system

Abstract

We provide the system. [Solution] A means including a processing device for receiving agricultural land information and formulating flight routes and work plans based thereon, A means including a drone control device capable of communicating with the processing device for instructing the operation of the drone, Means including an analytical device for analyzing data collected by a drone and evaluating the condition of crops, A means including a display device for notifying agricultural workers of the evaluation results and for revising the next work plan, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the aging of agricultural workers and the shortage of successors progressing, it is particularly urgent to improve the efficiency and labor saving of work on farmland, especially on steep slopes. In the conventional method, the physical burden on the elderly is large, and it is difficult to perform efficient work, which is an issue. Furthermore, there is a lack of an environment in which young people who will be the future bearers can easily enter the agricultural industry. As a result, it is difficult to achieve sustainable agriculture.

Means for Solving the Problems

[0005] This invention provides a system equipped with a processing device that receives farmland information and automatically formulates flight routes and work plans. The system instructs the drone's movements via a drone control device and accurately evaluates the health of crops by analyzing the data collected by the drone with an analysis device. Furthermore, by including a display device that notifies farmers of the analysis results and allows for easy adjustment of the work plan, it is possible to improve the efficiency of agricultural work, reduce the burden on the elderly, and promote young people's entry into agriculture.

[0006] "Agricultural land information" refers to data on the topography, weather, and crops of agricultural land.

[0007] A "processing device" is a computer system that receives agricultural land information and formulates flight routes and work plans.

[0008] A "drone control device" is a device that transmits instructions from a processing unit to a drone and controls its operation.

[0009] An "analysis device" is a device that analyzes data collected by drones and has the function of evaluating the condition of crops.

[0010] A "display device" is a device equipped with a user interface that notifies agricultural workers of the analysis results and makes it easier for them to revise their work plans.

[0011] "System" refers to the entire integrated device that performs a series of functions, combining a processing unit, a drone control unit, an analysis unit, and a display unit. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]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] It 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. <了

Mode for Carrying Out the Invention

[0013] [[ID=4上0]] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] <000上089>First, the language used in the following description will be explained.

[0015] In the following embodiments, the labeled 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.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is an AI-equipped drone system that assists farmers in performing agricultural work efficiently. Based on farmland information, it formulates flight routes and work plans, and the drone autonomously carries out the work, thereby improving the efficiency of agricultural work.

[0034] The server automatically calculates the optimal flight route and work plan based on farmland information (terrain, crops, and weather data) received from the user. This plan includes pesticide application points, crop monitoring areas, and priority of areas requiring harvesting.

[0035] The drone control system receives instructions from a server and controls the drone in real time to ensure safe and efficient flight. This includes features such as altitude adjustment based on terrain and obstacle avoidance.

[0036] The drone, acting as the terminal, flies over a designated work area, using cameras and sensors to monitor the condition of the crops. The collected information is transmitted to a server as data indicating the health and growth of the crops.

[0037] The server processes the received data using an analysis device and utilizes an AI model to detect crop health and abnormalities. The analysis results are then communicated to the user and provided as suggestions for improving farm work and scheduling the next tasks.

[0038] Users can easily modify their next work plan based on the information provided. For example, an elderly farmer can use their smartphone to specify a map of their entire farmland and request an AI drone to spray pesticides. The drone will then automatically fly along the planned route and perform optimal spraying. This process reduces the physical burden on the elderly and improves agricultural efficiency.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users input topographic information, crop types, and weather data for their farmland using a dedicated app, and then send this information to the server.

[0042] Step 2:

[0043] The server uses received farmland information and an internal database to formulate flight routes and work plans. This includes determining optimal pesticide application points and monitoring areas.

[0044] Step 3:

[0045] The server transmits the formulated work plan and flight route to the drone control unit. The drone control unit then prepares the drone for flight based on the received plan.

[0046] Step 4:

[0047] The drone, acting as the terminal, begins flying along a designated route. During flight, the drone sprays pesticides and monitors the condition of the crops using cameras and sensors.

[0048] Step 5:

[0049] The drone transmits data collected during flight to a server in real time. This data includes image data and environmental data.

[0050] Step 6:

[0051] The server analyzes the received data and uses an AI model to evaluate the health of the crops. If an anomaly is detected, it generates that information as feedback.

[0052] Step 7:

[0053] Based on feedback received from the server, users adjust their next work plan within the app. This information can be used to perform agricultural tasks more efficiently.

[0054] (Example 1)

[0055] 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."

[0056] Traditional farming methods are labor-intensive and time-consuming, and make it difficult to properly monitor crop health and environmental conditions. Furthermore, inexperience and an aging workforce are increasingly making it difficult to develop efficient work plans. To address these challenges, a system is needed that enables farmers to work efficiently and effectively, thereby achieving sustainable agriculture.

[0057] 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.

[0058] In this invention, the server includes means for receiving farmland information and formulating an optimal flight path and work plan based on it using a generating AI model; means for directing the drone's movements and controlling safe and efficient flight in real time, and means for analyzing environmental data collected by the drone and analyzing the health of crops using a generating AI model. This enables farmers to optimize farm work and manage crop health with minimal effort.

[0059] "Agricultural land information" is a general term for information related to agricultural land, such as topography, crop types, and weather data.

[0060] A "flight path" is the optimal flight route set up to allow a drone to move safely and efficiently over designated farmland.

[0061] A "work plan" outlines the specific procedures and strategies for farm work, including pesticide application points and crop monitoring areas.

[0062] A "generative AI model" is an artificial intelligence model used to analyze large amounts of data and make decisions based on the results.

[0063] An "information processing device" is a device or system that performs calculations and analyses based on received data and outputs the results.

[0064] A "drone controller" is a device that manages the operation of a drone in real time, supporting safe and efficient flight.

[0065] "Environmental data" refers to data about the surrounding environment, such as the condition of crops, soil conditions, and weather conditions, collected by drones.

[0066] An "analyzer" is a device that analyzes collected data and uses the results to evaluate the health of crops.

[0067] One embodiment of this invention involves using an AI-equipped drone system with the aim of enabling agricultural workers to perform tasks efficiently and effectively.

[0068] The server receives farmland information entered by the user and uses an AI model to generate the optimal flight path and work plan based on that information. This plan includes pesticide application points and crop monitoring areas, and the AI ​​model can use TENSORFLOW® or similar machine learning libraries to analyze large amounts of data. Users input this information from devices such as smartphones and tablets. For example, a user might specify the topography of their farmland and the type of crops they are growing, and make a request such as, "Please spray pesticides on the west side of the tomato field."

[0069] The plan formulated by the server is transmitted to the drone controller, which manages the drone's movements in real time. This controller automatically adjusts the flight altitude based on terrain information, ensuring safe and efficient flight.

[0070] The drone, acting as the terminal, flies according to a pre-determined plan, collecting environmental data using its built-in camera and sensors. This data, which indicates the health and growth status of crops, is transmitted from the drone to a server.

[0071] The server receives the transmitted environmental data and uses AI to analyze the health of the crops. The analysis results are notified to the user and provided as suggestions for improving the next work schedule. For example, information such as "We detected an abnormality in the tomato leaves, so we need to spray additional pesticides on the east side" is sent.

[0072] An example of a prompt message could be input to the generating AI model, such as, "Based on the terrain and weather data of the farmland specified by the user, please create the optimal flight route and work plan. The results should include pesticide spraying points and crop monitoring areas."

[0073] Thus, the present invention contributes to the optimization of agricultural production by significantly improving the efficiency of farmland management.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] Users input farmland information using smartphones or tablets. This information includes topography, crop types, and weather data. The entered information is sent to the server. In this step, the specific action is to digitize the data collected by the user and send it to the server.

[0077] Step 2:

[0078] The server uses a generated AI model based on the received farmland information to formulate the optimal flight path and work plan. In this process, the AI ​​model analyzes the input data to determine the areas where pesticide spraying is necessary and the monitoring areas. The output is a detailed work plan. Specifically, this step involves the AI ​​calculating and analyzing the input data and automatically creating the plan.

[0079] Step 3:

[0080] The server transmits the created work plan to the drone controller. The drone controller receives this and prepares the drone for safe and efficient flight. The input for this step is the work plan, and the output is flight instruction information. Specifically, the controller prepares to use terrain information to adjust altitude.

[0081] Step 4:

[0082] The drone, acting as the terminal, flies along a designated route based on instructions from the drone controller. During flight, the drone collects environmental data using its built-in sensors and camera. The input is instructions from the controller, and the output is the collected environmental data. Specifically, its operation involves flying while monitoring the condition of crops using its sensors.

[0083] Step 5:

[0084] The server receives environmental data transmitted from the drone and uses an AI model to analyze the health of the crops. The input is environmental data, and the output is the analysis results. Specifically, the AI ​​performs analysis to identify anomalies and problem areas.

[0085] Step 6:

[0086] The server notifies the user of the analysis results. The user can then use this information to revise their next work plan. The input is the analysis results, and the output is the notified information. The specific operation involves providing information through a user interface.

[0087] (Application Example 1)

[0088] 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."

[0089] Inventory management and stocktaking in logistics centers are time-consuming and labor-intensive, making efficient operation essential. However, conventional methods have problems with real-time inventory tracking and efficient item placement. This raises concerns about unnecessary costs and decreased accuracy in inventory management. This invention aims to improve the efficiency and accuracy of inventory management in logistics centers.

[0090] 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.

[0091] In this invention, the server includes means including a processing device for receiving area information and formulating flight paths and work plans based thereon; means including an unmanned aircraft control device that can communicate with the processing device for instructing the operation of the unmanned aircraft; means including an analysis device for analyzing data collected by the unmanned aircraft and evaluating the condition of goods; and means including an augmented reality display device as a support device for an operator wearing a visual assistance device to monitor the operation of the unmanned aircraft. This enables real-time inventory status monitoring and efficient logistics operations.

[0092] "Area information" refers to information about the physical space required when an unmanned aircraft flies and performs its duties.

[0093] A "flight path" is the route that an unmanned aircraft takes to reach its destination.

[0094] A "work plan" is a schedule that includes the specific tasks and procedures that the unmanned aerial vehicle (UAV) should perform.

[0095] A "processing device" is a device that has information processing functions for formulating flight paths and operational plans based on received information.

[0096] An "unmanned aircraft control system" is a device that has control functions to manage and direct the operation of an unmanned aircraft.

[0097] An "analysis device" is an information processing device that analyzes data collected by an unmanned aerial vehicle and evaluates the information based on the results.

[0098] A "visual assistance device" is a device used by workers to visually confirm the working status of an unmanned aircraft, and it is equipped with an augmented reality display function.

[0099] An "augmented reality display device" is a display device that has the function of overlaying digital information onto real-world visual information.

[0100] The system for implementing this invention is used when an unmanned aerial vehicle (UAV) autonomously performs inventory management tasks within a logistics center. The server formulates an optimal flight path and work plan based on area information received from the center. This plan is transmitted to the UAV control unit, and the UAV operates according to the plan. The UAV is equipped with cameras and sensors, which are used to scan QR codes (registered trademark) on shelves and collect inventory data.

[0101] The server processes the collected data in real time using an analysis device to evaluate the inventory status. This evaluation result is displayed on a visual assistance device worn by the user, i.e., an augmented reality display device, enabling workers to immediately issue necessary instructions at the logistics center.

[0102] As a concrete example, when new products arrive at a logistics center, the server receives a prompt saying "Start checking inventory for new products." This prompts an automated system (DVR) to move to a designated area, enabling highly efficient inventory checks. This system allows for quick and accurate inventory management at the logistics center, significantly improving operational efficiency.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server receives area information from the logistics center and uses this information as input data. The server uses an algorithm to formulate the optimal flight path and operational plan. It generates plan data that is sent to the unmanned aerial vehicle control system as output.

[0106] Step 2:

[0107] The unmanned aircraft control system generates operational instructions for the unmanned aircraft based on planning data received from the server. It receives planning data as input and generates commands as output to ensure the unmanned aircraft moves safely along its flight path.

[0108] Step 3:

[0109] The drone flies around the logistics center following commands from its control unit. Equipped with cameras and sensors, the drone scans QR codes on inventory items as input, collecting inventory data. The collected data is transmitted to a server in real time.

[0110] Step 4:

[0111] The server processes inventory data sent from the drone using a generative AI model in the analysis device. Based on the input data, it evaluates the inventory status and sends the evaluation result as output to the visual assistance device.

[0112] Step 5:

[0113] The user wears a visual aid and receives inventory valuation results as output. This allows them to monitor the progress of work within the logistics center and, if necessary, send instructions to the server using prompt messages.

[0114] 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.

[0115] This invention combines an AI-powered drone system that supports agricultural work with an emotion engine to provide an efficient and less stressful environment for farmers. It features a system that formulates flight routes and work plans based on farmland information, allowing the drone to perform tasks automatically while simultaneously recognizing the user's emotions and providing corresponding feedback.

[0116] The server receives topographic information, crop data, and weather forecasts from the user and uses this information to generate the optimal flight route and work plan. This plan includes pesticide spraying, crop monitoring, and harvesting assistance. Once the plan is formulated, the server sends the necessary instructions to the drone control unit, and the drone autonomously carries out the work accordingly.

[0117] The drone, acting as the terminal, flies over farmland and performs various tasks based on instructions from the server, collecting crop data using its onboard sensors and cameras. The collected data is transmitted to the server in real time and used for analysis to assess the health of the crops.

[0118] Furthermore, the emotion engine analyzes user input such as voice and facial expressions to determine emotional states such as stress and anxiety. Based on this information, the system's user interface provides emotion-appropriate feedback notifications. For example, if fatigue is particularly evident, the notification frequency is reduced, and simple, reassuring messages are provided.

[0119] As a concrete example, consider a scenario where a farmer operates a smartphone while performing daily farm work. The user inputs farmland information and activates a drone, which flies along a planned route and monitors the health of the crops. When feedback is provided to the user regarding the progress of the work and the condition of the crops, the emotion engine selects feedback that is tailored to the user's state, allowing the user to continue working with peace of mind. In this way, utilizing this system reduces the burden on farmers, including the elderly, and improves work efficiency.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The user launches a dedicated application and inputs information about the farmland's topography, crop types, and current weather data. This information is then sent to the server.

[0123] Step 2:

[0124] The server uses AI algorithms based on the received farmland information to formulate the optimal flight route and work plan. This plan includes pesticide application areas, crop monitoring areas, and harvesting areas.

[0125] Step 3:

[0126] The server transmits the formulated plan to the drone control unit and issues instructions to launch the drone. The control unit prepares to automatically adjust the drone's flight altitude while taking terrain information into consideration.

[0127] Step 4:

[0128] The drone, acting as the terminal, flies over farmland following its assigned flight route, collecting image data and sensor data of crops. The drone also automatically sprays pesticides according to the designated route.

[0129] Step 5:

[0130] The data collected by the drone is transmitted to a server in real time, where it is analyzed using an AI model in an analysis device. This allows for the evaluation of crop health and signs of disease.

[0131] Step 6:

[0132] The analysis results are fed back to the user from the server, and at the same time, the emotion engine analyzes the user's voice and facial expressions to determine their emotional state. Based on this data, the content and method of feedback are adjusted.

[0133] Step 7:

[0134] Users receive feedback notifications on their smartphones, adjusted by an emotion engine, to adjust their next work plan. Because the notifications are clear and emotionally sensitive, users can reduce stress and work efficiently on their farm tasks.

[0135] (Example 2)

[0136] 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".

[0137] Traditional agricultural practices have presented challenges in that efficient crop management requires considerable effort and time. Furthermore, the provision of a work environment that considers the emotional state of farmers has been insufficient, highlighting the need for stress reduction. Additionally, real-time monitoring of crop conditions and prompt responses have been difficult.

[0138] 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.

[0139] In this invention, the server includes means including a processing device for receiving farmland information and formulating flight routes and work plans based thereon; means including a control device that can communicate with the processing device for instructing the operation of the drone; means including an analysis device for analyzing data collected by the drone and evaluating the condition of plants; means including an emotion engine for analyzing the user's voice and facial expressions and determining their emotional state; and means including an information providing device for providing feedback according to the emotional state. This makes it possible to reduce the burden on agricultural workers and provide an efficient and less stressful working environment.

[0140] "Agricultural land information" refers to data concerning the topography of agricultural land, the types of crops, their growth status, and the surrounding weather conditions.

[0141] A "flight route" is a planned path for a drone to travel over farmland.

[0142] A "work plan" is a plan that includes detailed procedures and schedules for agricultural work, tailored to the specific purpose.

[0143] A "processing device" is an electronic device used to receive and analyze information, and it plays a role in formulating plans.

[0144] A "control device" is a device that directs and manages the drone's movements based on the flight route and work plan.

[0145] An "analysis device" is a device used to analyze data collected by drones and evaluate the condition of crops.

[0146] "Plant condition" refers to information indicating the health and growth progress of a crop.

[0147] An "emotion engine" is a system that recognizes and analyzes a user's emotional state based on their voice and facial expressions.

[0148] An "information provision device" is a device that transmits analysis results and emotion-based feedback to the user.

[0149] This invention is an AI-powered drone system aimed at improving agricultural efficiency and reducing stress. The system is designed to enable farmers to perform efficient tasks without increasing their workload through the coordinated interaction of the server, terminal, and user components.

[0150] The server processes various sensor data to generate flight routes and work plans. Specifically, it receives topographic information of farmland, crop data, and weather forecasts to determine the optimal work procedure. This uses cloud-based data analysis software and technology that updates the plan based on real-time changing environmental information.

[0151] The drone, acting as the terminal, receives instructions from the server and autonomously performs tasks along a designated route. The drone is equipped with high-precision cameras and sensors, which are used to monitor the health of the crops. The collected data is transmitted to the server in real time and used to continuously assess the crop's condition.

[0152] Users can interface with the system through a terminal or other information terminal and receive feedback tailored to their work status and emotional state. An emotion engine analyzes the user's voice and facial expressions to determine their stress and fatigue levels. Based on this, psychologically sensitive information is provided to the user.

[0153] As a concrete example, a farmer uses their smartphone to activate a drone and input farmland information. The user then prompts the AI ​​model with the message, "Please suggest an efficient flight route based on the farmland information." The AI ​​then quickly calculates the optimal route and suggests it to the user. This system provides a less burdensome farming environment, particularly for the elderly and newcomers.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The server receives topographic information of farmland, crop data, and weather forecasts as input from the user. This data is analyzed by a processing unit to generate information necessary for formulating flight routes and work plans. In this process, cloud-based data analysis software is used to calculate optimized work procedures based on the input data. The output is expressed as the optimal flight route and work plan.

[0157] Step 2:

[0158] The server transmits the generated flight route and work plan to the control unit. The drone receives this information as input and begins its work along the specified route. The drone can fly precisely along the designated path using high-precision GPS technology and built-in sensors. The output is the start of the drone's flight along the planned route.

[0159] Step 3:

[0160] The drone, acting as the terminal, collects crop data in real time during flight using its built-in camera and sensors. The collected data is transmitted from the terminal to a server. The input is raw data observed by various sensors, and the output is formatted crop condition data for evaluation.

[0161] Step 4:

[0162] The server analyzes crop data transmitted from drones using an analysis device to evaluate the health and growth progress of the plants. Machine learning algorithms are used for the analysis to detect anomalies and predict growth. The input is data from sensors, and the output is feedback on the health status as an evaluation result.

[0163] Step 5:

[0164] The user receives feedback on the condition of the evaluated crops through the device. The user interface uses a generative AI model and an emotion engine to analyze the user's voice and facial expressions and provide appropriate feedback messages. The input is the analysis results and the user's emotion data, and the output is a customized feedback message.

[0165] Step 6:

[0166] The user uses this feedback to provide further instructions to the system, generating new prompts. For example, by entering "Recommend the timing of the next pesticide application" as a prompt, a new work plan or adjustments will be made. The output is the proposed work adjustments.

[0167] (Application Example 2)

[0168] 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".

[0169] In factory manufacturing processes, it is necessary to efficiently maintain and improve product quality while reducing stress and burden on workers. This requires a system that manages the entire manufacturing process while simultaneously incorporating flexible feedback that takes into account the workers' conditions.

[0170] 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 including an information processing device for receiving manufacturing information in a factory and formulating work routes and process plans based thereon; means including a control device that can communicate with the information processing device for instructing the operation of manufacturing equipment; and means including an emotion recognition device for recognizing the emotional state of workers and providing feedback corresponding to those emotions. This makes it possible to provide appropriate support to workers while maintaining product quality.

[0171] "Manufacturing information" refers to the materials, processes, schedules, and all related data used in the factory.

[0172] An "information processing device" refers to a computer system used to analyze manufacturing data and formulate optimal work routes and process plans.

[0173] "Control device" refers to a device that communicates with the aforementioned information processing device and issues operational instructions to the manufacturing device.

[0174] An "analysis device" refers to a device that evaluates the condition of a product based on collected data and makes decisions for maintaining quality.

[0175] A "display device" refers to a visual or audio output device used to communicate evaluation results to workers and to present revised plans for the next process.

[0176] An "emotion recognition device" refers to a system that analyzes a worker's voice, facial expressions, or other physiological data to identify their emotional state.

[0177] This invention is a system aimed at improving efficiency in manufacturing operations and providing mental support to workers. The system begins with a factory server receiving "manufacturing information" from each production line and using an "information processing device" to formulate optimal work routes and process plans. Based on this information, a "control device" issues precise operational instructions to each manufacturing device.

[0178] The "analysis device" uses data collected from the manufacturing equipment to evaluate the condition of the product and analyze possible defects and quality issues. The results are provided to the operator via the "display device," which helps in revising the plan for the next process.

[0179] Furthermore, the worker's "emotion recognition device" analyzes the worker's emotional state and provides feedback tailored to that emotion. For example, if the device determines that the workload is heavy, it can suggest relaxation techniques or adjust parts of the task.

[0180] This system is designed to reduce stress from long hours of work while maintaining worker productivity. For example, when a worker starts operating a new machine in a factory, an emotion recognition device could detect their anxiety and display specific instructions on what to do next, as well as reassuring feedback.

[0181] A concrete example is a situation where workers can check the current process progress and product status through smart glasses. If a worker feels tired, a message such as "It might be a good idea to take a short break" will appear on the screen, ultimately reducing the burden on the worker.

[0182] Examples of prompts for a generative AI model:

[0183] "How can I design a factory management app that monitors workers' emotional states in real time and provides appropriate feedback based on their location and the progress of the production line?"

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The server receives manufacturing information from within the factory. This input includes material information, manufacturing processes, and planned schedules. The server sends this data to an information processing unit, which then executes algorithms to develop optimal work routes and process plans. This process outputs a specific process plan designed to maximize the efficiency of the production line.

[0187] Step 2:

[0188] The terminal receives the process plan from the information processing unit, and the control unit transmits specific operational instructions to the manufacturing equipment. This input includes information on the operation details and timing for each process. Based on this, the control unit starts the manufacturing equipment and executes the planned process. As a result, each manufacturing line begins to operate efficiently.

[0189] Step 3:

[0190] The manufacturing equipment transmits data collected during operation to the server. This input includes information about product quality and any abnormal conditions. The server's analysis equipment receives this data and performs analysis for quality control. The output obtained at this stage is the result of the quality evaluation.

[0191] Step 4:

[0192] The user receives the analysis results via a display device. The evaluation results are fed back to be incorporated into the next process plan. This input includes quality evaluation results and recommended corrections. The user reviews them and instructs on the necessary adjustments for subsequent processes.

[0193] Step 5:

[0194] The terminal uses an emotion recognition device to detect the worker's voice and facial expressions, and sends this data to a server. Based on this data, the server performs emotion analysis and determines how to provide appropriate feedback. This output includes a specific message tailored to the worker's emotions.

[0195] Step 6:

[0196] Users receive feedback in real time through smart glasses. Messages output by the server are displayed to the user, and workers adjust their actions based on this feedback, improving work efficiency and mental health.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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".

[0213] This invention is an AI-equipped drone system that assists farmers in performing agricultural work efficiently. Based on farmland information, it formulates flight routes and work plans, and the drone autonomously carries out the work, thereby improving the efficiency of agricultural work.

[0214] The server automatically calculates the optimal flight route and work plan based on farmland information (terrain, crops, and weather data) received from the user. This plan includes pesticide application points, crop monitoring areas, and priority of areas requiring harvesting.

[0215] The drone control system receives instructions from a server and controls the drone in real time to ensure safe and efficient flight. This includes features such as altitude adjustment based on terrain and obstacle avoidance.

[0216] The drone, acting as the terminal, flies over a designated work area, using cameras and sensors to monitor the condition of the crops. The collected information is transmitted to a server as data indicating the health and growth of the crops.

[0217] The server processes the received data using an analysis device and utilizes an AI model to detect crop health and abnormalities. The analysis results are then communicated to the user and provided as suggestions for improving farm work and scheduling the next tasks.

[0218] Users can easily modify their next work plan based on the information provided. For example, an elderly farmer can use their smartphone to specify a map of their entire farmland and request an AI drone to spray pesticides. The drone will then automatically fly along the planned route and perform optimal spraying. This process reduces the physical burden on the elderly and improves agricultural efficiency.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] Users input topographic information, crop types, and weather data for their farmland using a dedicated app, and then send this information to the server.

[0222] Step 2:

[0223] The server uses received farmland information and an internal database to formulate flight routes and work plans. This includes determining optimal pesticide application points and monitoring areas.

[0224] Step 3:

[0225] The server transmits the formulated work plan and flight route to the drone control unit. The drone control unit then prepares the drone for flight based on the received plan.

[0226] Step 4:

[0227] The drone, acting as the terminal, begins flying along a designated route. During flight, the drone sprays pesticides and monitors the condition of the crops using cameras and sensors.

[0228] Step 5:

[0229] The drone transmits data collected during flight to a server in real time. This data includes image data and environmental data.

[0230] Step 6:

[0231] The server analyzes the received data and uses an AI model to evaluate the health of the crops. If an anomaly is detected, it generates that information as feedback.

[0232] Step 7:

[0233] Based on feedback received from the server, users adjust their next work plan within the app. This information can be used to perform agricultural tasks more efficiently.

[0234] (Example 1)

[0235] 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".

[0236] Traditional farming methods are labor-intensive and time-consuming, and make it difficult to properly monitor crop health and environmental conditions. Furthermore, inexperience and an aging workforce are increasingly making it difficult to develop efficient work plans. To address these challenges, a system is needed that enables farmers to work efficiently and effectively, thereby achieving sustainable agriculture.

[0237] 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.

[0238] In this invention, the server includes means for receiving farmland information and formulating an optimal flight path and work plan based on it using a generating AI model; means for directing the drone's movements and controlling safe and efficient flight in real time, and means for analyzing environmental data collected by the drone and analyzing the health of crops using a generating AI model. This enables farmers to optimize farm work and manage crop health with minimal effort.

[0239] "Agricultural land information" is a general term for information related to agricultural land, such as topography, crop types, and weather data.

[0240] A "flight path" is the optimal flight route set up to allow a drone to move safely and efficiently over designated farmland.

[0241] A "work plan" outlines the specific procedures and strategies for farm work, including pesticide application points and crop monitoring areas.

[0242] A "generative AI model" is an artificial intelligence model used to analyze large amounts of data and make decisions based on the results.

[0243] An "information processing device" is a device or system that performs calculations and analyses based on received data and outputs the results.

[0244] A "drone controller" is a device that manages the operation of a drone in real time, supporting safe and efficient flight.

[0245] "Environmental data" refers to data about the surrounding environment, such as the condition of crops, soil conditions, and weather conditions, collected by drones.

[0246] An "analyzer" is a device that analyzes collected data and uses the results to evaluate the health of crops.

[0247] One embodiment of this invention involves using an AI-equipped drone system with the aim of enabling agricultural workers to perform tasks efficiently and effectively.

[0248] The server receives farmland information entered by the user and uses an AI model to generate the optimal flight path and work plan based on that information. This plan includes pesticide application points and crop monitoring areas, and the AI ​​model can use TensorFlow or similar machine learning libraries to analyze large amounts of data. Users input this information from devices such as smartphones and tablets. For example, a user might specify the terrain and crop types of their farmland and make a request such as, "Please spray pesticides on the west side of the tomato field."

[0249] The plan formulated by the server is transmitted to the drone controller, which manages the drone's movements in real time. This controller automatically adjusts the flight altitude based on terrain information, ensuring safe and efficient flight.

[0250] The drone, acting as the terminal, flies according to a pre-determined plan, collecting environmental data using its built-in camera and sensors. This data, which indicates the health and growth status of crops, is transmitted from the drone to a server.

[0251] The server receives the transmitted environmental data and uses AI to analyze the health of the crops. The analysis results are notified to the user and provided as suggestions for improving the next work schedule. For example, information such as "We detected an abnormality in the tomato leaves, so we need to spray additional pesticides on the east side" is sent.

[0252] An example of a prompt message could be input to the generating AI model, such as, "Based on the terrain and weather data of the farmland specified by the user, please create the optimal flight route and work plan. The results should include pesticide spraying points and crop monitoring areas."

[0253] Thus, the present invention contributes to the optimization of agricultural production by significantly improving the efficiency of farmland management.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] Users input farmland information using smartphones or tablets. This information includes topography, crop types, and weather data. The entered information is sent to the server. In this step, the specific action is to digitize the data collected by the user and send it to the server.

[0257] Step 2:

[0258] The server uses a generated AI model based on the received farmland information to formulate the optimal flight path and work plan. In this process, the AI ​​model analyzes the input data to determine the areas where pesticide spraying is necessary and the monitoring areas. The output is a detailed work plan. Specifically, this step involves the AI ​​calculating and analyzing the input data and automatically creating the plan.

[0259] Step 3:

[0260] The server transmits the created work plan to the drone controller. The drone controller receives this and prepares the drone for safe and efficient flight. The input for this step is the work plan, and the output is flight instruction information. Specifically, the controller prepares to use terrain information to adjust altitude.

[0261] Step 4:

[0262] The drone, acting as the terminal, flies along a designated route based on instructions from the drone controller. During flight, the drone collects environmental data using its built-in sensors and camera. The input is instructions from the controller, and the output is the collected environmental data. Specifically, its operation involves flying while monitoring the condition of crops using its sensors.

[0263] Step 5:

[0264] The server receives environmental data transmitted from the drone and uses an AI model to analyze the health of the crops. The input is environmental data, and the output is the analysis results. Specifically, the AI ​​performs analysis to identify anomalies and problem areas.

[0265] Step 6:

[0266] The server notifies the user of the analysis results. The user can then use this information to revise their next work plan. The input is the analysis results, and the output is the notified information. The specific operation involves providing information through a user interface.

[0267] (Application Example 1)

[0268] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] Inventory management and stocktaking in logistics centers are time-consuming and labor-intensive, making efficient operation essential. However, conventional methods have problems with real-time inventory tracking and efficient item placement. This raises concerns about unnecessary costs and decreased accuracy in inventory management. This invention aims to improve the efficiency and accuracy of inventory management in logistics centers.

[0270] 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.

[0271] In this invention, the server includes means including a processing device for receiving area information and formulating flight paths and work plans based thereon; means including an unmanned aircraft control device that can communicate with the processing device for instructing the operation of the unmanned aircraft; means including an analysis device for analyzing data collected by the unmanned aircraft and evaluating the condition of goods; and means including an augmented reality display device as a support device for an operator wearing a visual assistance device to monitor the operation of the unmanned aircraft. This enables real-time inventory status monitoring and efficient logistics operations.

[0272] "Area information" refers to information about the physical space required when an unmanned aircraft flies and performs its duties.

[0273] A "flight path" is the route that an unmanned aircraft takes to reach its destination.

[0274] A "work plan" is a schedule that includes the specific tasks and procedures that the unmanned aerial vehicle (UAV) should perform.

[0275] A "processing device" is a device that has information processing functions for formulating flight paths and operational plans based on received information.

[0276] An "unmanned aircraft control system" is a device that has control functions to manage and direct the operation of an unmanned aircraft.

[0277] The "analysis device" is an information processing device that analyzes the data collected by the drone and evaluates information based on the results.

[0278] The "visual assistance device" is a device used by an operator to visually confirm the working status of the drone and has an augmented reality display function.

[0279] The "augmented reality display device" is a display device that has a function of superimposing digital information on actual visual information for display.

[0280] The system for implementing this invention is utilized when the drone autonomously conducts inventory management operations within the logistics center. The server formulates an optimal flight route and operation plan based on the area information received from the center. This plan is transmitted to the drone control device, and the drone operates according to the plan. The drone is equipped with a camera and sensors, and these are used to scan the QR codes on the shelves to collect inventory data.

[0281] The server processes the collected data in real time using the analysis device and evaluates the inventory status. The result of this evaluation is displayed by the visual assistance device worn by the user, that is, the augmented reality display device, enabling the operator to immediately give the necessary instructions in the logistics center.

[0282] As a specific example, when new goods are received at the logistics center, the server receives a prompt of "Start inventory confirmation for new goods". Thereby, the drone can move to the designated area and efficiently confirm the inventory status. With this system, the inventory management of the logistics center is carried out quickly and accurately, and the business efficiency is greatly improved.

[0283] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0284] Step 1:

[0285] The server receives area information from the logistics center and uses this information as input data. The server uses an algorithm to formulate an optimal flight route and work plan, and generates plan data to be transmitted to the drone control device as output.

[0286] Step 2:

[0287] Based on the plan data received from the server, the drone control device generates operation instructions for the drone. It receives the plan data as input and generates, as output, commands for the drone to move safely along the flight route.

[0288] Step 3:

[0289] The drone flies within the logistics center according to the commands from the control device. The camera and sensors mounted on the drone scan the QR codes of the inventory as input and collect inventory data. The collected data is transmitted to the server in real time.

[0290] Step 4:

[0291] The server processes the inventory data sent from the drone using the generation AI model in the analysis device. Based on the input data, it evaluates the inventory situation and transmits the evaluation result as output to the visual assistance device.

[0292] Step 5:

[0293] The user wears the visual assistance device and receives the inventory evaluation result as output. Thereby, the user checks the progress of the work in the logistics center and, if necessary, sends the next instruction to the server using prompt text.

[0294] 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 identification model 59 and perform specific processing using the user's emotion.

[0295] This invention combines an AI-powered drone system that supports agricultural work with an emotion engine to provide an efficient and less stressful environment for farmers. It features a system that formulates flight routes and work plans based on farmland information, allowing the drone to perform tasks automatically while simultaneously recognizing the user's emotions and providing corresponding feedback.

[0296] The server receives topographic information, crop data, and weather forecasts from the user and uses this information to generate the optimal flight route and work plan. This plan includes pesticide spraying, crop monitoring, and harvesting assistance. Once the plan is formulated, the server sends the necessary instructions to the drone control unit, and the drone autonomously carries out the work accordingly.

[0297] The drone, acting as the terminal, flies over farmland and performs various tasks based on instructions from the server, collecting crop data using its onboard sensors and cameras. The collected data is transmitted to the server in real time and used for analysis to assess the health of the crops.

[0298] Furthermore, the emotion engine analyzes user input such as voice and facial expressions to determine emotional states such as stress and anxiety. Based on this information, the system's user interface provides emotion-appropriate feedback notifications. For example, if fatigue is particularly evident, the notification frequency is reduced, and simple, reassuring messages are provided.

[0299] As a concrete example, consider a scenario where a farmer operates a smartphone while performing daily farm work. The user inputs farmland information and activates a drone, which flies along a planned route and monitors the health of the crops. When feedback is provided to the user regarding the progress of the work and the condition of the crops, the emotion engine selects feedback that is tailored to the user's state, allowing the user to continue working with peace of mind. In this way, utilizing this system reduces the burden on farmers, including the elderly, and improves work efficiency.

[0300] The process flow will be described below.

[0301] Step 1:

[0302] The user launches the dedicated application and inputs the topographical information of the farmland, the type of crops, and the current weather data. These information are sent to the server.

[0303] Step 2:

[0304] Based on the received farmland information, the server formulates an optimal flight route and work plan by utilizing AI algorithms. This plan includes the pesticide spraying locations, crop monitoring areas, and harvesting target areas.

[0305] Step 3:

[0306] The server sends the formulated plan to the drone control device and issues an instruction to start the drone. The control device prepares to automatically adjust the flight altitude of the drone while considering the topographical information.

[0307] Step 4:

[0308] The drone, which is the terminal, collects image data and sensor data of the crops while flying over the farmland according to the assigned flight route. The drone automatically sprays pesticides according to the specified route.

[0309] Step 5:

[0310] The data collected by the drone is sent to the server in real time, and analysis using an AI model is performed in the analysis device. Thereby, the health condition of the crops and signs of diseases are evaluated.

[0311] Step 6:

[0312] The analysis results are fed back to the user from the server, and at the same time, the emotion engine analyzes the user's voice and facial expressions to determine their emotional state. Based on this data, the content and method of feedback are adjusted.

[0313] Step 7:

[0314] Users receive feedback notifications on their smartphones, adjusted by an emotion engine, to adjust their next work plan. Because the notifications are clear and emotionally sensitive, users can reduce stress and work efficiently on their farm tasks.

[0315] (Example 2)

[0316] 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".

[0317] Traditional agricultural practices have presented challenges in that efficient crop management requires considerable effort and time. Furthermore, the provision of a work environment that considers the emotional state of farmers has been insufficient, highlighting the need for stress reduction. Additionally, real-time monitoring of crop conditions and prompt responses have been difficult.

[0318] 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.

[0319] In this invention, the server includes means including a processing device for receiving farmland information and formulating flight routes and work plans based thereon; means including a control device that can communicate with the processing device for instructing the operation of the drone; means including an analysis device for analyzing data collected by the drone and evaluating the condition of plants; means including an emotion engine for analyzing the user's voice and facial expressions and determining their emotional state; and means including an information providing device for providing feedback according to the emotional state. This makes it possible to reduce the burden on agricultural workers and provide an efficient and less stressful working environment.

[0320] "Agricultural land information" refers to data concerning the topography of agricultural land, the types of crops, their growth status, and the surrounding weather conditions.

[0321] A "flight route" is a planned path for a drone to travel over farmland.

[0322] A "work plan" is a plan that includes detailed procedures and schedules for agricultural work, tailored to the specific purpose.

[0323] A "processing device" is an electronic device used to receive and analyze information, and it plays a role in formulating plans.

[0324] A "control device" is a device that directs and manages the drone's movements based on the flight route and work plan.

[0325] An "analysis device" is a device used to analyze data collected by drones and evaluate the condition of crops.

[0326] "Plant condition" refers to information indicating the health and growth progress of a crop.

[0327] An "emotion engine" is a system that recognizes and analyzes a user's emotional state based on their voice and facial expressions.

[0328] An "information provision device" is a device that transmits analysis results and emotion-based feedback to the user.

[0329] This invention is an AI-powered drone system aimed at improving agricultural efficiency and reducing stress. The system is designed to enable farmers to perform efficient tasks without increasing their workload through the coordinated interaction of the server, terminal, and user components.

[0330] The server processes various sensor data to generate flight routes and work plans. Specifically, it receives topographic information of farmland, crop data, and weather forecasts to determine the optimal work procedure. This uses cloud-based data analysis software and technology that updates the plan based on real-time changing environmental information.

[0331] The drone, acting as the terminal, receives instructions from the server and autonomously performs tasks along a designated route. The drone is equipped with high-precision cameras and sensors, which are used to monitor the health of the crops. The collected data is transmitted to the server in real time and used to continuously assess the crop's condition.

[0332] Users can interface with the system through a terminal or other information terminal and receive feedback tailored to their work status and emotional state. An emotion engine analyzes the user's voice and facial expressions to determine their stress and fatigue levels. Based on this, psychologically sensitive information is provided to the user.

[0333] As a concrete example, a farmer uses their smartphone to activate a drone and input farmland information. The user then prompts the AI ​​model with the message, "Please suggest an efficient flight route based on the farmland information." The AI ​​then quickly calculates the optimal route and suggests it to the user. This system provides a less burdensome farming environment, particularly for the elderly and newcomers.

[0334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0335] Step 1:

[0336] The server receives topographic information of farmland, crop data, and weather forecasts as input from the user. This data is analyzed by a processing unit to generate information necessary for formulating flight routes and work plans. In this process, cloud-based data analysis software is used to calculate optimized work procedures based on the input data. The output is expressed as the optimal flight route and work plan.

[0337] Step 2:

[0338] The server transmits the generated flight route and work plan to the control unit. The drone receives this information as input and begins its work along the specified route. The drone can fly precisely along the designated path using high-precision GPS technology and built-in sensors. The output is the start of the drone's flight along the planned route.

[0339] Step 3:

[0340] The drone, acting as the terminal, collects crop data in real time during flight using its built-in camera and sensors. The collected data is transmitted from the terminal to a server. The input is raw data observed by various sensors, and the output is formatted crop condition data for evaluation.

[0341] Step 4:

[0342] The server analyzes crop data transmitted from drones using an analysis device to evaluate the plant's health and growth progress. Machine learning algorithms are used for anomaly detection and growth prediction. The input is data from sensors, and the output is feedback on the plant's health status as an evaluation result.

[0343] Step 5:

[0344] The user receives feedback on the condition of the evaluated crops through the device. The user interface uses a generative AI model and an emotion engine to analyze the user's voice and facial expressions and provide appropriate feedback messages. The input is the analysis results and the user's emotion data, and the output is a customized feedback message.

[0345] Step 6:

[0346] The user uses this feedback to provide further instructions to the system, generating new prompts. For example, by entering "Recommend the timing of the next pesticide application" as a prompt, a new work plan or adjustments will be made. The output is the proposed work adjustments.

[0347] (Application Example 2)

[0348] 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."

[0349] In factory manufacturing processes, it is necessary to efficiently maintain and improve product quality while reducing stress and burden on workers. This requires a system that manages the entire manufacturing process while simultaneously incorporating flexible feedback that takes into account the workers' conditions.

[0350] 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 including an information processing device for receiving manufacturing information in a factory and formulating work routes and process plans based thereon; means including a control device that can communicate with the information processing device for instructing the operation of manufacturing equipment; and means including an emotion recognition device for recognizing the emotional state of workers and providing feedback corresponding to those emotions. This makes it possible to provide appropriate support to workers while maintaining product quality.

[0351] "Manufacturing information" refers to the materials, processes, schedules, and all related data used in the factory.

[0352] An "information processing device" refers to a computer system used to analyze manufacturing data and formulate optimal work routes and process plans.

[0353] "Control device" refers to a device that communicates with the aforementioned information processing device and issues operational instructions to the manufacturing device.

[0354] An "analysis device" refers to a device that evaluates the condition of a product based on collected data and makes decisions for maintaining quality.

[0355] A "display device" refers to a visual or audio output device used to communicate evaluation results to workers and to present revised plans for the next process.

[0356] An "emotion recognition device" refers to a system that analyzes a worker's voice, facial expressions, or other physiological data to identify their emotional state.

[0357] This invention is a system aimed at improving efficiency in manufacturing operations and providing mental support to workers. The system begins with a factory server receiving "manufacturing information" from each production line and using an "information processing device" to formulate optimal work routes and process plans. Based on this information, a "control device" issues precise operational instructions to each manufacturing device.

[0358] The "analysis device" uses data collected from the manufacturing equipment to evaluate the condition of the product and analyze possible defects and quality issues. The results are provided to the operator via the "display device," which helps in revising the plan for the next process.

[0359] Furthermore, the worker's "emotion recognition device" analyzes the worker's emotional state and provides feedback tailored to that emotion. For example, if the device determines that the workload is heavy, it can suggest relaxation techniques or adjust parts of the task.

[0360] This system is designed to reduce stress from long hours of work while maintaining worker productivity. For example, when a worker starts operating a new machine in a factory, an emotion recognition device could detect their anxiety and display specific instructions on what to do next, as well as reassuring feedback.

[0361] A concrete example is a situation where workers can check the current process progress and product status through smart glasses. If a worker feels tired, a message such as "It might be a good idea to take a short break" will appear on the screen, ultimately reducing the burden on the worker.

[0362] Examples of prompts for a generative AI model:

[0363] "How can I design a factory management app that monitors workers' emotional states in real time and provides appropriate feedback based on their location and the progress of the production line?"

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The server receives manufacturing information from within the factory. This input includes material information, manufacturing processes, and planned schedules. The server sends this data to an information processing unit, which then executes algorithms to develop optimal work routes and process plans. This process outputs a specific process plan designed to maximize the efficiency of the production line.

[0367] Step 2:

[0368] The terminal receives the process plan from the information processing unit, and the control unit transmits specific operational instructions to the manufacturing equipment. This input includes information on the operation details and timing for each process. Based on this, the control unit starts the manufacturing equipment and executes the planned process. As a result, each manufacturing line begins to operate efficiently.

[0369] Step 3:

[0370] The manufacturing equipment transmits data collected during operation to the server. This input includes information about product quality and any abnormal conditions. The server's analysis equipment receives this data and performs analysis for quality control. The output obtained at this stage is the result of the quality evaluation.

[0371] Step 4:

[0372] The user receives the analysis results via a display device. The evaluation results are fed back to be incorporated into the next process plan. This input includes quality evaluation results and recommended corrections. The user reviews them and instructs on the necessary adjustments for subsequent processes.

[0373] Step 5:

[0374] The terminal uses an emotion recognition device to detect the worker's voice and facial expressions, and sends this data to a server. Based on this data, the server performs emotion analysis and determines how to provide appropriate feedback. This output includes a specific message tailored to the worker's emotions.

[0375] Step 6:

[0376] Users receive feedback in real time through smart glasses. Messages output by the server are displayed to the user, and workers adjust their actions based on this feedback, improving work efficiency and mental health.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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".

[0393] This invention is an AI-equipped drone system that assists farmers in performing agricultural work efficiently. Based on farmland information, it formulates flight routes and work plans, and the drone autonomously carries out the work, thereby improving the efficiency of agricultural work.

[0394] The server automatically calculates the optimal flight route and work plan based on farmland information (terrain, crops, and weather data) received from the user. This plan includes pesticide application points, crop monitoring areas, and priority of areas requiring harvesting.

[0395] The drone control system receives instructions from a server and controls the drone in real time to ensure safe and efficient flight. This includes features such as altitude adjustment based on terrain and obstacle avoidance.

[0396] The drone, acting as the terminal, flies over a designated work area, using cameras and sensors to monitor the condition of the crops. The collected information is transmitted to a server as data indicating the health and growth of the crops.

[0397] The server processes the received data using an analysis device and utilizes an AI model to detect crop health and abnormalities. The analysis results are then communicated to the user and provided as suggestions for improving farm work and scheduling the next tasks.

[0398] Users can easily modify their next work plan based on the information provided. For example, an elderly farmer can use their smartphone to specify a map of their entire farmland and request an AI drone to spray pesticides. The drone will then automatically fly along the planned route and perform optimal spraying. This process reduces the physical burden on the elderly and improves agricultural efficiency.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] Users input topographic information, crop types, and weather data for their farmland using a dedicated app, and then send this information to the server.

[0402] Step 2:

[0403] The server uses received farmland information and an internal database to formulate flight routes and work plans. This includes determining optimal pesticide application points and monitoring areas.

[0404] Step 3:

[0405] The server transmits the formulated work plan and flight route to the drone control unit. The drone control unit then prepares the drone for flight based on the received plan.

[0406] Step 4:

[0407] The drone, acting as the terminal, begins flying along a designated route. During flight, the drone sprays pesticides and monitors the condition of the crops using cameras and sensors.

[0408] Step 5:

[0409] The drone transmits data collected during flight to a server in real time. This data includes image data and environmental data.

[0410] Step 6:

[0411] The server analyzes the received data and uses an AI model to evaluate the health of the crops. If an anomaly is detected, it generates that information as feedback.

[0412] Step 7:

[0413] Based on feedback received from the server, users adjust their next work plan within the app. This information can be used to perform agricultural tasks more efficiently.

[0414] (Example 1)

[0415] 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."

[0416] Traditional farming methods are labor-intensive and time-consuming, and make it difficult to properly monitor crop health and environmental conditions. Furthermore, inexperience and an aging workforce are increasingly making it difficult to develop efficient work plans. To address these challenges, a system is needed that enables farmers to work efficiently and effectively, thereby achieving sustainable agriculture.

[0417] 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.

[0418] In this invention, the server includes means for receiving farmland information and formulating an optimal flight path and work plan based on it using a generating AI model; means for directing the drone's movements and controlling safe and efficient flight in real time, and means for analyzing environmental data collected by the drone and analyzing the health of crops using a generating AI model. This enables farmers to optimize farm work and manage crop health with minimal effort.

[0419] "Agricultural land information" is a general term for information related to agricultural land, such as topography, crop types, and weather data.

[0420] A "flight path" is the optimal flight route set up to allow a drone to move safely and efficiently over designated farmland.

[0421] A "work plan" outlines the specific procedures and strategies for farm work, including pesticide application points and crop monitoring areas.

[0422] A "generative AI model" is an artificial intelligence model used to analyze large amounts of data and make decisions based on the results.

[0423] An "information processing device" is a device or system that performs calculations and analyses based on received data and outputs the results.

[0424] A "drone controller" is a device that manages the operation of a drone in real time, supporting safe and efficient flight.

[0425] "Environmental data" refers to data about the surrounding environment, such as the condition of crops, soil conditions, and weather conditions, collected by drones.

[0426] An "analyzer" is a device that analyzes collected data and uses the results to evaluate the health of crops.

[0427] One embodiment of this invention involves using an AI-equipped drone system with the aim of enabling agricultural workers to perform tasks efficiently and effectively.

[0428] The server receives farmland information entered by the user and uses an AI model to generate the optimal flight path and work plan based on that information. This plan includes pesticide application points and crop monitoring areas, and the AI ​​model can use TensorFlow or similar machine learning libraries to analyze large amounts of data. Users input this information from devices such as smartphones and tablets. For example, a user might specify the terrain and crop types of their farmland and make a request such as, "Please spray pesticides on the west side of the tomato field."

[0429] The plan formulated by the server is transmitted to the drone controller, which manages the drone's movements in real time. This controller automatically adjusts the flight altitude based on terrain information, ensuring safe and efficient flight.

[0430] The drone, acting as the terminal, flies according to a pre-determined plan, collecting environmental data using its built-in camera and sensors. This data, which indicates the health and growth status of crops, is transmitted from the drone to a server.

[0431] The server receives the transmitted environmental data and uses AI to analyze the health of the crops. The analysis results are notified to the user and provided as suggestions for improving the next work schedule. For example, information such as "We detected an abnormality in the tomato leaves, so we need to spray additional pesticides on the east side" is sent.

[0432] An example of a prompt message could be input to the generating AI model, such as, "Based on the terrain and weather data of the farmland specified by the user, please create the optimal flight route and work plan. The results should include pesticide spraying points and crop monitoring areas."

[0433] Thus, the present invention contributes to the optimization of agricultural production by significantly improving the efficiency of farmland management.

[0434] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0435] Step 1:

[0436] Users input farmland information using smartphones or tablets. This information includes topography, crop types, and weather data. The entered information is sent to the server. In this step, the specific action is to digitize the data collected by the user and send it to the server.

[0437] Step 2:

[0438] The server uses a generated AI model based on the received farmland information to formulate the optimal flight path and work plan. In this process, the AI ​​model analyzes the input data to determine the areas where pesticide spraying is necessary and the monitoring areas. The output is a detailed work plan. Specifically, this step involves the AI ​​calculating and analyzing the input data and automatically creating the plan.

[0439] Step 3:

[0440] The server transmits the created work plan to the drone controller. The drone controller receives this and prepares the drone for safe and efficient flight. The input for this step is the work plan, and the output is flight instruction information. Specifically, the controller prepares to use terrain information to adjust altitude.

[0441] Step 4:

[0442] The drone, acting as the terminal, flies along a designated route based on instructions from the drone controller. During flight, the drone collects environmental data using its built-in sensors and camera. The input is instructions from the controller, and the output is the collected environmental data. Specifically, its operation involves flying while monitoring the condition of crops using its sensors.

[0443] Step 5:

[0444] The server receives environmental data transmitted from the drone and uses an AI model to analyze the health of the crops. The input is environmental data, and the output is the analysis results. Specifically, the AI ​​performs analysis to identify anomalies and problem areas.

[0445] Step 6:

[0446] The server notifies the user of the analysis results. The user can then use this information to revise their next work plan. The input is the analysis results, and the output is the notified information. The specific operation involves providing information through a user interface.

[0447] (Application Example 1)

[0448] 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."

[0449] Inventory management and stocktaking in logistics centers are time-consuming and labor-intensive, making efficient operation essential. However, conventional methods have problems with real-time inventory tracking and efficient item placement. This raises concerns about unnecessary costs and decreased accuracy in inventory management. This invention aims to improve the efficiency and accuracy of inventory management in logistics centers.

[0450] 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.

[0451] In this invention, the server includes means including a processing device for receiving area information and formulating flight paths and work plans based thereon; means including an unmanned aircraft control device that can communicate with the processing device for instructing the operation of the unmanned aircraft; means including an analysis device for analyzing data collected by the unmanned aircraft and evaluating the condition of goods; and means including an augmented reality display device as a support device for an operator wearing a visual assistance device to monitor the operation of the unmanned aircraft. This enables real-time inventory status monitoring and efficient logistics operations.

[0452] "Area information" refers to information about the physical space required when an unmanned aircraft flies and performs its duties.

[0453] A "flight path" is the route that an unmanned aircraft takes to reach its destination.

[0454] A "work plan" is a schedule that includes the specific tasks and procedures that the unmanned aerial vehicle (UAV) should perform.

[0455] A "processing device" is a device that has information processing functions for formulating flight paths and operational plans based on received information.

[0456] An "unmanned aircraft control system" is a device that has control functions to manage and direct the operation of an unmanned aircraft.

[0457] An "analysis device" is an information processing device that analyzes data collected by an unmanned aerial vehicle and evaluates the information based on the results.

[0458] A "visual assistance device" is a device used by workers to visually confirm the working status of an unmanned aircraft, and it is equipped with an augmented reality display function.

[0459] An "augmented reality display device" is a display device that has the function of overlaying digital information onto real-world visual information.

[0460] The system for implementing this invention is used when an unmanned aerial vehicle (UAV) autonomously performs inventory management tasks within a logistics center. The server formulates an optimal flight path and work plan based on area information received from the center. This plan is transmitted to the UAV control unit, and the UAV operates according to the plan. The UAV is equipped with cameras and sensors, which are used to scan QR codes on shelves and collect inventory data.

[0461] The server processes the collected data in real time using an analysis device to evaluate the inventory status. This evaluation result is displayed on a visual assistance device worn by the user, i.e., an augmented reality display device, enabling workers to immediately issue necessary instructions at the logistics center.

[0462] As a concrete example, when new products arrive at a logistics center, the server receives a prompt saying "Start checking inventory for new products." This prompts an automated system (DVR) to move to a designated area, enabling highly efficient inventory checks. This system allows for quick and accurate inventory management at the logistics center, significantly improving operational efficiency.

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The server receives area information from the logistics center and uses this information as input data. The server uses an algorithm to formulate the optimal flight path and operational plan. It generates plan data that is sent to the unmanned aerial vehicle control system as output.

[0466] Step 2:

[0467] The unmanned aircraft control system generates operational instructions for the unmanned aircraft based on planning data received from the server. It receives planning data as input and generates commands as output to ensure the unmanned aircraft moves safely along its flight path.

[0468] Step 3:

[0469] The drone flies around the logistics center following commands from its control unit. Equipped with cameras and sensors, the drone scans QR codes on inventory items as input, collecting inventory data. The collected data is transmitted to a server in real time.

[0470] Step 4:

[0471] The server processes inventory data sent from the drone using a generative AI model in the analysis device. Based on the input data, it evaluates the inventory status and sends the evaluation result as output to the visual assistance device.

[0472] Step 5:

[0473] The user wears a visual aid and receives inventory valuation results as output. This allows them to monitor the progress of work within the logistics center and, if necessary, send instructions to the server using prompt messages.

[0474] 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.

[0475] This invention combines an AI-powered drone system that supports agricultural work with an emotion engine to provide an efficient and less stressful environment for farmers. It features a system that formulates flight routes and work plans based on farmland information, allowing the drone to perform tasks automatically while simultaneously recognizing the user's emotions and providing corresponding feedback.

[0476] The server receives topographic information, crop data, and weather forecasts from the user and uses this information to generate the optimal flight route and work plan. This plan includes pesticide spraying, crop monitoring, and harvesting assistance. Once the plan is formulated, the server sends the necessary instructions to the drone control unit, and the drone autonomously carries out the work accordingly.

[0477] The drone, acting as the terminal, flies over farmland and performs various tasks based on instructions from the server, collecting crop data using its onboard sensors and cameras. The collected data is transmitted to the server in real time and used for analysis to assess the health of the crops.

[0478] Furthermore, the emotion engine analyzes user input such as voice and facial expressions to determine emotional states such as stress and anxiety. Based on this information, the system's user interface provides emotion-appropriate feedback notifications. For example, if fatigue is particularly evident, the notification frequency is reduced, and simple, reassuring messages are provided.

[0479] As a concrete example, consider a scenario where a farmer operates a smartphone while performing daily farm work. The user inputs farmland information and activates a drone, which flies along a planned route and monitors the health of the crops. When feedback is provided to the user regarding the progress of the work and the condition of the crops, the emotion engine selects feedback that is tailored to the user's state, allowing the user to continue working with peace of mind. In this way, utilizing this system reduces the burden on farmers, including the elderly, and improves work efficiency.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The user launches a dedicated application and inputs information about the farmland's topography, crop types, and current weather data. This information is then sent to the server.

[0483] Step 2:

[0484] The server uses AI algorithms based on the received farmland information to formulate the optimal flight route and work plan. This plan includes pesticide application areas, crop monitoring areas, and harvest target areas.

[0485] Step 3:

[0486] The server transmits the formulated plan to the drone control unit and issues instructions to launch the drone. The control unit prepares to automatically adjust the drone's flight altitude while taking terrain information into consideration.

[0487] Step 4:

[0488] The drone, acting as the terminal, flies over farmland following its assigned flight route, collecting image data and sensor data of crops. The drone also automatically sprays pesticides according to the designated route.

[0489] Step 5:

[0490] The data collected by the drone is transmitted to a server in real time, where it is analyzed using an AI model in an analysis device. This allows for the evaluation of crop health and signs of disease.

[0491] Step 6:

[0492] The analysis results are fed back to the user from the server, and at the same time, the emotion engine analyzes the user's voice and facial expressions to determine their emotional state. Based on this data, the content and method of feedback are adjusted.

[0493] Step 7:

[0494] Users receive feedback notifications on their smartphones, adjusted by an emotion engine, to adjust their next work plan. Because the notifications are clear and emotionally sensitive, users can reduce stress and work efficiently on their farm tasks.

[0495] (Example 2)

[0496] 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."

[0497] Traditional agricultural practices have presented challenges in that efficient crop management requires considerable effort and time. Furthermore, the provision of a work environment that considers the emotional state of farmers has been insufficient, highlighting the need for stress reduction. Additionally, real-time monitoring of crop conditions and prompt responses have been difficult.

[0498] 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.

[0499] In this invention, the server includes means including a processing device for receiving farmland information and formulating flight routes and work plans based thereon; means including a control device that can communicate with the processing device for instructing the operation of the drone; means including an analysis device for analyzing data collected by the drone and evaluating the condition of plants; means including an emotion engine for analyzing the user's voice and facial expressions and determining their emotional state; and means including an information providing device for providing feedback according to the emotional state. This makes it possible to reduce the burden on agricultural workers and provide an efficient and less stressful working environment.

[0500] "Agricultural land information" refers to data concerning the topography of agricultural land, the types of crops, their growth status, and the surrounding weather conditions.

[0501] A "flight route" is a planned path for a drone to travel over farmland.

[0502] A "work plan" is a plan that includes detailed procedures and schedules for agricultural work, tailored to the specific purpose.

[0503] A "processing device" is an electronic device used to receive and analyze information, and it plays a role in formulating plans.

[0504] A "control device" is a device that directs and manages the drone's movements based on the flight route and work plan.

[0505] An "analysis device" is a device used to analyze data collected by drones and evaluate the condition of crops.

[0506] "Plant condition" refers to information indicating the health and growth progress of a crop.

[0507] An "emotion engine" is a system that recognizes and analyzes a user's emotional state based on their voice and facial expressions.

[0508] An "information provision device" is a device that transmits analysis results and emotion-based feedback to the user.

[0509] This invention is an AI-powered drone system aimed at improving agricultural efficiency and reducing stress. The system is designed to enable farmers to perform efficient tasks without increasing their workload through the coordinated interaction of the server, terminal, and user components.

[0510] The server processes various sensor data to generate flight routes and work plans. Specifically, it receives topographic information of farmland, crop data, and weather forecasts to determine the optimal work procedure. This uses cloud-based data analysis software and technology that updates the plan based on real-time changing environmental information.

[0511] The drone, acting as the terminal, receives instructions from the server and autonomously performs tasks along a designated route. The drone is equipped with high-precision cameras and sensors, which are used to monitor the health of the crops. The collected data is transmitted to the server in real time and used to continuously assess the crop's condition.

[0512] Users can interface with the system through a terminal or other information terminal and receive feedback tailored to their work status and emotional state. An emotion engine analyzes the user's voice and facial expressions to determine their stress and fatigue levels. Based on this, psychologically sensitive information is provided to the user.

[0513] As a concrete example, a farmer uses their smartphone to activate a drone and input farmland information. The user then prompts the AI ​​model with the message, "Please suggest an efficient flight route based on the farmland information." The AI ​​then quickly calculates the optimal route and suggests it to the user. This system provides a less burdensome farming environment, particularly for the elderly and newcomers.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] The server receives topographic information of farmland, crop data, and weather forecasts as input from the user. This data is analyzed by a processing unit to generate information necessary for formulating flight routes and work plans. In this process, cloud-based data analysis software is used to calculate optimized work procedures based on the input data. The output is expressed as the optimal flight route and work plan.

[0517] Step 2:

[0518] The server transmits the generated flight route and work plan to the control unit. The drone receives this information as input and begins its work along the specified route. The drone can fly precisely along the designated path using high-precision GPS technology and built-in sensors. The output is the start of the drone's flight along the planned route.

[0519] Step 3:

[0520] The drone, acting as the terminal, collects crop data in real time during flight using its built-in camera and sensors. The collected data is transmitted from the terminal to a server. The input is raw data observed by various sensors, and the output is formatted crop condition data for evaluation.

[0521] Step 4:

[0522] The server analyzes crop data transmitted from drones using an analysis device to evaluate the health and growth progress of the plants. Machine learning algorithms are used for the analysis to detect anomalies and predict growth. The input is data from sensors, and the output is feedback on the health status as an evaluation result.

[0523] Step 5:

[0524] The user receives feedback on the condition of the evaluated crops through the device. The user interface uses a generative AI model and an emotion engine to analyze the user's voice and facial expressions and provide appropriate feedback messages. The input is the analysis results and the user's emotion data, and the output is a customized feedback message.

[0525] Step 6:

[0526] Based on this feedback, the user provides further instructions to the system, generating new prompts. For example, by entering "Recommend the timing of the next pesticide application" as a prompt, a new work plan or adjustments will be made. The output is the proposed work adjustments.

[0527] (Application Example 2)

[0528] 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."

[0529] In factory manufacturing processes, it is necessary to efficiently maintain and improve product quality while reducing stress and burden on workers. This requires a system that manages the entire manufacturing process while simultaneously incorporating flexible feedback that takes into account the workers' conditions.

[0530] 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 including an information processing device for receiving manufacturing information in a factory and formulating work routes and process plans based thereon; means including a control device that can communicate with the information processing device for instructing the operation of manufacturing equipment; and means including an emotion recognition device for recognizing the emotional state of workers and providing feedback corresponding to those emotions. This makes it possible to provide appropriate support to workers while maintaining product quality.

[0531] "Manufacturing information" refers to the materials, processes, schedules, and all related data used in the factory.

[0532] An "information processing device" refers to a computer system used to analyze manufacturing data and formulate optimal work routes and process plans.

[0533] "Control device" refers to a device that communicates with the aforementioned information processing device and issues operational instructions to the manufacturing device.

[0534] An "analysis device" refers to a device that evaluates the condition of a product based on collected data and makes decisions for maintaining quality.

[0535] A "display device" refers to a visual or audio output device used to communicate evaluation results to workers and to present revised plans for the next process.

[0536] An "emotion recognition device" refers to a system that analyzes a worker's voice, facial expressions, or other physiological data to identify their emotional state.

[0537] This invention is a system aimed at improving efficiency in manufacturing operations and providing mental support to workers. The system begins with a factory server receiving "manufacturing information" from each production line and using an "information processing device" to formulate optimal work routes and process plans. Based on this information, a "control device" issues precise operational instructions to each manufacturing device.

[0538] The "analysis device" uses data collected from the manufacturing equipment to evaluate the condition of the product and analyze possible defects and quality issues. The results are provided to the operator via the "display device," which helps in revising the plan for the next process.

[0539] Furthermore, the worker's "emotion recognition device" analyzes the worker's emotional state and provides feedback tailored to that emotion. For example, if the device determines that the workload is heavy, it can suggest relaxation techniques or adjust parts of the task.

[0540] This system is designed to reduce stress from long hours of work while maintaining worker productivity. For example, if a worker starts operating a new machine in a factory, an emotion recognition device could detect their anxiety and display specific instructions on what to do next, as well as reassuring feedback.

[0541] A concrete example is a situation where workers can check the current process progress and product status through smart glasses. If a worker feels tired, a message such as "It might be a good idea to take a short break" will appear on the screen, ultimately reducing the burden on the worker.

[0542] Examples of prompts for a generative AI model:

[0543] "How can I design a factory management app that monitors workers' emotional states in real time and provides appropriate feedback based on their location and the progress of the production line?"

[0544] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0545] Step 1:

[0546] The server receives manufacturing information from within the factory. This input includes material information, manufacturing processes, and planned schedules. The server sends this data to an information processing unit, which executes algorithms to formulate the optimal work routes and process plans. This process outputs a specific process plan designed to maximize the efficiency of the work line.

[0547] Step 2:

[0548] The terminal receives the process plan from the information processing unit, and the control unit transmits specific operational instructions to the manufacturing equipment. This input includes information on the operation details and timing for each process. Based on this, the control unit starts the manufacturing equipment and executes the planned process. As a result, each manufacturing line begins to operate efficiently.

[0549] Step 3:

[0550] The manufacturing equipment transmits data collected during operation to the server. This input includes information about product quality and any abnormal conditions. The server's analysis equipment receives this data and performs analysis for quality control. The output obtained at this stage is the result of the quality evaluation.

[0551] Step 4:

[0552] The user receives the analysis results via a display device. The evaluation results are fed back to be incorporated into the next process plan. This input includes quality evaluation results and recommended corrections. The user reviews them and instructs on the necessary adjustments for subsequent processes.

[0553] Step 5:

[0554] The terminal uses an emotion recognition device to detect the worker's voice and facial expressions, and sends this data to a server. Based on this data, the server performs emotion analysis and determines how to provide appropriate feedback. This output includes a specific message tailored to the worker's emotions.

[0555] Step 6:

[0556] Users receive feedback in real time through smart glasses. Messages output by the server are displayed to the user, and workers adjust their actions based on this feedback, improving work efficiency and mental health.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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".

[0574] This invention is an AI-equipped drone system that assists farmers in performing agricultural work efficiently. Based on farmland information, it formulates flight routes and work plans, and the drone autonomously carries out the work, thereby improving the efficiency of agricultural work.

[0575] The server automatically calculates the optimal flight route and work plan based on farmland information (terrain, crops, and weather data) received from the user. This plan includes pesticide application points, crop monitoring areas, and priority of areas requiring harvesting.

[0576] The drone control system receives instructions from a server and controls the drone in real time to ensure safe and efficient flight. This includes features such as altitude adjustment based on terrain and obstacle avoidance.

[0577] The drone, acting as the terminal, flies over a designated work area, using cameras and sensors to monitor the condition of the crops. The collected information is transmitted to a server as data indicating the health and growth of the crops.

[0578] The server processes the received data using an analysis device and utilizes an AI model to detect crop health and abnormalities. The analysis results are then communicated to the user and provided as suggestions for improving farm work and scheduling the next tasks.

[0579] Users can easily modify their next work plan based on the information provided. For example, an elderly farmer can use their smartphone to specify a map of their entire farmland and request an AI drone to spray pesticides. The drone will then automatically fly along the planned route and perform optimal spraying. This process reduces the physical burden on the elderly and improves agricultural efficiency.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] Users input topographic information, crop types, and weather data for their farmland using a dedicated app, and then send this information to the server.

[0583] Step 2:

[0584] The server uses received farmland information and an internal database to formulate flight routes and work plans. This includes determining optimal pesticide application points and monitoring areas.

[0585] Step 3:

[0586] The server transmits the formulated work plan and flight route to the drone control unit. The drone control unit then prepares the drone for flight based on the received plan.

[0587] Step 4:

[0588] The drone, acting as the terminal, begins flying along a designated route. During flight, the drone sprays pesticides and monitors the condition of the crops using cameras and sensors.

[0589] Step 5:

[0590] The drone transmits data collected during flight to a server in real time. This data includes image data and environmental data.

[0591] Step 6:

[0592] The server analyzes the received data and uses an AI model to evaluate the health of the crops. If an anomaly is detected, it generates that information as feedback.

[0593] Step 7:

[0594] Based on feedback received from the server, users adjust their next work plan within the app. This information can be used to perform agricultural tasks more efficiently.

[0595] (Example 1)

[0596] 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".

[0597] Traditional farming methods are labor-intensive and time-consuming, and make it difficult to properly monitor crop health and environmental conditions. Furthermore, inexperience and an aging workforce are increasingly making it difficult to develop efficient work plans. To address these challenges, a system is needed that enables farmers to work efficiently and effectively, thereby achieving sustainable agriculture.

[0598] 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.

[0599] In this invention, the server includes means for receiving farmland information and formulating an optimal flight path and work plan based on it using a generating AI model; means for directing the drone's movements and controlling safe and efficient flight in real time, and means for analyzing environmental data collected by the drone and analyzing the health of crops using a generating AI model. This enables farmers to optimize farm work and manage crop health with minimal effort.

[0600] "Agricultural land information" is a general term for information related to agricultural land, such as topography, crop types, and weather data.

[0601] A "flight path" is the optimal flight route set up to allow a drone to move safely and efficiently over designated farmland.

[0602] A "work plan" outlines the specific procedures and strategies for farm work, including pesticide application points and crop monitoring areas.

[0603] A "generative AI model" is an artificial intelligence model used to analyze large amounts of data and make decisions based on the results.

[0604] An "information processing device" is a device or system that performs calculations and analyses based on received data and outputs the results.

[0605] A "drone controller" is a device that manages the operation of a drone in real time, supporting safe and efficient flight.

[0606] "Environmental data" refers to data about the surrounding environment, such as the condition of crops, soil conditions, and weather conditions, collected by drones.

[0607] An "analyzer" is a device that analyzes collected data and uses the results to evaluate the health of crops.

[0608] One embodiment of this invention involves using an AI-equipped drone system with the aim of enabling agricultural workers to perform tasks efficiently and effectively.

[0609] The server receives farmland information entered by the user and uses an AI model to generate the optimal flight path and work plan based on that information. This plan includes pesticide application points and crop monitoring areas, and the AI ​​model can use TensorFlow or similar machine learning libraries to analyze large amounts of data. Users input this information from devices such as smartphones and tablets. For example, a user might specify the terrain and crop types of their farmland and make a request such as, "Please spray pesticides on the west side of the tomato field."

[0610] The plan formulated by the server is transmitted to the drone controller, which manages the drone's movements in real time. This controller automatically adjusts the flight altitude based on terrain information, ensuring safe and efficient flight.

[0611] The drone, acting as the terminal, flies according to a pre-determined plan, collecting environmental data using its built-in camera and sensors. This data, which indicates the health and growth status of crops, is transmitted from the drone to a server.

[0612] The server receives the transmitted environmental data and uses AI to analyze the health of the crops. The analysis results are notified to the user and provided as suggestions for improving the next work schedule. For example, information such as "We detected an abnormality in the tomato leaves, so we need to spray additional pesticides on the east side" is sent.

[0613] An example of a prompt message could be input to the generating AI model, such as, "Based on the terrain and weather data of the farmland specified by the user, please create the optimal flight route and work plan. The results should include pesticide spraying points and crop monitoring areas."

[0614] Thus, the present invention contributes to the optimization of agricultural production by significantly improving the efficiency of farmland management.

[0615] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0616] Step 1:

[0617] Users input farmland information using smartphones or tablets. This information includes topography, crop types, and weather data. The entered information is sent to the server. In this step, the specific action is to digitize the data collected by the user and send it to the server.

[0618] Step 2:

[0619] The server uses a generated AI model based on the received farmland information to formulate the optimal flight path and work plan. In this process, the AI ​​model analyzes the input data to determine the areas where pesticide spraying is necessary and the monitoring areas. The output is a detailed work plan. Specifically, this step involves the AI ​​calculating and analyzing the input data and automatically creating the plan.

[0620] Step 3:

[0621] The server transmits the created work plan to the drone controller. The drone controller receives this and prepares the drone for safe and efficient flight. The input for this step is the work plan, and the output is flight instruction information. Specifically, the controller prepares to use terrain information to adjust altitude.

[0622] Step 4:

[0623] The drone, acting as the terminal, flies along a designated route based on instructions from the drone controller. During flight, the drone collects environmental data using its built-in sensors and camera. The input is instructions from the controller, and the output is the collected environmental data. Specifically, its operation involves flying while monitoring the condition of crops using its sensors.

[0624] Step 5:

[0625] The server receives environmental data transmitted from the drone and uses an AI model to analyze the health of the crops. The input is environmental data, and the output is the analysis results. Specifically, the AI ​​performs analysis to identify anomalies and problem areas.

[0626] Step 6:

[0627] The server notifies the user of the analysis results. The user can then use this information to revise their next work plan. The input is the analysis results, and the output is the notified information. The specific operation involves providing information through a user interface.

[0628] (Application Example 1)

[0629] 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".

[0630] Inventory management and stocktaking in logistics centers are time-consuming and labor-intensive, making efficient operation essential. However, conventional methods have problems with real-time inventory tracking and efficient item placement. This raises concerns about unnecessary costs and decreased accuracy in inventory management. This invention aims to improve the efficiency and accuracy of inventory management in logistics centers.

[0631] 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.

[0632] In this invention, the server includes means including a processing device for receiving area information and formulating flight paths and work plans based thereon; means including an unmanned aircraft control device that can communicate with the processing device for instructing the operation of the unmanned aircraft; means including an analysis device for analyzing data collected by the unmanned aircraft and evaluating the condition of goods; and means including an augmented reality display device as a support device for an operator wearing a visual assistance device to monitor the operation of the unmanned aircraft. This enables real-time inventory status monitoring and efficient logistics operations.

[0633] "Area information" refers to information about the physical space required when an unmanned aircraft flies and performs its duties.

[0634] A "flight path" is the route that an unmanned aircraft takes to reach its destination.

[0635] A "work plan" is a schedule that includes the specific tasks and procedures that the unmanned aerial vehicle (UAV) should perform.

[0636] A "processing device" is a device that has information processing functions for formulating flight paths and operational plans based on received information.

[0637] An "unmanned aircraft control system" is a device that has control functions to manage and direct the operation of an unmanned aircraft.

[0638] An "analysis device" is an information processing device that analyzes data collected by an unmanned aerial vehicle and evaluates the information based on the results.

[0639] A "visual assistance device" is a device used by workers to visually confirm the working status of an unmanned aircraft, and it is equipped with an augmented reality display function.

[0640] An "augmented reality display device" is a display device that has the function of overlaying digital information onto real-world visual information.

[0641] The system for implementing this invention is used when an unmanned aerial vehicle (UAV) autonomously performs inventory management tasks within a logistics center. The server formulates an optimal flight path and work plan based on area information received from the center. This plan is transmitted to the UAV control unit, and the UAV operates according to the plan. The UAV is equipped with cameras and sensors, which are used to scan QR codes on shelves and collect inventory data.

[0642] The server processes the collected data in real time using an analysis device to evaluate the inventory status. This evaluation result is displayed on a visual assistance device worn by the user, i.e., an augmented reality display device, enabling workers to immediately issue necessary instructions at the logistics center.

[0643] As a concrete example, when new products arrive at a logistics center, the server receives a prompt saying "Start checking inventory for new products." This prompts an automated system (DVR) to move to a designated area, enabling highly efficient inventory checks. This system allows for quick and accurate inventory management at the logistics center, significantly improving operational efficiency.

[0644] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0645] Step 1:

[0646] The server receives area information from the logistics center and uses this information as input data. The server uses an algorithm to formulate the optimal flight path and operational plan. It generates plan data that is sent to the unmanned aerial vehicle control system as output.

[0647] Step 2:

[0648] The unmanned aircraft control system generates operational instructions for the unmanned aircraft based on planning data received from the server. It receives planning data as input and generates commands as output to ensure the unmanned aircraft moves safely along its flight path.

[0649] Step 3:

[0650] The drone flies around the logistics center following commands from its control unit. Equipped with cameras and sensors, the drone scans QR codes on inventory items as input, collecting inventory data. The collected data is transmitted to a server in real time.

[0651] Step 4:

[0652] The server processes inventory data sent from the drone using a generative AI model in the analysis device. Based on the input data, it evaluates the inventory status and sends the evaluation result as output to the visual assistance device.

[0653] Step 5:

[0654] The user wears a visual aid and receives inventory valuation results as output. This allows them to monitor the progress of work within the logistics center and, if necessary, send instructions to the server using prompt messages.

[0655] 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.

[0656] This invention combines an AI-powered drone system that supports agricultural work with an emotion engine to provide an efficient and less stressful environment for farmers. It features a system that formulates flight routes and work plans based on farmland information, allowing the drone to perform tasks automatically while simultaneously recognizing the user's emotions and providing corresponding feedback.

[0657] The server receives topographic information, crop data, and weather forecasts from the user and uses this information to generate the optimal flight route and work plan. This plan includes pesticide spraying, crop monitoring, and harvesting assistance. Once the plan is formulated, the server sends the necessary instructions to the drone control unit, and the drone autonomously carries out the work accordingly.

[0658] The drone, acting as the terminal, flies over farmland and performs various tasks based on instructions from the server, collecting crop data using its onboard sensors and cameras. The collected data is transmitted to the server in real time and used for analysis to assess the health of the crops.

[0659] Furthermore, the emotion engine analyzes user input such as voice and facial expressions to determine emotional states such as stress and anxiety. Based on this information, the system's user interface provides emotion-appropriate feedback notifications. For example, if fatigue is particularly evident, the notification frequency is reduced, and simple, reassuring messages are provided.

[0660] As a concrete example, consider a scenario where a farmer operates a smartphone while performing daily farm work. The user inputs farmland information and activates a drone, which flies along a planned route and monitors the health of the crops. When feedback is provided to the user regarding the progress of the work and the condition of the crops, the emotion engine selects feedback that is tailored to the user's state, allowing the user to continue working with peace of mind. In this way, utilizing this system reduces the burden on farmers, including the elderly, and improves work efficiency.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The user launches a dedicated application and inputs information about the farmland's topography, crop types, and current weather data. This information is then sent to the server.

[0664] Step 2:

[0665] The server uses AI algorithms based on the received farmland information to formulate the optimal flight route and work plan. This plan includes pesticide application areas, crop monitoring areas, and harvest target areas.

[0666] Step 3:

[0667] The server transmits the formulated plan to the drone control unit and issues instructions to launch the drone. The control unit prepares to automatically adjust the drone's flight altitude while taking terrain information into consideration.

[0668] Step 4:

[0669] The drone, acting as the terminal, flies over farmland following its assigned flight route, collecting image data and sensor data of crops. The drone also automatically sprays pesticides according to the designated route.

[0670] Step 5:

[0671] The data collected by the drone is transmitted to a server in real time, where it is analyzed using an AI model in an analysis device. This allows for the evaluation of crop health and signs of disease.

[0672] Step 6:

[0673] The analysis results are fed back to the user from the server, and at the same time, the emotion engine analyzes the user's voice and facial expressions to determine their emotional state. Based on this data, the content and method of feedback are adjusted.

[0674] Step 7:

[0675] Users receive feedback notifications on their smartphones, adjusted by an emotion engine, to adjust their next work plan. Because the notifications are clear and emotionally sensitive, users can reduce stress and work efficiently on their farm tasks.

[0676] (Example 2)

[0677] 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".

[0678] Traditional agricultural practices have presented challenges in that efficient crop management requires considerable effort and time. Furthermore, the provision of a work environment that considers the emotional state of farmers has been insufficient, highlighting the need for stress reduction. Additionally, real-time monitoring of crop conditions and prompt responses have been difficult.

[0679] 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.

[0680] In this invention, the server includes means including a processing device for receiving farmland information and formulating flight routes and work plans based thereon; means including a control device that can communicate with the processing device for instructing the operation of the drone; means including an analysis device for analyzing data collected by the drone and evaluating the condition of plants; means including an emotion engine for analyzing the user's voice and facial expressions and determining their emotional state; and means including an information providing device for providing feedback according to the emotional state. This makes it possible to reduce the burden on agricultural workers and provide an efficient and less stressful working environment.

[0681] "Agricultural land information" refers to data concerning the topography of agricultural land, the types of crops, their growth status, and the surrounding weather conditions.

[0682] A "flight route" is a planned path for a drone to travel over farmland.

[0683] A "work plan" is a plan that includes detailed procedures and schedules for agricultural work, tailored to the specific purpose.

[0684] A "processing device" is an electronic device used to receive and analyze information, and it plays a role in formulating plans.

[0685] A "control device" is a device that directs and manages the drone's movements based on the flight route and work plan.

[0686] An "analysis device" is a device used to analyze data collected by drones and evaluate the condition of crops.

[0687] "Plant condition" refers to information indicating the health and growth progress of a crop.

[0688] An "emotion engine" is a system that recognizes and analyzes a user's emotional state based on their voice and facial expressions.

[0689] An "information provision device" is a device that transmits analysis results and emotion-based feedback to the user.

[0690] This invention is an AI-powered drone system aimed at improving agricultural efficiency and reducing stress. The system is designed to enable farmers to perform efficient tasks without increasing their workload through the coordinated interaction of the server, terminal, and user components.

[0691] The server processes various sensor data to generate flight routes and work plans. Specifically, it receives topographic information of farmland, crop data, and weather forecasts to determine the optimal work procedure. This uses cloud-based data analysis software and technology that updates the plan based on real-time changing environmental information.

[0692] The drone, acting as the terminal, receives instructions from the server and autonomously performs tasks along a designated route. The drone is equipped with high-precision cameras and sensors, which are used to monitor the health of the crops. The collected data is transmitted to the server in real time and used to continuously assess the crop's condition.

[0693] Users can interface with the system through a terminal or other information terminal and receive feedback tailored to their work status and emotional state. An emotion engine analyzes the user's voice and facial expressions to determine their stress and fatigue levels. Based on this, psychologically sensitive information is provided to the user.

[0694] As a concrete example, a farmer uses their smartphone to activate a drone and input farmland information. The user then prompts the AI ​​model with the message, "Please suggest an efficient flight route based on the farmland information." The AI ​​then quickly calculates the optimal route and suggests it to the user. This system provides a less burdensome farming environment, particularly for the elderly and newcomers.

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] The server receives topographic information of farmland, crop data, and weather forecasts as input from the user. This data is analyzed by a processing unit to generate information necessary for formulating flight routes and work plans. In this process, cloud-based data analysis software is used to calculate optimized work procedures based on the input data. The output is expressed as the optimal flight route and work plan.

[0698] Step 2:

[0699] The server transmits the generated flight route and work plan to the control unit. The drone receives this information as input and begins its work along the specified route. The drone can fly precisely along the designated path using high-precision GPS technology and built-in sensors. The output is the start of the drone's flight along the planned route.

[0700] Step 3:

[0701] The drone, acting as the terminal, collects crop data in real time during flight using its built-in camera and sensors. The collected data is transmitted from the terminal to a server. The input is raw data observed by various sensors, and the output is formatted crop condition data for evaluation.

[0702] Step 4:

[0703] The server analyzes crop data transmitted from drones using an analysis device to evaluate the health and growth progress of the plants. Machine learning algorithms are used for the analysis to detect anomalies and predict growth. The input is data from sensors, and the output is feedback on the health status as an evaluation result.

[0704] Step 5:

[0705] The user receives feedback on the condition of the evaluated crops through the device. The user interface uses a generative AI model and an emotion engine to analyze the user's voice and facial expressions and provide appropriate feedback messages. The input is the analysis results and the user's emotion data, and the output is a customized feedback message.

[0706] Step 6:

[0707] Based on this feedback, the user provides further instructions to the system, generating new prompts. For example, by entering "Recommend the timing of the next pesticide application" as a prompt, a new work plan or adjustments will be made. The output is the proposed work adjustments.

[0708] (Application Example 2)

[0709] 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".

[0710] In factory manufacturing processes, it is necessary to efficiently maintain and improve product quality while reducing stress and burden on workers. This requires a system that manages the entire manufacturing process while simultaneously incorporating flexible feedback that takes into account the workers' conditions.

[0711] 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 including an information processing device for receiving manufacturing information in a factory and formulating work routes and process plans based thereon; means including a control device that can communicate with the information processing device for instructing the operation of manufacturing equipment; and means including an emotion recognition device for recognizing the emotional state of workers and providing feedback corresponding to those emotions. This makes it possible to provide appropriate support to workers while maintaining product quality.

[0712] "Manufacturing information" refers to the materials, processes, schedules, and all related data used in the factory.

[0713] An "information processing device" refers to a computer system used to analyze manufacturing data and formulate optimal work routes and process plans.

[0714] "Control device" refers to a device that communicates with the aforementioned information processing device and issues operational instructions to the manufacturing device.

[0715] An "analysis device" refers to a device that evaluates the condition of a product based on collected data and makes decisions for maintaining quality.

[0716] A "display device" refers to a visual or audio output device used to communicate evaluation results to workers and to present revised plans for the next process.

[0717] An "emotion recognition device" refers to a system that analyzes a worker's voice, facial expressions, or other physiological data to identify their emotional state.

[0718] This invention is a system aimed at improving efficiency in manufacturing operations and providing mental support to workers. The system begins with a factory server receiving "manufacturing information" from each production line and using an "information processing device" to formulate optimal work routes and process plans. Based on this information, a "control device" issues precise operational instructions to each manufacturing device.

[0719] The "analysis device" uses data collected from the manufacturing equipment to evaluate the condition of the product and analyze possible defects and quality issues. The results are provided to the operator via the "display device," which helps in revising the plan for the next process.

[0720] Furthermore, the worker's "emotion recognition device" analyzes the worker's emotional state and provides feedback tailored to that emotion. For example, if the device determines that the workload is heavy, it can suggest relaxation techniques or adjust parts of the task.

[0721] This system is designed to reduce stress from long hours of work while maintaining worker productivity. For example, if a worker starts operating a new machine in a factory, an emotion recognition device could detect their anxiety and display specific instructions on what to do next, as well as reassuring feedback.

[0722] A concrete example is a situation where workers can check the current process progress and product status through smart glasses. If a worker feels tired, a message such as "It might be a good idea to take a short break" will appear on the screen, ultimately reducing the burden on the worker.

[0723] Examples of prompts for a generative AI model:

[0724] "How can I design a factory management app that monitors workers' emotional states in real time and provides appropriate feedback based on their location and the progress of the production line?"

[0725] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0726] Step 1:

[0727] The server receives manufacturing information from within the factory. This input includes material information, manufacturing processes, and planned schedules. The server sends this data to an information processing unit, which executes algorithms to formulate the optimal work routes and process plans. This process outputs a specific process plan designed to maximize the efficiency of the work line.

[0728] Step 2:

[0729] The terminal receives the process plan from the information processing unit, and the control unit transmits specific operational instructions to the manufacturing equipment. This input includes information on the operation details and timing for each process. Based on this, the control unit starts the manufacturing equipment and executes the planned process. As a result, each manufacturing line begins to operate efficiently.

[0730] Step 3:

[0731] The manufacturing equipment transmits data collected during operation to the server. This input includes information about product quality and any abnormal conditions. The server's analysis equipment receives this data and performs analysis for quality control. The output obtained at this stage is the result of the quality evaluation.

[0732] Step 4:

[0733] The user receives the analysis results via a display device. The evaluation results are fed back to be incorporated into the next process plan. This input includes quality evaluation results and recommended corrections. The user reviews them and instructs on the necessary adjustments for subsequent processes.

[0734] Step 5:

[0735] The terminal uses an emotion recognition device to detect the worker's voice and facial expressions, and sends this data to a server. Based on this data, the server performs emotion analysis and determines how to provide appropriate feedback. This output includes a specific message tailored to the worker's emotions.

[0736] Step 6:

[0737] Users receive feedback in real time through smart glasses. Messages output by the server are displayed to the user, and workers adjust their actions based on this feedback, improving work efficiency and mental health.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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."

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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 as being incorporated by reference.

[0759] The following is further disclosed regarding the embodiments described above.

[0760] (Claim 1)

[0761] A means including a processing device for receiving agricultural land information and formulating flight routes and work plans based thereon,

[0762] A means including a drone control device capable of communicating with the processing device for instructing the operation of the drone,

[0763] Means including an analytical device for analyzing data collected by a drone and evaluating the condition of crops,

[0764] A means including a display device for notifying agricultural workers of the evaluation results and for revising the next work plan,

[0765] A system that includes this.

[0766] (Claim 2)

[0767] The system according to claim 1, wherein the analysis device has a function to analyze crop growth and disease information and to determine the need for pesticide application or additional work based on the results.

[0768] (Claim 3)

[0769] The system according to claim 1, wherein the drone control device has a function to enable safe flight by adjusting altitude based on terrain information.

[0770] "Example 1"

[0771] (Claim 1)

[0772] A means including an information processing device for receiving agricultural land information and formulating an optimal flight path and work plan based on that information using an AI model,

[0773] A means including a drone controller for directing the drone's movements and controlling its flight safely and efficiently in real time,

[0774] A means including an analyzer for analyzing environmental data collected by drones and analyzing the health of crops using a generated AI model,

[0775] A means including a display that transmits the analysis results to agricultural workers and improves their next work schedule,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, wherein the analyzer has a function to analyze in detail information regarding the growth stage and abnormalities of crops and to determine whether or not pesticide application is necessary based on the results.

[0779] (Claim 3)

[0780] The system according to claim 1, wherein the drone controller has a function to automatically adjust its altitude using terrain information to enable safe navigation.

[0781] "Application Example 1"

[0782] (Claim 1)

[0783] A means including a processing device for receiving area information and formulating flight paths and operational plans based thereon,

[0784] Means including an unmanned aircraft control device capable of communicating with the processing device for instructing the operation of the unmanned aircraft,

[0785] Means including an analysis device for analyzing data collected by an unmanned aerial vehicle and evaluating the condition of an item,

[0786] A means including a display device for notifying workers of the evaluation results and for revising the next work plan,

[0787] As a reinforcement device, means including an augmented reality display device for workers equipped with visual assistance devices to monitor the work of the unmanned aircraft,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, wherein the analysis device has a function to analyze the organization and information of items and to determine the need for rearrangement or additional work based on the results.

[0791] (Claim 3)

[0792] The system according to claim 1, wherein the unmanned aircraft control device has a function to enable safe flight by adjusting the altitude based on spatial information.

[0793] "Example 2 of combining an emotion engine"

[0794] (Claim 1)

[0795] A means including a processing device for receiving agricultural land information and formulating flight routes and work plans based thereon,

[0796] A means including a control device that can communicate with the processing device for instructing the operation of the drone,

[0797] Means including an analytical device for analyzing data collected by a drone and evaluating the condition of plants,

[0798] Means including an output device for notifying workers of the evaluation results and modifying the next work plan,

[0799] A device including an emotion engine that analyzes the user's voice and facial expressions to determine their emotional state,

[0800] Means including an information providing device for providing feedback according to emotional state,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, wherein the analysis device has a function to analyze plant growth and disease information and to determine the need for pesticide application or additional work based on the results.

[0804] (Claim 3)

[0805] The system according to claim 1, wherein the control device has a function to ensure safe flight by adjusting altitude based on terrain information.

[0806] "Application example 2 when combining with an emotional engine"

[0807] (Claim 1)

[0808] A means including an information processing device for receiving manufacturing information in a factory and formulating work routes and process plans based on that information,

[0809] Means including a control device capable of communicating with the information processing device for instructing the operation of the manufacturing apparatus,

[0810] Means including an analysis device for analyzing data collected by manufacturing equipment and evaluating the condition of the product,

[0811] A means including a display device for notifying workers of the evaluation results and for modifying the next process plan,

[0812] Means including an emotion recognition device for recognizing the emotional state of a worker and providing feedback corresponding to that emotion,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the analysis device has a function to analyze product defects and quality information and to determine the need for corrective work or additional processes based on the results.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the control device has a function to ensure safe operation by adjusting the operating state based on work environment information. [Explanation of Symbols]

[0818] 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 including a processing device for receiving agricultural land information and formulating flight routes and work plans based thereon, A means including a drone control device capable of communicating with the processing device for instructing the operation of the drone, Means including an analytical device for analyzing data collected by a drone and evaluating the condition of crops, A means including a display device for notifying agricultural workers of the evaluation results and for revising the next work plan, A system that includes this.

2. The system according to claim 1, wherein the analysis device has a function to analyze crop growth and disease information and to determine the necessity of pesticide application or additional work based on the results.

3. The system according to claim 1, wherein the drone control device has a function to enable safe flight by adjusting altitude based on terrain information.

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