants, means for generating information that proposes improvements to agricultural activities based on the analyzed data, means for identifying the emotional state of the user using an emotion analysis device, and means for adjusting the proposals for the user based on the emotional state. As a result, by making proposals that take into account the emotional state of the user, efficient and highly satisfying management that meets actual needs becomes possible in both agricultural activities and factory work planning.

[0378] A "flying object" is a mechanical device used to fly over a terrain area and acquire image information.

[0379] "Image information" refers to visual data of the terrain area acquired by sensors and cameras mounted on the aircraft.

[0380] "Health status" refers to biological indicators that show whether plants or crops are growing normally.

[0381] An "abnormality" is a condition or symptom that deviates from the standard growth pattern in an organism.

[0382] An "emotion analysis device" is a combination of hardware and software used to identify a user's emotional state.

[0383] "Suggestions" refer to specific instructions or advice given based on collected data and emotional states.

[0384] A "user" is an individual or organization that uses this system to perform tasks or receive information.

[0385] The system that realizes this invention detects the health and abnormalities of plants based on image information of the terrain area collected by an aircraft, and further provides improvement suggestions that take into account the user's emotional state.

[0386] The server receives high-resolution image information transmitted from the aircraft and stores it in a database. After preprocessing, this image data is analyzed using a generative AI model (e.g., OpenAI GPT-4) to identify the health status and abnormalities of plants. Based on the information extracted through the analysis, specific suggestions aimed at improving agricultural activities are generated. At this stage, user emotion data is collected using an emotion analysis device, and the user's emotional state is identified using facial expression analysis software (e.g., Microsoft Azure Emotion API).

[0387] The terminal displays and presents suggestions supplied from the server to the user. These suggestions are customized according to the user's emotional state; for example, if the user is stressed, the suggestions may include adjustments to reduce their workload. This system allows users to receive optimal suggestions based on their emotional state, improving work efficiency and satisfaction.

[0388] A concrete example is the operational management of robots in factories. By using unmanned aerial vehicles to monitor the status of equipment from above within the factory and adjusting the robots' work schedules and movements according to the stress levels of the staff, it is expected that the overall efficiency of the work environment will improve.

[0389] An example of a prompt message might be, "If the user's emotional state is stressed, how should the suggestions for improving agricultural activities be adjusted?"

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The server receives terrain image data from the aircraft. The received data is first converted to an appropriate format and stored in the database. The input is image data acquired from the aircraft, which is used to save data to the database. The output is the accumulation of data in the database.

[0393] Step 2:

[0394] The server preprocesses image data stored in the database and uses a generative AI model to analyze the health and abnormalities of plants. The input here is preprocessed image data, and the generative AI model extracts and analyzes features from the data, resulting in output of plant health information. Specifically, it identifies color and shape patterns within the image to evaluate the plant's health.

[0395] Step 3:

[0396] The server generates suggested information aimed at improving agricultural activities based on the analysis results. The input is the plant health information obtained in step 2, and improvement suggestions are generated based on this. The output is specific improvement plans and measures. By using prompts to consider how the suggestions can be implemented, more specific output can be obtained.

[0397] Step 4:

[0398] The terminal displays suggestions received from the server and provides them to the user. Input consists of improvement suggestions sent from the server; these suggestions are presented on the display device, allowing the user to review them. Output is the presentation of suggestion information to the user. Specifically, the data is visualized in a visually easy-to-understand format, making it easy for the user to receive and process.

[0399] Step 5:

[0400] The device uses a camera and microphone to collect the user's face and voice, and an emotion analysis device identifies the user's emotional state. The input consists of the user's facial image and voice data, and the emotion analysis device analyzes this data to determine the emotional state. The output is information about the user's emotional state. Specifically, facial recognition software detects subtle changes in facial expression and determines the emotion based on that.

[0401] Step 6:

[0402] The server takes the user's emotional state into consideration and adjusts the suggested information accordingly. The input consists of the user's emotional state information and initial suggested information, and the adjusted suggestions are output. Specifically, the emotional information is incorporated into the generating AI model via prompt messages, and new suggestions are generated. An example of such a prompt message would be, "If the user's emotional state is stress, how should the suggestions for improving agricultural activities be adjusted?"

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] This invention is an integrated system designed to address diverse management needs in agriculture, utilizing aircraft, particularly unmanned aerial vehicles, to achieve efficient farmland management. The specific details of how each component of the system works in conjunction will be explained.

[0420] In this invention, the user first identifies the farmland to be managed and sets its geographical information and flight schedule. This information is input into the system via a terminal and forms the basis of the flight plan executed by the aircraft. The server receives this information and issues appropriate instructions to the aircraft.

[0421] The drone autonomously flies within a designated area and acquires image data of the farmland using a high-resolution camera. Multispectral imaging technology can be used for this, allowing for the capture of crop health and growth conditions across various spectral ranges.

[0422] Image data is transmitted wirelessly to a server, securely stored, and then undergoes preprocessing before proceeding to analysis. During the analysis, a generative AI model processes the image data to understand the growth status and detect abnormalities. For example, it can identify diseases or nutrient deficiencies based on changes in leaf color and pattern.

[0423] Based on the analysis results, the server generates specific information to optimize agricultural activities. This includes suggestions for fertilization schedules, irrigation timing, and guidelines for disease control. The generated information is displayed on terminals through dashboards and applications, making it easily accessible to users.

[0424] For example, if a drone captures images of a cornfield and detects discoloration of the leaves, the server can determine that this is a specific disease and suggest necessary control and preventative measures to the user. Based on this information, the user can effectively manage their farmland.

[0425] This system allows farmers to quickly and effectively understand the condition of their farmland and take appropriate measures to improve productivity.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The user sets the geographical information of the farmland and the flight plan on the terminal and inputs the necessary data into the system. This includes the location information of the farmland, the frequency of image acquisition, and the flight altitude.

[0429] Step 2:

[0430] The server transmits the flight plan details to the aircraft and prepares the drone to fly autonomously based on the specified conditions.

[0431] Step 3:

[0432] The drone flies towards its destination, following a pre-set trajectory and capturing images of the farmland with its high-resolution camera. The captured image data is transmitted to a server in real time.

[0433] Step 4:

[0434] The server stores the received image data in a database and performs preprocessing such as noise reduction and image shaping. This prepares the data for analysis.

[0435] Step 5:

[0436] The server uses a generative AI model to analyze pre-processed image data and identify crop growth conditions and abnormalities. This analysis is performed by detecting changes in specific patterns and color indices.

[0437] Step 6:

[0438] The server generates information to optimize agricultural activities based on the analysis results. This includes schedules for fertilization and irrigation, and specific plans for disease control.

[0439] Step 7:

[0440] The server sends the generated information to the terminal, which then presents it to the user using a dashboard. This allows the user to quickly take the necessary actions.

[0441] Step 8:

[0442] Users implement on-site management measures based on the provided information and provide feedback on the results via their devices. This feedback is sent to the server as information to improve the accuracy of data analysis in the future.

[0443] (Example 1)

[0444] 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."

[0445] Managing farmland in agriculture is difficult due to its vast scale. Furthermore, the inability to monitor crop growth and detect diseases early can delay the planning and execution of appropriate agricultural activities, leading to decreased productivity. Solving these problems and improving productivity is therefore essential.

[0446] 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.

[0447] In this invention, the server includes means for acquiring geographical area data using an aircraft, means for analyzing the data using a generative AI model, and means for generating information that proposes improvements to agricultural work based on the analyzed data. This makes it possible to efficiently grasp the conditions of farmland over a wide area and to quickly and effectively optimize agricultural activities.

[0448] "Aircraft" refers to devices that move through the air, such as unmanned aerial vehicles and drones, used to collect data in a geographical area.

[0449] A "generative AI model" refers to an artificial intelligence system that utilizes machine learning algorithms to gain insights and perform analysis from large amounts of data.

[0450] "Wireless communication" refers to a communication method that uses radio waves to send and receive data, and is a technology that transmits data without requiring physical connections such as cables.

[0451] "Map information" refers to data that represents geographical location and terrain, and is used when constructing flight plans for aircraft.

[0452] "Analyzed data" refers to data that has been processed and analyzed by a generative AI model, thereby adding value as information.

[0453] "Improving agricultural practices" refers to reviewing and optimizing plans and methods to increase crop productivity and streamline agricultural activities.

[0454] This invention is a system for streamlining agricultural management, which uses drones (unmanned aerial vehicles) to collect information about farmland, analyzes the data using a generated AI model, and optimizes agricultural activities.

[0455] First, the user sets the farmland to be managed. Using their device, the user inputs geographical information and flight schedules for the farmland they wish to manage. This information is then sent to the server. The device displays map information in an intuitive manner, allowing the user to create a flight plan.

[0456] Next, the server issues flight commands to the drone based on the transmitted data. The drone autonomously flies within the designated area according to the specified flight schedule. During flight, the drone acquires image data of the farmland using a high-resolution camera and a multispectral imaging sensor. The image data is transmitted to the server via wireless communication.

[0457] The server receives the acquired image data and analyzes it using a generative AI model. The AI ​​model processes and analyzes the data to recognize the growth status of crops and signs of abnormalities. This analysis allows for the detection of changes in the color and shape of crop leaves, enabling the early detection of potential diseases.

[0458] The server utilizes the analysis results to generate suggestions for fertilization schedules and irrigation timings, as well as guidelines for disease control. This information is provided to the user via a terminal. Users can then use the information provided on their terminal to improve their agricultural practices.

[0459] For example, if a drone captures images of a cornfield and detects areas where the leaves have turned yellow, the server uses a generative AI model to analyze this and determine that it is due to a nitrogen deficiency. It then proposes an appropriate fertilization plan, enabling the user to take swift action.

[0460] An example of a prompt message is as follows: "Use a generative AI model to analyze the latest multispectral imaging data of a cornfield and evaluate its growth status. If any abnormalities are found, describe them in detail and submit a proposed solution."

[0461] This system enables farmers to quickly and accurately grasp the conditions of farmland across a wide area and implement appropriate agricultural management.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The user uses a terminal to input geographical information about the farmland they wish to manage and the flight schedule. The entered information is sent to the server as data including GPS coordinates, flight routes, and scheduled times. This establishes the target area of ​​the farmland to be managed and the basic information of the flight plan.

[0465] Step 2:

[0466] The server receives geographical information and flight schedules sent by the user. Based on this data, it generates specific flight commands for drones, which are unmanned aerial vehicles. As part of the data processing, an algorithm is applied to optimize the transmitted schedule and reflect it in each flight session. The output is instruction data showing the actual flight path and timing.

[0467] Step 3:

[0468] The server transmits flight command data to the drone via wireless communication. The drone receives this command and autonomously flies over the designated farmland. During flight, the onboard high-resolution camera and multispectral imaging sensor are activated to acquire image data of the farmland in real time. Flight command data is the input, and image data of the farmland is the output.

[0469] Step 4:

[0470] The acquired image data is transmitted wirelessly from the drone to the server. Upon receiving this data, the server performs initial data processing, including image file compression / decompression and noise filtering. The pre-processed image data becomes its output.

[0471] Step 5:

[0472] The server analyzes pre-processed image data using a generative AI model. During this process, the AI ​​algorithm detects patterns to identify crop health and growth abnormalities. It identifies leaf color, shape, and other indicators, and records details of any abnormalities found. The input is pre-processed image data, and the output is the anomaly detection result.

[0473] Step 6:

[0474] Based on the analysis results, the server automatically generates specific improvement suggestions, such as fertilization schedules, irrigation timings, and disease control measures. These results are sent to the terminal as suggested information in text format. The output obtained by the server is optimized agricultural guidance information.

[0475] Step 7:

[0476] The terminal receives suggested information sent from the server and displays it in a user-friendly interface on the dashboard. Based on this information, the user plans and implements practical steps for farm management. At this stage, the user is ready to take concrete action based on the outputted improvement instructions.

[0477] (Application Example 1)

[0478] 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."

[0479] When efficiently monitoring and managing production processes in manufacturing facilities, conventional methods have limitations in productivity improvement due to the difficulty in rapidly detecting anomalies and optimizing production activities. In particular, there is a need to immediately detect and address equipment deterioration and abnormal product placement.

[0480] 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.

[0481] In this invention, the server includes means for acquiring image data of a production facility area using an imaging device, means for analyzing the image data to detect production processes or anomalies, and means for generating information that proposes optimization of production activities based on the analyzed data. This enables real-time monitoring of production processes and rapid anomaly detection.

[0482] An "imaging device" is a device used to acquire image data of a production facility area, and it is a device that converts visual information into electrical signals using optical means.

[0483] A "mobile object" is a machine or device that has the function of collecting data while patrolling a specific area, and that can operate according to the situation.

[0484] "Analysis means" refers to a process or apparatus for processing acquired image data using an algorithm or method to extract information or detect anomalies.

[0485] An "optimization proposal" is a set of methods or information for generating and providing to the user work plans and improvement measures to enhance efficiency based on analyzed data.

[0486] A "production facility" refers to a place or equipment where goods are manufactured, processed, or assembled, and encompasses an environment that includes various manufacturing processes.

[0487] To implement this invention, it is necessary to construct a system for monitoring and managing production processes within a production facility. The main components of the system include an imaging device mounted on a mobile device, a server for data processing, and a terminal for users to view this information.

[0488] The imaging device is mounted on a mobile vehicle and patrols the production facility, periodically acquiring image data of the area. This image data is transmitted to a server via wireless communication, where it is analyzed using a high-performance generative AI model. During the data analysis process, the server detects anomalies in the production process and generates suggestions for optimizing production activities based on that information.

[0489] The generated optimization suggestions are provided to the user in real time via a terminal. The user can use the terminal to monitor the production process at their facility and respond quickly if an anomaly occurs. For example, if a misalignment of a product on the same line is detected, the user can immediately begin corrective work.

[0490] An example of a prompt in a generative AI model is, "Check the position of the product on the conveyor belt and determine if there are any abnormalities." By using this prompt, the AI ​​model can process the data appropriately and provide reliable analysis results.

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The terminal controls an imaging device mounted on a mobile vehicle, acquiring image data at regular intervals while patrolling the production facility. In this process, the terminal provides navigation instructions to the mobile vehicle, and the imaging device captures images using optical means. The input is visual information of the production facility, and the output is the acquired image data.

[0494] Step 2:

[0495] The server receives the retrieved image data and preprocesses it. Preprocessing includes noise reduction and image resizing. The input is the acquired image data, and the output is preprocessed data in a format suitable for analysis. This prepares the system for efficient analysis by the generative AI model.

[0496] Step 3:

[0497] The server inputs pre-processed data along with the prompt message "Check the product positions on the conveyor belt and determine if there are any abnormalities" into the generated AI model and performs the analysis. The AI ​​model analyzes the data in detail and detects abnormalities in the production process and product placement. The output is the abnormality detection result based on the analysis. At this step, the presence or absence of abnormalities becomes clear.

[0498] Step 4:

[0499] The server generates optimization suggestions based on the analysis results. These suggestions include methods for correcting anomalies and improvements to the production process. The input is the anomaly detection result, and the output is the content of the suggestions. The generated suggestions lead to increased efficiency in the production process and faster problem solving.

[0500] Step 5:

[0501] The terminal provides the user with generated optimization suggestions in real time. The user reviews the suggestions via the terminal and takes action as needed. The input is the content of the suggestions, and the output is information that leads to user action. In this step, the user can start taking action immediately.

[0502] 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.

[0503] This invention enables the more effective provision of information for optimizing agricultural activities to users by incorporating an emotion engine that recognizes user emotions into a farmland management system that utilizes an aerial vehicle. This system aims to improve user satisfaction by providing not only the information necessary for farmland management but also suggestions that reflect the user's emotional state.

[0504] Specifically, the user first inputs information about the farmland to be managed and the flight plan into the system via a terminal. Based on this, the server issues appropriate flight commands to the aircraft. The aircraft, specifically the unmanned aerial vehicle, flies over the designated farmland, and the image data acquired by the high-resolution camera is transmitted to the server in real time.

[0505] The transmitted image data is stored in the server's database and, after preprocessing, is analyzed. The analysis uses a generative AI model to identify crop health and abnormalities, and generates information for optimizing agricultural activities based on the collected data. This information includes specific suggestions such as fertilization schedules, irrigation plans, and disease control measures.

[0506] Furthermore, this system includes an emotion engine, which allows the server to analyze user responses and understand their emotional state. This is done through facial and vocal expression analysis when providing information to the user via the terminal. The emotional information recognized by the emotion engine is used to customize the suggestions provided.

[0507] For example, if a user expresses dissatisfaction with a suggestion, the system re-evaluates the suggestion and improves it by adding new information. If the suggestion meets the user's expectations, the same approach is continued.

[0508] Thus, the present invention enables agricultural workers to receive not only standard farmland management information but also suggestions tailored to their own usage experience. This improves both the efficiency of agricultural activities and user satisfaction.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] Users configure detailed information about the farmland's geographical location, intended management activities, and flight schedules on their terminals and input this information into the system. This input includes details such as the farmland's coordinates, the frequency of photography, and the flight altitude.

[0512] Step 2:

[0513] The server formulates a flight plan based on the received information and sends specific commands to the aircraft. These commands include the flight path and shooting locations.

[0514] Step 3:

[0515] The drone, acting as the aircraft, receives commands from the server and flies over designated farmland, acquiring image data of the farmland from above using a high-resolution camera. The acquired image data is transmitted to the server in real time.

[0516] Step 4:

[0517] The server securely stores the acquired image data in a database and then performs image preprocessing. This preprocessing includes noise reduction and resolution adjustment.

[0518] Step 5:

[0519] The server inputs pre-processed image data into a generating AI model to analyze crop growth conditions and any abnormalities. Based on the information obtained from this analysis, it generates specific suggestions for optimizing agricultural activities, such as fertilization schedules and irrigation plans.

[0520] Step 6:

[0521] The generated suggestion information is provided to the user via the terminal. Here, the emotion engine recognizes emotions through the user's facial and voice input and analyzes the user's response.

[0522] Step 7:

[0523] The server uses recognized user sentiment information to adjust the suggestions in real time. If the user appears dissatisfied, it re-evaluates the suggestions and provides additional information.

[0524] Step 8:

[0525] Users implement farm management based on the improved suggestions and report the results and feedback to the system via their devices. This feedback helps improve the accuracy of the emotion engine and refine future suggestions.

[0526] (Example 2)

[0527] 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."

[0528] In agriculture, there is a need to quickly and accurately grasp the health and abnormalities of crops and manage them efficiently. However, current technology lacks the ability to provide information that takes into account the user's emotional state. Therefore, the challenge is to provide a system that optimizes agricultural activities while improving user satisfaction.

[0529] 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.

[0530] In this invention, the server includes means for acquiring image data of the ground area using an aircraft, means for analyzing the image data to detect the growth state or abnormalities of plants, means for generating information that proposes optimization of agricultural activities based on the analyzed data, and means for analyzing the user's reactions to recognize the user's emotional state and customizing the optimization proposal information. This makes it possible to provide information that is tailored to the user's emotions, thereby improving the efficiency of agricultural activities and increasing user satisfaction.

[0531] A "flying object" is a mechanical device that can collect information from a designated point while flying through the air.

[0532] "Image data" refers to visual information of the ground surface area acquired from an aircraft, and it forms the basis for analysis and interpretation.

[0533] "Analysis" refers to an information processing technique that involves processing acquired image data to identify the growth status and abnormalities of plants.

[0534] "Plant growth status" refers to information indicating the degree of plant growth and health, and is important in managing agricultural activities.

[0535] "Abnormality" refers to a deviation from the normal state of plant growth, including the effects of diseases and environmental stress.

[0536] "Optimization proposal information" refers to information created based on analysis results with the aim of improving the efficiency of agricultural activities, and includes specific plans such as fertilization and irrigation.

[0537] "Users" refer to individuals or groups who utilize the system in agricultural activities, and their emotional state and needs must be taken into consideration.

[0538] "Emotional state" refers to the user's mental response to the suggested information, and includes states such as satisfaction and dissatisfaction.

[0539] "Analysis means" refers to technical methods or functions for processing information to achieve a specific purpose.

[0540] A "server" refers to a centralized computing device used for storing, processing, analyzing, and providing data results.

[0541] This invention is a farmland management system designed to streamline agricultural activities and improve user satisfaction. Its most distinctive feature is its ability to analyze data collected by unmanned aerial vehicles and provide suggestions that take into account the user's emotional state.

[0542] First, the user uses a device to input information about the farmland to be managed. This device can be a tablet or personal computer and has an intuitive interface for the user. The data entered by the user includes geographical information, crop types, and flight plans.

[0543] Next, the server receives the data and sends flight commands to the unmanned aerial vehicle (UAV). This UAV is equipped with a high-resolution camera and acquires image data while flying over a designated area of ​​the ground. The UAV uses GPS for precise location identification and flies along a planned route. The collected image data is transmitted to the server in real time and stored in a database.

[0544] The server preprocesses this image data and then analyzes it using a generated AI model. This model identifies crop health and abnormalities and generates information to optimize agricultural activities. This optimization information includes fertilization schedules, irrigation plans, and disease control measures.

[0545] Furthermore, the system uses an emotion engine to analyze user reactions. The device captures the user's facial expressions through its camera and records audio with its microphone. This allows the system to understand the user's emotional state and customize suggestions based on that information.

[0546] For example, if a farmer wants to maximize their yield, the emotion engine evaluates the farmer's satisfaction with the suggestions. If dissatisfaction is indicated, the server re-evaluates the timing and amount of fertilizer application and generates new suggestions. This allows users to receive suggestions tailored to their own emotions.

[0547] Example of a prompt:

[0548] "Please explain how the unmanned aerial vehicle receives flight commands and collects data when a user inputs information about farmland. Please describe the specific process by which the analyzed information helps optimize agricultural activities."

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The user uses a terminal to input information about the farmland. This input includes the location of the farmland, the type of crop, and the desired flight plan. The terminal sends this information to the server. The input in this process is data from the user, and the output is data sent to the server.

[0552] Step 2:

[0553] The server generates appropriate flight commands for the unmanned aerial vehicle (UAV) based on the data received from the terminal. This process involves calculating GPS coordinates and flight routes. The server then transmits these flight commands to the UAV. The input is data from the terminal, and the output is the flight commands sent to the UAV.

[0554] Step 3:

[0555] The unmanned aerial vehicle receives commands from a server, flies over farmland, and collects image data using a high-resolution camera. This data is transmitted to the server in real time. The input here is flight commands from the server, and the output is image data sent back to the server.

[0556] Step 4:

[0557] The server stores the received image data in a database and performs preprocessing. This preprocessing includes denoising and correcting the images. Then, a generative AI model is used to analyze the data and identify crop health and abnormalities. The input is image data from the unmanned aerial vehicle, and the output is the analysis results.

[0558] Step 5:

[0559] The server generates optimization information for agricultural activities based on the analysis results. This information includes fertilization schedules and irrigation plans. The generated information is sent to the terminal. The input is the analysis results, and the output is the optimization information.

[0560] Step 6:

[0561] The terminal presents the user with optimized information sent from the server. Simultaneously, it collects facial and vocal expressions using the camera and microphone to analyze the user's response using an emotion engine. The input here is the optimized information from the server and the user's response, and the output is the user's emotional state.

[0562] Step 7:

[0563] The server analyzes the user's emotional information obtained from the emotion engine and customizes the suggestions as needed. If the user is dissatisfied, the server re-evaluates the suggestions, adds new information, and makes improvements. The input is the user's emotional state, and the output is customized suggestion information.

[0564] (Application Example 2)

[0565] 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."

[0566] Conventional farmland management systems have a problem in that their suggestions, based on the analysis of terrain data acquired using aircraft, are solely based on the data itself and do not take into account the subjective satisfaction or emotional state of the users, thus failing to adequately meet the actual needs of the users. Similarly, in factories, robot work plans do not reflect the emotional state of the workers, which can lead to decreased work efficiency and satisfaction.

[0567] 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. In this invention, the server includes means for acquiring image information of the terrain area using an aircraft, means for analyzing the image information to detect the health status or abnormalities of plants, means for generating information that proposes improvements to agricultural activities based on the analyzed data, means for identifying the emotional state of the user using an emotion analysis device, and means for adjusting the proposals for the user based on the emotional state. As a result, by making proposals that take into account the emotional state of the user, efficient and highly satisfying management that meets actual needs becomes possible in both agricultural activities and factory work planning.

[0568] A "flying object" is a mechanical device used to fly over a terrain area and acquire image information.

[0569] "Image information" refers to visual data of the terrain area acquired by sensors and cameras mounted on the aircraft.

[0570] "Health status" refers to biological indicators that show whether plants or crops are growing normally.

[0571] An "abnormality" is a condition or symptom that deviates from the standard growth pattern in an organism.

[0572] An "emotion analysis device" is a combination of hardware and software used to identify a user's emotional state.

[0573] "Suggestions" refer to specific instructions or advice given based on collected data and emotional states.

[0574] A "user" is an individual or organization that uses this system to perform tasks or receive information.

[0575] The system that realizes this invention detects the health and abnormalities of plants based on image information of the terrain area collected by an aircraft, and further provides improvement suggestions that take into account the user's emotional state.

[0576] The server receives high-resolution image information transmitted from the aircraft and stores it in a database. After preprocessing, this image data is analyzed using a generative AI model (e.g., OpenAI GPT-4) to identify the health status and abnormalities of plants. Based on the information extracted through the analysis, specific suggestions aimed at improving agricultural activities are generated. At this stage, user emotion data is collected using an emotion analysis device, and the user's emotional state is identified using facial expression analysis software (e.g., Microsoft Azure Emotion API).

[0577] The terminal displays and presents suggestions supplied from the server to the user. These suggestions are customized according to the user's emotional state; for example, if the user is stressed, the suggestions may include adjustments to reduce their workload. This system allows users to receive optimal suggestions based on their emotional state, improving work efficiency and satisfaction.

[0578] A concrete example is the operational management of robots in factories. By using unmanned aerial vehicles to monitor the status of equipment from above within the factory and adjusting the robots' work schedules and movements according to the stress levels of the staff, it is expected that the overall efficiency of the work environment will improve.

[0579] An example of a prompt message might be, "If the user's emotional state is stressed, how should the suggestions for improving agricultural activities be adjusted?"

[0580] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0581] Step 1:

[0582] The server receives terrain image data from the aircraft. The received data is first converted to an appropriate format and stored in the database. The input is image data acquired from the aircraft, which is used to save data to the database. The output is the accumulation of data in the database.

[0583] Step 2:

[0584] The server preprocesses image data stored in the database and uses a generative AI model to analyze the health and abnormalities of plants. The input here is preprocessed image data, and the generative AI model extracts and analyzes features from the data, resulting in output of plant health information. Specifically, it identifies color and shape patterns within the image to evaluate the plant's health.

[0585] Step 3:

[0586] The server generates suggested information aimed at improving agricultural activities based on the analysis results. The input is the plant health information obtained in step 2, and improvement suggestions are generated based on this. The output is specific improvement plans and measures. By using prompts to consider how the suggestions can be implemented, more specific output can be obtained.

[0587] Step 4:

[0588] The terminal displays suggestions received from the server and provides them to the user. Input consists of improvement suggestions sent from the server; these suggestions are presented on the display device, allowing the user to review them. Output is the presentation of suggestion information to the user. Specifically, the data is visualized in a visually easy-to-understand format, making it easy for the user to receive and process.

[0589] Step 5:

[0590] The device uses a camera and microphone to collect the user's face and voice, and an emotion analysis device identifies the user's emotional state. The input consists of the user's facial image and voice data, and the emotion analysis device analyzes this data to determine the emotional state. The output is information about the user's emotional state. Specifically, facial recognition software detects subtle changes in facial expression and determines the emotion based on that.

[0591] Step 6:

[0592] The server takes the user's emotional state into consideration and adjusts the suggested information accordingly. The input consists of the user's emotional state information and initial suggested information, and the adjusted suggestions are output. Specifically, the emotional information is incorporated into the generating AI model via prompt messages, and new suggestions are generated. An example of such a prompt message would be, "If the user's emotional state is stress, how should the suggestions for improving agricultural activities be adjusted?"

[0593] 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.

[0594] 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.

[0595] 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.

[0596] [Fourth Embodiment]

[0597] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0598] 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.

[0599] 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).

[0600] 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.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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".

[0610] This invention is an integrated system designed to address diverse management needs in agriculture, utilizing aircraft, particularly unmanned aerial vehicles, to achieve efficient farmland management. The specific details of how each component of the system works in conjunction will be explained.

[0611] In this invention, the user first identifies the farmland to be managed and sets its geographical information and flight schedule. This information is input into the system via a terminal and forms the basis of the flight plan executed by the aircraft. The server receives this information and issues appropriate instructions to the aircraft.

[0612] The drone autonomously flies within a designated area and acquires image data of the farmland using a high-resolution camera. Multispectral imaging technology can be used for this, allowing for the capture of crop health and growth conditions across various spectral ranges.

[0613] Image data is transmitted wirelessly to a server, securely stored, and then undergoes preprocessing before proceeding to analysis. During the analysis, a generative AI model processes the image data to understand the growth status and detect abnormalities. For example, it can identify diseases or nutrient deficiencies based on changes in leaf color and pattern.

[0614] Based on the analysis results, the server generates specific information to optimize agricultural activities. This includes suggestions for fertilization schedules, irrigation timing, and guidelines for disease control. The generated information is displayed on terminals through dashboards and applications, making it easily accessible to users.

[0615] For example, if a drone captures images of a cornfield and detects discoloration of the leaves, the server can determine that this is a specific disease and suggest necessary control and preventative measures to the user. Based on this information, the user can effectively manage their farmland.

[0616] This system allows farmers to quickly and effectively understand the condition of their farmland and take appropriate measures to improve productivity.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] The user sets the geographical information of the farmland and the flight plan on the terminal and inputs the necessary data into the system. This includes the location information of the farmland, the frequency of image acquisition, and the flight altitude.

[0620] Step 2:

[0621] The server transmits the flight plan details to the aircraft and prepares the drone to fly autonomously based on the specified conditions.

[0622] Step 3:

[0623] The drone flies towards its destination, following a pre-set trajectory and capturing images of the farmland with its high-resolution camera. The captured image data is transmitted to a server in real time.

[0624] Step 4:

[0625] The server stores the received image data in a database and performs preprocessing such as noise reduction and image shaping. This prepares the data for analysis.

[0626] Step 5:

[0627] The server uses a generative AI model to analyze pre-processed image data and identify crop growth conditions and abnormalities. This analysis is performed by detecting changes in specific patterns and color indices.

[0628] Step 6:

[0629] The server generates information to optimize agricultural activities based on the analysis results. This includes schedules for fertilization and irrigation, and specific plans for disease control.

[0630] Step 7:

[0631] The server sends the generated information to the terminal, which then presents it to the user using a dashboard. This allows the user to quickly take the necessary actions.

[0632] Step 8:

[0633] Users implement on-site management measures based on the provided information and provide feedback on the results via their devices. This feedback is sent to the server as information to improve the accuracy of data analysis in the future.

[0634] (Example 1)

[0635] 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".

[0636] Managing farmland in agriculture is difficult due to its vast scale. Furthermore, the inability to monitor crop growth and detect diseases early can delay the planning and execution of appropriate agricultural activities, leading to decreased productivity. Solving these problems and improving productivity is therefore essential.

[0637] 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.

[0638] In this invention, the server includes means for acquiring geographical area data using an aircraft, means for analyzing the data using a generative AI model, and means for generating information that proposes improvements to agricultural work based on the analyzed data. This makes it possible to efficiently grasp the conditions of farmland over a wide area and to quickly and effectively optimize agricultural activities.

[0639] "Aircraft" refers to devices that move through the air, such as unmanned aerial vehicles and drones, used to collect data in a geographical area.

[0640] A "generative AI model" refers to an artificial intelligence system that utilizes machine learning algorithms to gain insights and perform analysis from large amounts of data.

[0641] "Wireless communication" refers to a communication method that uses radio waves to send and receive data, and is a technology that transmits data without requiring physical connections such as cables.

[0642] "Map information" refers to data that represents geographical location and terrain, and is used when constructing flight plans for aircraft.

[0643] "Analyzed data" refers to data that has been processed and analyzed by a generative AI model, thereby adding value as information.

[0644] "Improving agricultural practices" refers to reviewing and optimizing plans and methods to increase crop productivity and streamline agricultural activities.

[0645] This invention is a system for streamlining agricultural management, which uses drones (unmanned aerial vehicles) to collect information about farmland, analyzes the data using a generated AI model, and optimizes agricultural activities.

[0646] First, the user sets the farmland to be managed. Using their device, the user inputs geographical information and flight schedules for the farmland they wish to manage. This information is then sent to the server. The device displays map information in an intuitive manner, allowing the user to create a flight plan.

[0647] Next, the server issues flight commands to the drone based on the transmitted data. The drone autonomously flies within the designated area according to the specified flight schedule. During flight, the drone acquires image data of the farmland using a high-resolution camera and a multispectral imaging sensor. The image data is transmitted to the server via wireless communication.

[0648] The server receives the acquired image data and analyzes it using a generative AI model. The AI ​​model processes and analyzes the data to recognize the growth status of crops and signs of abnormalities. This analysis allows for the detection of changes in the color and shape of crop leaves, enabling the early detection of potential diseases.

[0649] The server utilizes the analysis results to generate suggestions for fertilization schedules and irrigation timings, as well as guidelines for disease control. This information is provided to the user via a terminal. Users can then use the information provided on their terminal to improve their agricultural practices.

[0650] For example, if a drone captures images of a cornfield and detects areas where the leaves have turned yellow, the server uses a generative AI model to analyze this and determine that it is due to a nitrogen deficiency. It then proposes an appropriate fertilization plan, enabling the user to take swift action.

[0651] An example of a prompt message is as follows: "Use a generative AI model to analyze the latest multispectral imaging data of a cornfield and evaluate its growth status. If any abnormalities are found, describe them in detail and submit a proposed solution."

[0652] This system enables farmers to quickly and accurately grasp the conditions of farmland across a wide area and implement appropriate agricultural management.

[0653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0654] Step 1:

[0655] The user uses a terminal to input geographical information about the farmland they wish to manage and the flight schedule. The entered information is sent to the server as data including GPS coordinates, flight routes, and scheduled times. This establishes the target area of ​​the farmland to be managed and the basic information of the flight plan.

[0656] Step 2:

[0657] The server receives geographical information and flight schedules sent by the user. Based on this data, it generates specific flight commands for drones, which are unmanned aerial vehicles. As part of the data processing, an algorithm is applied to optimize the transmitted schedule and reflect it in each flight session. The output is instruction data showing the actual flight path and timing.

[0658] Step 3:

[0659] The server transmits flight command data to the drone via wireless communication. The drone receives this command and autonomously flies over the designated farmland. During flight, the onboard high-resolution camera and multispectral imaging sensor are activated to acquire image data of the farmland in real time. Flight command data is the input, and image data of the farmland is the output.

[0660] Step 4:

[0661] The acquired image data is transmitted wirelessly from the drone to the server. Upon receiving this data, the server performs initial data processing, including image file compression / decompression and noise filtering. The pre-processed image data becomes its output.

[0662] Step 5:

[0663] The server analyzes pre-processed image data using a generative AI model. During this process, the AI ​​algorithm detects patterns to identify crop health and growth abnormalities. It identifies leaf color, shape, and other indicators, and records details of any abnormalities found. The input is pre-processed image data, and the output is the anomaly detection result.

[0664] Step 6:

[0665] Based on the analysis results, the server automatically generates specific improvement suggestions, such as fertilization schedules, irrigation timings, and disease control measures. These results are sent to the terminal as suggested information in text format. The output obtained by the server is optimized agricultural guidance information.

[0666] Step 7:

[0667] The terminal receives suggested information sent from the server and displays it in a user-friendly interface on the dashboard. Based on this information, the user plans and implements practical steps for farm management. At this stage, the user is ready to take concrete action based on the outputted improvement instructions.

[0668] (Application Example 1)

[0669] 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".

[0670] When efficiently monitoring and managing production processes in manufacturing facilities, conventional methods have limitations in productivity improvement due to the difficulty in rapidly detecting anomalies and optimizing production activities. In particular, there is a need to immediately detect and address equipment deterioration and abnormal product placement.

[0671] 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.

[0672] In this invention, the server includes means for acquiring image data of a production facility area using an imaging device, means for analyzing the image data to detect production processes or anomalies, and means for generating information that proposes optimization of production activities based on the analyzed data. This enables real-time monitoring of production processes and rapid anomaly detection.

[0673] An "imaging device" is a device used to acquire image data of a production facility area, and it is a device that converts visual information into electrical signals using optical means.

[0674] A "mobile object" is a machine or device that has the function of collecting data while patrolling a specific area, and that can operate according to the situation.

[0675] "Analysis means" refers to a process or apparatus for processing acquired image data using an algorithm or method to extract information or detect anomalies.

[0676] An "optimization proposal" is a set of methods or information for generating and providing to the user work plans and improvement measures to enhance efficiency based on analyzed data.

[0677] A "production facility" refers to a place or equipment where goods are manufactured, processed, or assembled, and encompasses an environment that includes various manufacturing processes.

[0678] To implement this invention, it is necessary to construct a system for monitoring and managing production processes within a production facility. The main components of the system include an imaging device mounted on a mobile device, a server for data processing, and a terminal for users to view this information.

[0679] The imaging device is mounted on a mobile vehicle and patrols the production facility, periodically acquiring image data of the area. This image data is transmitted to a server via wireless communication, where it is analyzed using a high-performance generative AI model. During the data analysis process, the server detects anomalies in the production process and generates suggestions for optimizing production activities based on that information.

[0680] The generated optimization suggestions are provided to the user in real time via a terminal. The user can use the terminal to monitor the production process at their facility and respond quickly if an anomaly occurs. For example, if a misalignment of a product on the same line is detected, the user can immediately begin corrective work.

[0681] An example of a prompt in a generative AI model is, "Check the position of the product on the conveyor belt and determine if there are any abnormalities." By using this prompt, the AI ​​model can process the data appropriately and provide reliable analysis results.

[0682] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0683] Step 1:

[0684] The terminal controls an imaging device mounted on a mobile vehicle, acquiring image data at regular intervals while patrolling the production facility. In this process, the terminal provides navigation instructions to the mobile vehicle, and the imaging device captures images using optical means. The input is visual information of the production facility, and the output is the acquired image data.

[0685] Step 2:

[0686] The server receives the retrieved image data and preprocesses it. Preprocessing includes noise reduction and image resizing. The input is the acquired image data, and the output is preprocessed data in a format suitable for analysis. This prepares the system for efficient analysis by the generative AI model.

[0687] Step 3:

[0688] The server inputs pre-processed data along with the prompt message "Check the product positions on the conveyor belt and determine if there are any abnormalities" into the generated AI model and performs the analysis. The AI ​​model analyzes the data in detail and detects abnormalities in the production process and product placement. The output is the abnormality detection result based on the analysis. At this step, the presence or absence of abnormalities becomes clear.

[0689] Step 4:

[0690] The server generates optimization suggestions based on the analysis results. These suggestions include methods for correcting anomalies and improvements to the production process. The input is the anomaly detection result, and the output is the content of the suggestions. The generated suggestions lead to increased efficiency in the production process and faster problem solving.

[0691] Step 5:

[0692] The terminal provides the user with generated optimization suggestions in real time. The user reviews the suggestions via the terminal and takes action as needed. The input is the content of the suggestions, and the output is information that leads to user action. In this step, the user can start taking action immediately.

[0693] 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.

[0694] This invention enables the more effective provision of information for optimizing agricultural activities to users by incorporating an emotion engine that recognizes user emotions into a farmland management system that utilizes an aerial vehicle. This system aims to improve user satisfaction by providing not only the information necessary for farmland management but also suggestions that reflect the user's emotional state.

[0695] Specifically, the user first inputs information about the farmland to be managed and the flight plan into the system via a terminal. Based on this, the server issues appropriate flight commands to the aircraft. The aircraft, specifically the unmanned aerial vehicle, flies over the designated farmland, and the image data acquired by the high-resolution camera is transmitted to the server in real time.

[0696] The transmitted image data is stored in the server's database and, after preprocessing, is analyzed. The analysis uses a generative AI model to identify crop health and abnormalities, and generates information for optimizing agricultural activities based on the collected data. This information includes specific suggestions such as fertilization schedules, irrigation plans, and disease control measures.

[0697] Furthermore, this system includes an emotion engine, which allows the server to analyze user responses and understand their emotional state. This is done through facial and vocal expression analysis when providing information to the user via the terminal. The emotional information recognized by the emotion engine is used to customize the suggestions provided.

[0698] For example, if a user expresses dissatisfaction with a suggestion, the system re-evaluates the suggestion and improves it by adding new information. If the suggestion meets the user's expectations, the same approach is continued.

[0699] Thus, the present invention enables agricultural workers to receive not only standard farmland management information but also suggestions tailored to their own usage experience. This improves both the efficiency of agricultural activities and user satisfaction.

[0700] The following describes the processing flow.

[0701] Step 1:

[0702] Users configure detailed information about the farmland's geographical location, intended management activities, and flight schedules on their terminals and input this information into the system. This input includes details such as the farmland's coordinates, the frequency of photography, and the flight altitude.

[0703] Step 2:

[0704] The server formulates a flight plan based on the received information and sends specific commands to the aircraft. These commands include the flight path and shooting locations.

[0705] Step 3:

[0706] The drone, acting as the aircraft, receives commands from the server and flies over designated farmland, acquiring image data of the farmland from above using a high-resolution camera. The acquired image data is transmitted to the server in real time.

[0707] Step 4:

[0708] The server securely stores the acquired image data in a database and then performs image preprocessing. This preprocessing includes noise reduction and resolution adjustment.

[0709] Step 5:

[0710] The server inputs pre-processed image data into a generating AI model to analyze crop growth conditions and any abnormalities. Based on the information obtained from this analysis, it generates specific suggestions for optimizing agricultural activities, such as fertilization schedules and irrigation plans.

[0711] Step 6:

[0712] The generated suggestion information is provided to the user via the terminal. Here, the emotion engine recognizes emotions through the user's facial and voice input and analyzes the user's response.

[0713] Step 7:

[0714] The server uses recognized user sentiment information to adjust the suggestions in real time. If the user appears dissatisfied, it re-evaluates the suggestions and provides additional information.

[0715] Step 8:

[0716] Users implement farm management based on the improved suggestions and report the results and feedback to the system via their devices. This feedback helps improve the accuracy of the emotion engine and refine future suggestions.

[0717] (Example 2)

[0718] 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".

[0719] In agriculture, there is a need to quickly and accurately grasp the health and abnormalities of crops and manage them efficiently. However, current technology lacks the ability to provide information that takes into account the user's emotional state. Therefore, the challenge is to provide a system that optimizes agricultural activities while improving user satisfaction.

[0720] 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.

[0721] In this invention, the server includes means for acquiring image data of the ground area using an aircraft, means for analyzing the image data to detect the growth state or abnormalities of plants, means for generating information that proposes optimization of agricultural activities based on the analyzed data, and means for analyzing the user's reactions to recognize the user's emotional state and customizing the optimization proposal information. This makes it possible to provide information that is tailored to the user's emotions, thereby improving the efficiency of agricultural activities and increasing user satisfaction.

[0722] A "flying object" is a mechanical device that can collect information from a designated point while flying through the air.

[0723] "Image data" refers to visual information of the ground surface area acquired from an aircraft, and it forms the basis for analysis and interpretation.

[0724] "Analysis" refers to an information processing technique that involves processing acquired image data to identify the growth status and abnormalities of plants.

[0725] "Plant growth status" refers to information indicating the degree of plant growth and health, and is important in managing agricultural activities.

[0726] "Abnormality" refers to a deviation from the normal state of plant growth, including the effects of diseases and environmental stress.

[0727] "Optimization proposal information" refers to information created based on analysis results with the aim of improving the efficiency of agricultural activities, and includes specific plans such as fertilization and irrigation.

[0728] "Users" refer to individuals or groups who utilize the system in agricultural activities, and their emotional state and needs must be taken into consideration.

[0729] "Emotional state" refers to the user's mental response to the suggested information, and includes states such as satisfaction and dissatisfaction.

[0730] "Analysis means" refers to technical methods or functions for processing information to achieve a specific purpose.

[0731] A "server" refers to a centralized computing device used for storing, processing, analyzing, and providing data results.

[0732] This invention is a farmland management system designed to streamline agricultural activities and improve user satisfaction. Its most distinctive feature is its ability to analyze data collected by unmanned aerial vehicles and provide suggestions that take into account the user's emotional state.

[0733] First, the user uses a device to input information about the farmland to be managed. This device can be a tablet or personal computer and has an intuitive interface for the user. The data entered by the user includes geographical information, crop types, and flight plans.

[0734] Next, the server receives the data and sends flight commands to the unmanned aerial vehicle (UAV). This UAV is equipped with a high-resolution camera and acquires image data while flying over a designated area of ​​the ground. The UAV uses GPS for precise location identification and flies along a planned route. The collected image data is transmitted to the server in real time and stored in a database.

[0735] The server preprocesses this image data and then analyzes it using a generated AI model. This model identifies crop health and abnormalities and generates information to optimize agricultural activities. This optimization information includes fertilization schedules, irrigation plans, and disease control measures.

[0736] Furthermore, the system uses an emotion engine to analyze user reactions. The device captures the user's facial expressions through its camera and records audio with its microphone. This allows the system to understand the user's emotional state and customize suggestions based on that information.

[0737] For example, if a farmer wants to maximize their yield, the emotion engine evaluates the farmer's satisfaction with the suggestions. If dissatisfaction is indicated, the server re-evaluates the timing and amount of fertilizer application and generates new suggestions. This allows users to receive suggestions tailored to their own emotions.

[0738] Example of a prompt:

[0739] "Please explain how the unmanned aerial vehicle receives flight commands and collects data when a user inputs information about farmland. Please describe the specific process by which the analyzed information helps optimize agricultural activities."

[0740] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0741] Step 1:

[0742] The user uses a terminal to input information about the farmland. This input includes the location of the farmland, the type of crop, and the desired flight plan. The terminal sends this information to the server. The input in this process is data from the user, and the output is data sent to the server.

[0743] Step 2:

[0744] The server generates appropriate flight commands for the unmanned aerial vehicle (UAV) based on the data received from the terminal. This process involves calculating GPS coordinates and flight routes. The server then transmits these flight commands to the UAV. The input is data from the terminal, and the output is the flight commands sent to the UAV.

[0745] Step 3:

[0746] The unmanned aerial vehicle receives commands from a server, flies over farmland, and collects image data using a high-resolution camera. This data is transmitted to the server in real time. The input here is flight commands from the server, and the output is image data sent back to the server.

[0747] Step 4:

[0748] The server stores the received image data in a database and performs preprocessing. This preprocessing includes denoising and correcting the images. Then, a generative AI model is used to analyze the data and identify crop health and abnormalities. The input is image data from the unmanned aerial vehicle, and the output is the analysis results.

[0749] Step 5:

[0750] The server generates optimization information for agricultural activities based on the analysis results. This information includes fertilization schedules and irrigation plans. The generated information is sent to the terminal. The input is the analysis results, and the output is the optimization information.

[0751] Step 6:

[0752] The terminal presents the user with optimized information sent from the server. Simultaneously, it collects facial and vocal expressions using the camera and microphone to analyze the user's response using an emotion engine. The input here is the optimized information from the server and the user's response, and the output is the user's emotional state.

[0753] Step 7:

[0754] The server analyzes the user's emotional information obtained from the emotion engine and customizes the suggestions as needed. If the user is dissatisfied, the server re-evaluates the suggestions, adds new information, and makes improvements. The input is the user's emotional state, and the output is customized suggestion information.

[0755] (Application Example 2)

[0756] 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".

[0757] Conventional farmland management systems have a problem in that their suggestions, based on the analysis of terrain data acquired using aircraft, are solely based on the data itself and do not take into account the subjective satisfaction or emotional state of the users, thus failing to adequately meet the actual needs of the users. Similarly, in factories, robot work plans do not reflect the emotional state of the workers, which can lead to decreased work efficiency and satisfaction.

[0758] 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. In this invention, the server includes means for acquiring image information of the terrain area using an aircraft, means for analyzing the image information to detect the health status or abnormalities of plants, means for generating information that proposes improvements to agricultural activities based on the analyzed data, means for identifying the emotional state of the user using an emotion analysis device, and means for adjusting the proposals for the user based on the emotional state. As a result, by making proposals that take into account the emotional state of the user, efficient and highly satisfying management that meets actual needs becomes possible in both agricultural activities and factory work planning.

[0759] A "flying object" is a mechanical device used to fly over a terrain area and acquire image information.

[0760] "Image information" refers to visual data of the terrain area acquired by sensors and cameras mounted on the aircraft.

[0761] "Health status" refers to biological indicators that show whether plants or crops are growing normally.

[0762] An "abnormality" is a condition or symptom that deviates from the standard growth pattern in an organism.

[0763] An "emotion analysis device" is a combination of hardware and software used to identify a user's emotional state.

[0764] "Suggestions" refer to specific instructions or advice given based on collected data and emotional states.

[0765] A "user" is an individual or organization that uses this system to perform tasks or receive information.

[0766] The system that realizes this invention detects the health and abnormalities of plants based on image information of the terrain area collected by an aircraft, and further provides improvement suggestions that take into account the user's emotional state.

[0767] The server receives high-resolution image information transmitted from the aircraft and stores it in a database. After preprocessing, this image data is analyzed using a generative AI model (e.g., OpenAI GPT-4) to identify the health status and abnormalities of plants. Based on the information extracted through the analysis, specific suggestions aimed at improving agricultural activities are generated. At this stage, user emotion data is collected using an emotion analysis device, and the user's emotional state is identified using facial expression analysis software (e.g., Microsoft Azure Emotion API).

[0768] The terminal displays and presents suggestions supplied from the server to the user. These suggestions are customized according to the user's emotional state; for example, if the user is stressed, the suggestions may include adjustments to reduce their workload. This system allows users to receive optimal suggestions based on their emotional state, improving work efficiency and satisfaction.

[0769] A concrete example is the operational management of robots in factories. By using unmanned aerial vehicles to monitor the status of equipment from above within the factory and adjusting the robots' work schedules and movements according to the stress levels of the staff, it is expected that the overall efficiency of the work environment will improve.

[0770] An example of a prompt message might be, "If the user's emotional state is stressed, how should the suggestions for improving agricultural activities be adjusted?"

[0771] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0772] Step 1:

[0773] The server receives terrain image data from the aircraft. The received data is first converted to an appropriate format and stored in the database. The input is image data acquired from the aircraft, which is used to save data to the database. The output is the accumulation of data in the database.

[0774] Step 2:

[0775] The server preprocesses image data stored in the database and uses a generative AI model to analyze the health and abnormalities of plants. The input here is preprocessed image data, and the generative AI model extracts and analyzes features from the data, resulting in output of plant health information. Specifically, it identifies color and shape patterns within the image to evaluate the plant's health.

[0776] Step 3:

[0777] The server generates suggested information aimed at improving agricultural activities based on the analysis results. The input is the plant health information obtained in step 2, and improvement suggestions are generated based on this. The output is specific improvement plans and measures. By using prompts to consider how the suggestions can be implemented, more specific output can be obtained.

[0778] Step 4:

[0779] The terminal displays suggestions received from the server and provides them to the user. Input consists of improvement suggestions sent from the server; these suggestions are presented on the display device, allowing the user to review them. Output is the presentation of suggestion information to the user. Specifically, the data is visualized in a visually easy-to-understand format, making it easy for the user to receive and process.

[0780] Step 5:

[0781] The device uses a camera and microphone to collect the user's face and voice, and an emotion analysis device identifies the user's emotional state. The input consists of the user's facial image and voice data, and the emotion analysis device analyzes this data to determine the emotional state. The output is information about the user's emotional state. Specifically, facial recognition software detects subtle changes in facial expression and determines the emotion based on that.

[0782] Step 6:

[0783] The server takes the user's emotional state into consideration and adjusts the suggested information accordingly. The input consists of the user's emotional state information and initial suggested information, and the adjusted suggestions are output. Specifically, the emotional information is incorporated into the generating AI model via prompt messages, and new suggestions are generated. An example of such a prompt message would be, "If the user's emotional state is stress, how should the suggestions for improving agricultural activities be adjusted?"

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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."

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] The following is further disclosed regarding the embodiments described above.

[0806] (Claim 1)

[0807] A means of acquiring image data of the ground surface using an aircraft,

[0808] A means for analyzing the aforementioned image data to detect the growth state or abnormalities of the plant,

[0809] A means for generating information that proposes the optimization of agricultural activities based on the analyzed data,

[0810] Means for providing the generated information to the user,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the flying object is an unmanned aerial vehicle.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the optimization information based on the analyzed data includes a fertilization schedule, an irrigation plan, or a disease control measure.

[0816] "Example 1"

[0817] (Claim 1)

[0818] Means of acquiring geographical data using an aircraft,

[0819] A means of using a generative AI model to analyze the aforementioned data,

[0820] A means for generating information that proposes improvements to agricultural work based on the analyzed data,

[0821] A means for notifying the user of the generated information,

[0822] A means of acquiring image data via wireless communication,

[0823] A means of constructing a flight plan using map information,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, wherein the flying object is an unmanned aerial vehicle.

[0827] (Claim 3)

[0828] The system according to claim 1, wherein the improvement information based on the analyzed data includes a fertilization plan, an irrigation schedule, or disease management.

[0829] "Application Example 1"

[0830] (Claim 1)

[0831] A means for acquiring image data of the production facility area using an imaging device,

[0832] Means for analyzing the aforementioned image data to detect production processes or abnormalities,

[0833] A means for generating information that proposes the optimization of production activities based on the analyzed data,

[0834] Means for providing the generated information to the user,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the imaging device is mounted on a mobile body.

[0838] (Claim 3)

[0839] The system according to claim 1, wherein the optimization information based on the analyzed data includes maintenance planning for the device, adjustment of product placement, or anomaly monitoring.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] A means of acquiring image data of the ground surface using an aircraft,

[0843] A means for analyzing the aforementioned image data to detect the growth state or abnormalities of the plant,

[0844] A means for generating information that proposes the optimization of agricultural activities based on the analyzed data,

[0845] Means for providing the generated information to the user,

[0846] A means for analyzing the user's reactions in order to recognize the user's emotional state and for customizing the optimization suggestion information,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, wherein the flying object is an unmanned aerial vehicle.

[0850] (Claim 3)

[0851] The system according to claim 1, wherein the optimization information based on the analyzed data includes a fertilization schedule, an irrigation plan, or a disease control measure.

[0852] "Application example 2 when combining with an emotional engine"

[0853] (Claim 1)

[0854] A means of acquiring image information of the terrain area using an aircraft,

[0855] Means for analyzing the aforementioned image information to detect the health status or abnormality of the plant,

[0856] A means for generating information that proposes improvements to agricultural activities based on the analyzed data,

[0857] Means for supplying the generated information to the user,

[0858] A means for identifying the emotional state of a user using an emotion analysis device,

[0859] A means for adjusting the proposal for the user based on the aforementioned emotional state,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, wherein the flying object is an unmanned aerial vehicle.

[0863] (Claim 3)

[0864] The system according to claim 1, wherein the improvement information based on the analyzed data includes a fertilizer application plan, an irrigation plan, or a disease control plan. [Explanation of Symbols]

[0865] 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 image data of the ground surface using an aircraft, A means for analyzing the aforementioned image data to detect the growth state or abnormalities of the plant, A means for generating information that proposes the optimization of agricultural activities based on the analyzed data, Means for providing the generated information to the user, A system that includes this.

2. The system according to claim 1, wherein the flying object is an unmanned aerial vehicle.

3. The system according to claim 1, wherein the optimization information based on the analyzed data includes a fertilization schedule, an irrigation plan, or a disease control measure.

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