system

Abstract

We provide the system. [Solution] An aerial vehicle that automatically transports items to be delivered, A machine intelligence control means for optimizing the flight path of an aerial mobile object, A power mechanism mounted on an aerial vehicle for attaching the object to be delivered to the aerial vehicle, A user interaction tool for managing the receipt, departure, delivery, and confirmation of items to be delivered, Information processing means that calculates and notifies the delivery time based on order information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional delivery system, a problem is that a great deal of human labor and time are consumed. As a result, efficient delivery is difficult, and an issue is that delivery costs soar. Also, there is a demand for a method to minimize delivery delays, congestion, and delivery troubles due to the influence of the weather.

Means for Solving the Problems

[0005] This invention provides an automated transport system using an unmanned aerial vehicle (UAV). The UAV moves according to a flight path optimized by artificial intelligence control, and the transport object can be safely attached to the UAV by a power mechanism. Furthermore, the system notifies the user in real time about the receipt and delivery status of the transport object via a user interface. This streamlines the entire transport process, reducing human labor and time, and lowering costs.

[0006] An "unmanned aerial vehicle" is an aircraft that flies without a human on board, either remotely controlled or automatically controlled.

[0007] "Artificial intelligence control means" refers to a system equipped with a computer program that analyzes data and calculates the optimal flight path.

[0008] A "power mechanism" is a device used to perform operations such as mechanically grasping, lifting, or moving an object.

[0009] A "user interface means" is a software or hardware interface that allows a user to interact with a system and exchange information.

[0010] A "flight path" is the predicted aerial route that an unmanned aerial vehicle will take when traveling to its destination.

[0011] "Delivery items" refer to goods, mail, and other items transported by unmanned aerial vehicles. [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] This 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.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include 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 numbered 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 numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

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

[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] The system of the present invention performs automated delivery using unmanned aerial vehicles and is operated by combining artificial intelligence control means, power mechanism means, and user interface means.

[0034] The server receives order information from users through the application and plans the optimal flight path for that order. This planning uses artificial intelligence analysis based on real-time traffic information and weather conditions. As a result, the delivery process is made more efficient, reducing both time and costs.

[0035] The system's terminal performs maintenance on the unmanned aerial vehicle (UAV) and safely loads the delivered items using a robotic arm as the power mechanism. The robotic arm uses sensors to confirm the shape and weight of the delivered item, grasps it with the optimal force to match its shape, and places it on the UAV. This reduces human error and enhances the safety of delivery operations.

[0036] Users can manage their orders and check the delivery status in real time through a dedicated application. The user interface is intuitive and easy to use, allowing users to check their order details and delivery status at any time. For example, users can receive notifications when their delivery is approaching its destination, allowing them to plan and adjust their delivery timing accordingly.

[0037] This system integrates the necessary functions to provide fast and efficient delivery services and can be operated without relying on existing human resources. Because it operates autonomously in this way, it can further improve the efficiency of delivery operations.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server receives order information from the user through the application. The user selects the items they want delivered and enters the delivery address details. The server records this data in a database.

[0041] Step 2:

[0042] The server uses artificial intelligence to plan the optimal flight path based on the received order information. This process takes into account real-time data such as traffic information and weather conditions. Based on these results, it generates an efficient delivery schedule and prepares instructions for the unmanned aerial vehicle.

[0043] Step 3:

[0044] The terminal starts up the unmanned aerial vehicle and performs basic function checks. After checking the battery status and sensor operation status and confirming that the aircraft is functioning correctly, it resets the robotic arm to its initial settings and prepares it to safely grasp the delivery target.

[0045] Step 4:

[0046] The terminal uses a robotic arm to handle the items to be delivered and load them properly onto the unmanned aerial vehicle. Based on sensor information from the items, the robotic arm grasps the items with the appropriate force and secures them while adjusting the power mechanism.

[0047] Step 5:

[0048] Once all preparations are complete, the server sends an automated flight commencement command to the unmanned aerial vehicle (UAV). The UAV then proceeds to its destination according to the planned flight path.

[0049] Step 6:

[0050] Users can check the current location of the unmanned aerial vehicle and the progress of the delivery through the application. They will receive notifications from the server regarding the estimated arrival time of the delivery and ongoing updates.

[0051] Step 7:

[0052] The terminal confirms that the unmanned aerial vehicle has arrived at its delivery destination and issues instructions for a safe landing. Subsequently, a robotic arm handles the handover of the delivered items, completing the delivery. The user can confirm this handover and report the completion of receipt via the application.

[0053] (Example 1)

[0054] 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."

[0055] In automated transport systems, improving the efficiency and safety of transporting goods is a key challenge. In particular, there is a need to plan optimal routes using real-time traffic and weather data to achieve efficient transportation, while also ensuring the safe and reliable loading of goods onto aircraft.

[0056] 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.

[0057] In this invention, the server includes intelligent control means for optimizing the aircraft's movement path, power engine means for mounting the delivery goods onto the aircraft, and detection means for verifying the shape and weight of the delivery goods for safe loading. This enables efficient transportation and safe handling of the delivery goods.

[0058] "Delivered goods" refer to items that are transported automatically using aircraft.

[0059] An "aircraft" is an unmanned aerial vehicle used to transport goods in the air.

[0060] "Intelligent control means" refers to a technological means that uses artificial intelligence to analyze information and plan in order to optimize the flight path of an aircraft.

[0061] "Powering mechanism" refers to the mechanical mechanism used to securely attach goods to an aircraft.

[0062] The "operation interface means" is an interface that allows the user to operate the system and manage the receipt, dispatch, delivery, and confirmation of delivered goods.

[0063] "Detection means" refers to sensors and other measuring devices used to verify the shape and weight of the delivered goods.

[0064] This invention's system uses unmanned aerial vehicles to automatically transport goods. Specifically, it streamlines the delivery process through collaboration between a server, a terminal, and a user, ensuring safe and reliable transportation.

[0065] The server receives order information transmitted by the user and uses a generative AI model to plan the optimal flight path based on that information. This process utilizes services such as Google® Maps API and OpenWeatherMap API to collect and analyze real-time traffic and weather data. Furthermore, the generative AI model assists in path planning through the input of prompts. For example, prompts may include phrases like "Calculate the optimal route to deliver a package to the city center at night."

[0066] The terminal helps to safely load goods onto aircraft. Using sensor technology, it detects the shape and weight of the goods, and based on this data, a robotic arm, which acts as a power source, accurately grasps the items and places them on the aircraft. Control devices such as Arduino and Raspberry Pi manage the operation of the sensors and robotic arm.

[0067] Users can manage their orders and delivery process through a dedicated application. This application is implemented using Swift and ANDROID® Studio and features an interface that allows users to visually check the delivery status. Users can receive notifications when their delivery is approaching its destination, allowing them to prepare for receipt.

[0068] This system configuration allows for efficient and safe delivery using unmanned aerial vehicles, enabling operations with minimal human resources.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The server receives order information entered by the user through the application. This input data includes details of the items to be delivered, the destination, and the desired arrival time. The server stores this information in an internal database in preparation for the next processing step.

[0072] Step 2:

[0073] The server plans the flight path based on the received order information. In this step, real-time traffic and weather data is obtained from external APIs. Inputs include information from the Google Maps API and the OpenWeatherMap API. The prompt message "Calculate the optimal flight path" is input to the generating AI model, which analyzes and processes this data to output the optimal flight route.

[0074] Step 3:

[0075] The terminal begins preparing the shipment. The input data includes the shape and weight of the shipment. Sensors scan the shipment, and a robotic arm, the power source, grasps the item with the optimal force and loads it onto the aircraft. This ensures safe and secure handling of goods.

[0076] Step 4:

[0077] Users can check the delivery progress through the application. The server outputs the current location of the delivery and the estimated arrival time. Based on this, a notification is sent to the user's device. Users can use this information to prepare for receipt.

[0078] Step 5:

[0079] The server confirms that the delivery has reached its destination. It verifies the endpoint's arrival data and completes the final delivery step. A notification is sent to the user for receipt confirmation, and the process ends.

[0080] (Application Example 1)

[0081] 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."

[0082] Traditional delivery systems suffered from low delivery accuracy and efficiency, making it difficult to improve customer satisfaction. Furthermore, real-time status monitoring was challenging, making it difficult for customers to understand delivery schedules and progress. This could lead to inconveniences regarding delivery timing and potential delays during the delivery process.

[0083] 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.

[0084] In this invention, the server includes an aerial mobile unit that automatically transports the delivery target, machine intelligence control means for optimizing the aerial mobile unit's flight path, user interaction means for managing the receipt, departure, delivery, and confirmation of the delivery target, and information processing means for calculating and notifying the delivery time based on order information. This enables improved delivery accuracy and real-time monitoring of the delivery progress.

[0085] "Items to be delivered" refers to items or goods that are scheduled to be transported by an aerial vehicle.

[0086] "Airborne vehicles" is a general term for aircraft and equipment that fly through the air, move autonomously, and transport cargo.

[0087] "Machine intelligence control means" refers to an artificial intelligence system that automatically plans and adjusts shipping routes to improve delivery efficiency.

[0088] "Power mechanism means" refers to a mechanical device for safely and securely attaching and fixing the object to be delivered to an aerial moving body.

[0089] A "user interaction mechanism" is a system that provides an interface for users to check and manage the delivery status.

[0090] "Information processing means" refers to a computing device or program that calculates delivery times based on order information and provides accurate notifications to users.

[0091] This invention's system achieves efficient delivery using an aerial vehicle that automatically transports items to be delivered. The server receives order information from users through an application and uses machine intelligence to plan the optimal route based on that information. In this process, it collects real-time traffic and weather data and analyzes it using an AI algorithm. This makes it possible to generate the most efficient and safe route for the aerial vehicle.

[0092] The aerial vehicle is equipped with a power mechanism for safely transporting items. This mechanical device has the function of accurately gripping items and preventing them from falling during transport. Sensor technology is used to control the power mechanism, allowing it to sense the shape and weight of the items and handle them with the optimal amount of force.

[0093] Furthermore, the user interface, which serves as a means of user interaction, allows users to easily check the status of their orders and the progress of their deliveries. This makes it easier for users to adjust delivery times and plan their receipts, thereby improving convenience.

[0094] The server uses information processing tools to calculate delivery times based on order information and provides accurate notifications to users. This ensures that users receive their orders at the appropriate time, further improving user convenience. A concrete example of this system is when a user orders food delivery through an app; the AI ​​calculates the optimal delivery route and estimated arrival time, and informs the user of the progress in real time.

[0095] An example of a prompt to input into a generating AI model is: "Explain how an unmanned aerial vehicle food delivery system works. Pay particular attention to how the AI ​​plans the optimal delivery route and sends real-time notifications to users." This allows the AI ​​to properly interpret the system's operation and suggest effective operational methods.

[0096] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0097] Step 1:

[0098] The server receives order information from the user. It receives the user's order information (delivery address, product details, etc.) as input and uses this to initiate the delivery process. The server organizes and stores this information for subsequent processing.

[0099] Step 2:

[0100] The server collects real-time traffic and weather data. In this step, real-time traffic and weather data is obtained from publicly available APIs and databases. This data is used for route planning by machine intelligence control systems.

[0101] Step 3:

[0102] The server uses machine intelligence to calculate the optimal route based on the collected information. The AI ​​algorithm generates the shortest possible delivery route while avoiding traffic congestion and bad weather. It then generates efficient route data as output and transmits it to the airborne vehicle.

[0103] Step 4:

[0104] The aerial vehicle departs for its delivery target based on the received route data. Using a powered mechanism, it begins flight with the delivery target safely loaded. Sensor information is utilized to perform appropriate flight control.

[0105] Step 5:

[0106] The server monitors the delivery progress in real time and notifies the user of the progress. The server receives route data from the airborne vehicle, analyzes the progress, and then sends a push notification to the user's device. This allows the user to know the estimated arrival time of their delivery.

[0107] Step 6:

[0108] The user confirms receipt of the delivered item, and the delivery is complete. In this step, the user confirms receipt by tapping on the application on their device. The server records this information and terminates the entire delivery process.

[0109] 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.

[0110] The system of the present invention not only transports objects using unmanned aerial vehicles, but also has the function of recognizing the user's emotions and dynamically adjusting the delivery service. This system is realized by combining artificial intelligence control means, power mechanism means, user interface means, and emotion engine.

[0111] The server receives order information from the user and uses an emotion engine to analyze the user's emotional state. Emotion recognition uses voice tone, facial recognition data, and text input provided by the user's device. The results of this analysis are then used to adjust the delivery process based on the user's emotional state. For example, if a user is dissatisfied, the system can customize the delivery experience to meet the user's needs in order to provide the best possible delivery experience.

[0112] The terminal operates a robotic arm as a power mechanism mounted on an unmanned aerial vehicle, accurately handling the items to be delivered. Furthermore, based on data provided by an emotion engine, the terminal selects the appropriate delivery timing and method, and provides feedback to improve the user experience.

[0113] Through the application, users can check the real-time progress of their delivery and receive personalized service based on their emotional state. The emotion engine provides users with the information and responses they need in a timely manner, ensuring a comfortable delivery experience. For example, if a user is anxious about delivery delays due to bad weather, the system will provide an accurate arrival forecast and alternative options.

[0114] Thus, the present invention automatically provides a flexible delivery service that responds to the user's emotions and adapts to the situation. This makes it possible to move away from conventional one-sided delivery services and significantly improve user satisfaction.

[0115] The following describes the processing flow.

[0116] Step 1:

[0117] Users create orders and enter delivery preferences through the application. Simultaneously, the application collects data necessary for emotion recognition, such as the user's facial recognition data and voice input.

[0118] Step 2:

[0119] The server receives order information and sentiment data from the user. The sentiment engine analyzes this data to estimate the user's emotional state and devise the most suitable delivery method.

[0120] Step 3:

[0121] The server plans the optimal flight path and delivery schedule through AI control mechanisms, taking into account the analyzed emotional state. In this case, if the user is feeling anxious or stressed, a rapid response is prioritized.

[0122] Step 4:

[0123] The terminal prepares the unmanned aerial vehicle and uses a robotic arm to securely attach the delivery target to the aircraft. During this process, the delivery preparation is adjusted based on emotional data.

[0124] Step 5:

[0125] The server issues commands to the unmanned aerial vehicle to begin flight and to carry out deliveries along the planned route. It monitors the flight path and status in real time and provides users with timely, emotionally relevant information.

[0126] Step 6:

[0127] Users can check the delivery status through the application and update their emotional state if it changes. The system uses this information to adjust the content and timing of notifications sent to the user.

[0128] Step 7:

[0129] The terminal confirms that the unmanned aerial vehicle has arrived at its destination and handles the handover of the delivered goods. After the handover, the user's new emotional state is sent to the system as feedback to help improve the experience.

[0130] (Example 2)

[0131] 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 will be referred to as the "terminal."

[0132] Conventional delivery systems using unmanned aerial vehicles (UAVs) are limited to one-way delivery services and lack the flexibility to respond based on the user's emotional state, making improving user satisfaction a strategic challenge. Furthermore, route planning that only considers weather and traffic information makes it difficult to provide an optimal delivery experience tailored to individual user needs.

[0133] 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.

[0134] In this invention, the server includes an unmanned aerial vehicle (UAV) that automatically transports the delivery target, intelligent control means for optimizing the UAV's movement path, and power unit means mounted on the UAV for equipping the delivery target. This makes it possible to provide personalized service that takes into account the user's emotional state through emotion analysis, thereby realizing an optimal delivery service that improves user satisfaction.

[0135] "Delivery target" refers to the goods or packages to be transported, specifically the items to be transported by unmanned aerial vehicles.

[0136] An "unmanned aerial vehicle" is an aircraft that is operated remotely or automatically to transport items to their destination.

[0137] "Intelligent control means" refers to a control system that includes artificial intelligence technology used to optimize the flight path and operation of an unmanned aerial vehicle.

[0138] "Powered device means" refers to a device mounted on an unmanned aerial vehicle that includes a mechanism for equipping the delivery target and releasing or securing it at a predetermined location.

[0139] A "user connection means" is an interface for notifying users of the status of delivery services and enables the exchange of information between users and the system.

[0140] An "emotion analysis tool" is a system component that analyzes a user's voice, facial expressions, and text data to evaluate their emotional state.

[0141] "Information provision means" refers to a feedback system that provides users with notifications regarding the progress of delivery and based on their emotions.

[0142] This invention relates to a delivery system utilizing unmanned aerial vehicles, aiming to flexibly adjust services according to the emotional state of the user. The system is realized by combining an unmanned aerial vehicle, intelligent control means, power unit means, user connection means, emotion analysis means, and information provision means.

[0143] The server utilizes a generative AI model to analyze voice tone, facial recognition data, and text input acquired from users, functioning as a means of emotion analysis. In this process, the AI ​​model comprehensively analyzes the data to accurately grasp the user's emotional state, identifying emotions such as dissatisfaction or joy. For example, it estimates emotions based on specific keywords or emojis in the text, as well as voice tone, and adjusts the service accordingly.

[0144] The terminal controls the power unit mounted on the unmanned aerial vehicle, ensuring accurate handling of the delivery target. To achieve this, it employs an algorithm that measures location, weight, and size of the delivery target in real time and calculates the optimal handling method. The power unit is a crucial mechanism within the unmanned aerial vehicle, enabling stable delivery of the target target to its destination.

[0145] Users can check the delivery progress through the application and receive real-time information from the server, including delivery details and estimated arrival times. By receiving notifications based on sentiment analysis results, users can understand the situation with peace of mind, even in cases of delays due to bad weather, and take appropriate action.

[0146] For example, if a user orders a specific product and checks the progress through the application during the delivery process, and becomes anxious about delays, the sentiment analysis tool will identify that emotion. The system will then recalculate the estimated arrival date and suggest alternative delivery methods. This allows the user to use the service with peace of mind.

[0147] An example of a prompt might be, "Please tell me how to use the emotion engine to analyze the emotions based on the data the user provided when ordering a product, and how to improve the delivery process."

[0148] This makes it possible to significantly improve user satisfaction by providing a unique delivery method that reflects user emotions and optimizes the delivery experience.

[0149] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0150] Step 1:

[0151] The server receives order information from the user. This input consists of data such as product information and desired delivery time, which the user submits through the application. Based on this information, the server uses a generative AI model to analyze it and create a basic delivery plan by comparing it with the user's past order history and current delivery schedule. The output is the data for the initial delivery plan.

[0152] Step 2:

[0153] The server receives the user's voice tone, facial recognition data, and text input as input data, and analyzes their emotions through emotion analysis tools. Here, data calculations that combine voice data analysis, facial expression analysis, and text emotion analysis are performed to determine the emotion. The output is data indicating the user's emotional state.

[0154] Step 3:

[0155] The server customizes the delivery plan based on the user's emotional state analyzed in step 2. The inputs are emotional state data and the initial delivery plan. Using a generative AI model, the server optimizes the delivery according to the emotion, for example, selecting the shortest route and rearranging the processing steps for urgent requests, resulting in an optimized custom delivery plan as output.

[0156] Step 4:

[0157] The terminal controls the propulsion system of the unmanned aerial vehicle (UAV) to handle the delivery target based on an optimized delivery plan. The input is the optimized delivery plan, and the terminal utilizes real-time weather and location information to perform actions to ensure stable transport of the delivery target. This includes balanced lifting actions by the robotic arm and navigation operations to the destination. The output is the progress of the delivery.

[0158] Step 5:

[0159] Users receive delivery progress updates via the application. Based on the ongoing information and sentiment, the server provides real-time notifications and service adjustments as needed. For example, if delays are anticipated, it updates the estimated arrival time and offers alternative options. The output consists of detailed delivery information and recommendations sent to the user.

[0160] These steps enable the system to consider user emotions and provide an optimal delivery experience tailored to diverse needs.

[0161] (Application Example 2)

[0162] 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".

[0163] In recent years, delivery services using unmanned aerial vehicles have expanded, but conventional systems are unable to provide flexible services that respond to the emotions of users, resulting in challenges in user satisfaction. Therefore, it is necessary to dynamically adjust the delivery process based on the emotions of users to provide a more comfortable and optimized delivery experience.

[0164] 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.

[0165] In this invention, the server includes an aerial transport device for automatically transporting items to be delivered, an intelligent control device for optimizing the flight path of the aerial transport device, a drive mechanism for attaching items to be delivered to the aerial transport device, a user interaction interface device, and an emotion analysis mechanism for recognizing the user's emotions and dynamically adjusting the delivery service. This makes it possible to provide flexible delivery services according to the user's emotional state.

[0166] "Delivery target items" refer to items or goods that are transported by unmanned aerial transport devices.

[0167] "Air transport device" refers to an unmanned aerial vehicle used to automatically transport goods for delivery.

[0168] An "intelligent control system" is a control system equipped with artificial intelligence to optimize the flight path of an aerial transport device.

[0169] A "driving mechanical device" is a mechanical device that supplies power to attach or detach objects to be delivered to an aerial transport device.

[0170] A "user interaction interface device" is an interface that exchanges information with users to manage the receipt, departure, delivery, and confirmation of items to be delivered.

[0171] The "emotion analysis mechanism" is a system that recognizes the user's emotions and dynamically adjusts the delivery service based on those emotions.

[0172] A system specifically implementing this invention includes an aerial transport device, an intelligent control device, a drive mechanism, a user interaction interface device, and an emotion analysis mechanism.

[0173] The server first transports the items to be delivered via an aerial transport system. The intelligent control unit utilizes real-time traffic and weather data to optimize the transport system's flight path. At this time, an emotion analysis mechanism dynamically adjusts the delivery process based on the user's emotional information. The server performs emotion analysis using voice data, facial recognition data, and text data collected from the user's smartphone or other devices. This allows the server to adjust the delivery priority and method, providing the user with the best possible experience.

[0174] The terminal provides delivery progress and emotion-based feedback via a user interaction interface. Utilizing an emotion analysis mechanism, it appropriately offers anxiety reduction and specific countermeasures as requested by the user. For example, if delivery is delayed due to bad weather, the system can send a notification to the user, providing an accurate estimated arrival time and alternative options.

[0175] As a concrete example, consider a case where a user orders food delivery at night. In this case, the server analyzes the user's voice tone and the degree of urgency and anxiety from the entered text, and adjusts the route to ensure prompt delivery. It also makes suggestions about benefits and services to increase customer satisfaction.

[0176] An example of a prompt for a generative AI model is, "Use the user's voice and facial expressions to analyze their emotions and adjust the delivery method accordingly."

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The server receives voice data, facial recognition data, and text data from the user as input. The server sends this data to an emotion analysis mechanism to analyze the user's emotional state. As part of the data processing, tone analysis is performed on the voice data, facial features are extracted, and emotion analysis is performed on the text data, and the results are output as the emotional state.

[0180] Step 2:

[0181] After the server receives the user's emotional state, it uses an intelligent control unit to acquire real-time traffic information and weather data as input. The input environmental data and the user's emotional state are combined to perform data calculations to determine the optimal flight path and delivery method. The output of this process is the adjusted flight path and delivery priority.

[0182] Step 3:

[0183] The terminal receives the coordinated flight path and delivery priority transmitted from the server, and operates the drive mechanism to attach the delivery target to the aerial transport device. The terminal controls the necessary power during this process and prepares for delivery. Furthermore, this information is notified to the user via the user interaction interface device. As output, information confirming the start of delivery and its progress is displayed to the user.

[0184] Step 4:

[0185] The user receives updates on the delivery progress and suggestions based on their emotions through the device. If the user feels anxious during delivery, the device accepts accurate arrival predictions and alternative suggestions as input, processes this information using a generating AI model, and provides information to reassure the user as output.

[0186] Step 5:

[0187] Ultimately, the server collects feedback after delivery is complete, records the user's emotions and experience, and analyzes the data to improve the service next time. This feedback serves as foundational data for continuously optimizing the service and for proposing further recommendations using generative AI models.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Second Embodiment]

[0192] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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".

[0204] The system of the present invention performs automated delivery using unmanned aerial vehicles and is operated by combining artificial intelligence control means, power mechanism means, and user interface means.

[0205] The server receives order information from users through the application and plans the optimal flight path for that order. This planning uses artificial intelligence analysis based on real-time traffic information and weather conditions. As a result, the delivery process is made more efficient, reducing both time and costs.

[0206] The system's terminal performs maintenance on the unmanned aerial vehicle (UAV) and safely loads the delivered items using a robotic arm as the power mechanism. The robotic arm uses sensors to confirm the shape and weight of the delivered item, grasps it with the optimal force to match its shape, and places it on the UAV. This reduces human error and enhances the safety of delivery operations.

[0207] Users can manage their orders and check the delivery status in real time through a dedicated application. The user interface is intuitive and easy to use, allowing users to check their order details and delivery status at any time. For example, users can receive notifications when their delivery is approaching its destination, allowing them to plan and adjust their delivery timing accordingly.

[0208] This system integrates the necessary functions to provide fast and efficient delivery services and can be operated without relying on existing human resources. Because it operates autonomously in this way, it can further improve the efficiency of delivery operations.

[0209] The following describes the processing flow.

[0210] Step 1:

[0211] The server receives order information from the user through the application. The user selects the items they want delivered and enters the delivery address details. The server records this data in a database.

[0212] Step 2:

[0213] The server uses artificial intelligence to plan the optimal flight path based on the received order information. This process takes into account real-time data such as traffic information and weather conditions. Based on these results, it generates an efficient delivery schedule and prepares instructions for the unmanned aerial vehicle.

[0214] Step 3:

[0215] The terminal starts up the unmanned aerial vehicle and performs basic function checks. After checking the battery status and sensor operation status and confirming that the aircraft is functioning correctly, it resets the robotic arm to its initial settings and prepares it to safely grasp the delivery target.

[0216] Step 4:

[0217] The terminal uses a robotic arm to handle the items to be delivered and load them properly onto the unmanned aerial vehicle. Based on sensor information from the items, the robotic arm grasps the items with the appropriate force and secures them while adjusting the power mechanism.

[0218] Step 5:

[0219] Once all preparations are complete, the server sends an automated flight commencement command to the unmanned aerial vehicle (UAV). The UAV then proceeds to its destination according to the planned flight path.

[0220] Step 6:

[0221] Users can check the current location of the unmanned aerial vehicle and the progress of the delivery through the application. They will receive notifications from the server regarding the estimated arrival time of the delivery and ongoing updates.

[0222] Step 7:

[0223] The terminal confirms that the unmanned aerial vehicle has arrived at its delivery destination and issues instructions for a safe landing. Subsequently, a robotic arm handles the handover of the delivered items, completing the delivery. The user can confirm this handover and report the completion of receipt via the application.

[0224] (Example 1)

[0225] 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."

[0226] In automated transport systems, improving the efficiency and safety of transporting goods is a key challenge. In particular, there is a need to plan optimal routes using real-time traffic and weather data to achieve efficient transportation, while also ensuring the safe and reliable loading of goods onto aircraft.

[0227] 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.

[0228] In this invention, the server includes intelligent control means for optimizing the aircraft's movement path, power engine means for mounting the delivery goods onto the aircraft, and detection means for verifying the shape and weight of the delivery goods for safe loading. This enables efficient transportation and safe handling of the delivery goods.

[0229] "Delivered goods" refer to items that are transported automatically using aircraft.

[0230] An "aircraft" is an unmanned aerial vehicle used to transport goods in the air.

[0231] "Intelligent control means" refers to a technological means that uses artificial intelligence to analyze information and plan in order to optimize the flight path of an aircraft.

[0232] "Powering mechanism" refers to the mechanical mechanism used to securely attach goods to an aircraft.

[0233] The "operation interface means" is an interface that allows the user to operate the system and manage the receipt, dispatch, delivery, and confirmation of delivered goods.

[0234] "Detection means" refers to sensors and other measuring devices used to verify the shape and weight of the delivered goods.

[0235] This invention's system uses unmanned aerial vehicles to automatically transport goods. Specifically, it streamlines the delivery process through collaboration between a server, a terminal, and a user, ensuring safe and reliable transportation.

[0236] The server receives order information transmitted by the user and uses a generative AI model to plan the optimal flight path based on that information. This process utilizes services such as the Google Maps API and OpenWeatherMap API to collect and analyze real-time traffic and weather data. Furthermore, the generative AI model assists in path planning through the input of prompts. For example, prompts may include phrases like "Calculate the optimal route to deliver a package to the city center at night."

[0237] The terminal helps to safely load goods onto aircraft. Using sensor technology, it detects the shape and weight of the goods, and based on this data, a robotic arm, which acts as a power source, accurately grasps the items and places them on the aircraft. Control devices such as Arduino and Raspberry Pi manage the operation of the sensors and robotic arm.

[0238] Users can manage their orders and delivery process through a dedicated application. This application, implemented using Swift and Android Studio, features a visual interface for tracking delivery status. Users receive notifications when their delivery is approaching its destination, allowing them to prepare for receipt.

[0239] This system configuration allows for efficient and safe delivery using unmanned aerial vehicles, enabling operations with minimal human resources.

[0240] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0241] Step 1:

[0242] The server receives order information entered by the user through the application. This input data includes details of the items to be delivered, the destination, and the desired arrival time. The server stores this information in an internal database in preparation for the next processing step.

[0243] Step 2:

[0244] The server plans the flight path based on the received order information. In this step, real-time traffic and weather data is obtained from external APIs. Inputs include information from the Google Maps API and the OpenWeatherMap API. The prompt message "Calculate the optimal flight path" is input to the generating AI model, which analyzes and processes this data to output the optimal flight route.

[0245] Step 3:

[0246] The terminal begins preparing the shipment. The input data includes the shape and weight of the shipment. Sensors scan the shipment, and a robotic arm, the power source, grasps the item with the optimal force and loads it onto the aircraft. This ensures safe and secure handling of goods.

[0247] Step 4:

[0248] Users can check the delivery progress through the application. The server outputs the current location of the delivery and the estimated arrival time. Based on this, a notification is sent to the user's device. Users can use this information to prepare for receipt.

[0249] Step 5:

[0250] The server confirms that the delivery has reached its destination. It verifies the endpoint's arrival data and completes the final delivery step. A notification is sent to the user for receipt confirmation, and the process ends.

[0251] (Application Example 1)

[0252] 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."

[0253] Traditional delivery systems suffered from low delivery accuracy and efficiency, making it difficult to improve customer satisfaction. Furthermore, real-time status monitoring was challenging, making it difficult for customers to understand delivery schedules and progress. This could lead to inconveniences regarding delivery timing and potential delays during the delivery process.

[0254] 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.

[0255] In this invention, the server includes an aerial mobile unit that automatically transports the delivery target, machine intelligence control means for optimizing the aerial mobile unit's flight path, user interaction means for managing the receipt, departure, delivery, and confirmation of the delivery target, and information processing means for calculating and notifying the delivery time based on order information. This enables improved delivery accuracy and real-time monitoring of the delivery progress.

[0256] "Items to be delivered" refers to items or goods that are scheduled to be transported by an aerial vehicle.

[0257] "Airborne vehicles" is a general term for aircraft and equipment that fly through the air, move autonomously, and transport cargo.

[0258] "Machine intelligence control means" refers to an artificial intelligence system that automatically plans and adjusts shipping routes to improve delivery efficiency.

[0259] "Power mechanism means" refers to a mechanical device for safely and securely attaching and fixing the object to be delivered to an aerial moving body.

[0260] A "user interaction mechanism" is a system that provides an interface for users to check and manage the delivery status.

[0261] "Information processing means" refers to a computing device or program that calculates delivery times based on order information and provides accurate notifications to users.

[0262] This invention's system achieves efficient delivery using an aerial vehicle that automatically transports items to be delivered. The server receives order information from users through an application and uses machine intelligence to plan the optimal route based on that information. In this process, it collects real-time traffic and weather data and analyzes it using an AI algorithm. This makes it possible to generate the most efficient and safe route for the aerial vehicle.

[0263] The aerial vehicle is equipped with a power mechanism for safely transporting items. This mechanical device has the function of accurately gripping items and preventing them from falling during transport. Sensor technology is used to control the power mechanism, allowing it to sense the shape and weight of the items and handle them with the optimal amount of force.

[0264] Furthermore, the user interface, which serves as a means of user interaction, allows users to easily check the status of their orders and the progress of their deliveries. This makes it easier for users to adjust delivery times and plan their receipts, thereby improving convenience.

[0265] The server uses information processing tools to calculate delivery times based on order information and provides accurate notifications to users. This ensures that users receive their orders at the appropriate time, further improving user convenience. A concrete example of this system is when a user orders food delivery through an app; the AI ​​calculates the optimal delivery route and estimated arrival time, and informs the user of the progress in real time.

[0266] An example of a prompt to input into a generating AI model is: "Explain how an unmanned aerial vehicle food delivery system works. Pay particular attention to how the AI ​​plans the optimal delivery route and sends real-time notifications to users." This allows the AI ​​to properly interpret the system's operation and suggest effective operational methods.

[0267] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0268] Step 1:

[0269] The server receives order information from the user. It receives the user's order information (delivery address, product details, etc.) as input and uses this to initiate the delivery process. The server organizes and stores this information for subsequent processing.

[0270] Step 2:

[0271] The server collects real-time traffic and weather data. In this step, real-time traffic and weather data is obtained from publicly available APIs and databases. This data is used for route planning by machine intelligence control systems.

[0272] Step 3:

[0273] The server uses machine intelligence to calculate the optimal route based on the collected information. The AI ​​algorithm generates the shortest possible delivery route while avoiding traffic congestion and bad weather. It then generates efficient route data as output and transmits it to the airborne vehicle.

[0274] Step 4:

[0275] The aerial vehicle departs for its delivery target based on the received route data. Using a powered mechanism, it begins flight with the delivery target safely loaded. Sensor information is utilized to perform appropriate flight control.

[0276] Step 5:

[0277] The server monitors the delivery progress in real time and notifies the user of the progress. The server receives route data from the airborne vehicle, analyzes the progress, and then sends a push notification to the user's device. This allows the user to know the estimated arrival time of their delivery.

[0278] Step 6:

[0279] The user confirms receipt of the delivered item, and the delivery is complete. In this step, the user confirms receipt by tapping on the application on their device. The server records this information and terminates the entire delivery process.

[0280] 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.

[0281] The system of the present invention not only transports objects using unmanned aerial vehicles, but also has the function of recognizing the user's emotions and dynamically adjusting the delivery service. This system is realized by combining artificial intelligence control means, power mechanism means, user interface means, and emotion engine.

[0282] The server receives order information from the user and uses an emotion engine to analyze the user's emotional state. Emotion recognition uses voice tone, facial recognition data, and text input provided by the user's device. The results of this analysis are then used to adjust the delivery process based on the user's emotional state. For example, if a user is dissatisfied, the system can customize the delivery experience to meet the user's needs in order to provide the best possible delivery experience.

[0283] The terminal operates a robotic arm as a power mechanism means mounted on an unmanned aircraft to accurately handle the delivery target. Furthermore, the terminal selects an appropriate delivery timing and method based on the data provided by the emotion engine and provides feedback to improve the user's experience.

[0284] The user can check, through the application, the real-time progress of the delivery and the personalized service based on their emotional state. The emotion engine provides the information and responses required by the user in a timely manner to ensure a comfortable delivery experience. For example, when the user feels anxious about the delivery delay due to bad weather, the system presents an accurate arrival prediction and alternative solutions.

[0285] In this way, the present invention responds to the user's emotions and automatically provides a flexible delivery service according to the situation. Thus, it is possible to break away from the conventional one-sided delivery service and significantly improve the user's satisfaction.

[0286] The following describes the processing flow.

[0287] Step 1:

[0288] The user creates an order through the application and enters the delivery request. At the same time, the application collects the data necessary for emotion recognition, such as the user's face recognition data and voice input.

[0289] Step 2:

[0290] The server receives the order information and emotion data from the user. The emotion engine analyzes these data to estimate the user's emotional state and devises the most suitable method for the delivery process.

[0291] Step 3:

[0292] The server plans the optimal flight path and delivery schedule through AI control mechanisms, taking into account the analyzed emotional state. In this case, if the user is feeling anxious or stressed, a rapid response is prioritized.

[0293] Step 4:

[0294] The terminal prepares the unmanned aerial vehicle and uses a robotic arm to securely attach the delivery target to the aircraft. During this process, the delivery preparation is adjusted based on emotional data.

[0295] Step 5:

[0296] The server issues commands to the unmanned aerial vehicle to begin flight and to carry out deliveries along the planned route. It monitors the flight path and status in real time and provides users with timely, emotionally relevant information.

[0297] Step 6:

[0298] Users can check the delivery status through the application and update their emotional state if it changes. The system uses this information to adjust the content and timing of notifications sent to the user.

[0299] Step 7:

[0300] The terminal confirms that the unmanned aerial vehicle has arrived at its destination and handles the handover of the delivered goods. After the handover, the user's new emotional state is sent to the system as feedback to help improve the experience.

[0301] (Example 2)

[0302] 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".

[0303] In a conventional delivery system using unmanned aircraft, flexible response based on the emotional state of the user is not possible, and it remains a one-way delivery service. Improving user satisfaction is a strategic issue. In addition, it is difficult to provide an optimal delivery experience according to individual user needs with a route plan that only considers weather information and traffic information.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0305] In this invention, the server includes an unmanned aircraft that automatically transports the delivery target, intelligent control means for optimizing the movement route of the unmanned aircraft, and power device means mounted on the unmanned aircraft for equipping the delivery target. As a result, it becomes possible to provide individualized responses considering the emotional state of the user through emotion analysis, and an optimal delivery service that improves user satisfaction can be realized.

[0306] The "delivery target" refers to the articles and goods to be transported, which are the objects to be transported by the unmanned aircraft.

[0307] The "unmanned aircraft" is an aircraft that is remotely or automatically operated to transport the delivery target to its destination.

[0308] The "intelligent control means" is a control system including artificial intelligence technology used to optimize the flight route and operation of the unmanned aircraft.

[0309] The "power device means" is a device mounted on the unmanned aircraft, including a mechanism for equipping the delivery target and releasing or fixing it at a predetermined position.

[0310] The "user connection means" is an interface for notifying the user of the provision status of the delivery service, enabling the exchange of information between the user and the system.

[0311] An "emotion analysis tool" is a system component that analyzes a user's voice, facial expressions, and text data to evaluate their emotional state.

[0312] "Information provision means" refers to a feedback system that provides users with notifications regarding the progress of delivery and based on their emotions.

[0313] This invention relates to a delivery system utilizing unmanned aerial vehicles, aiming to flexibly adjust services according to the emotional state of the user. The system is realized by combining an unmanned aerial vehicle, intelligent control means, power unit means, user connection means, emotion analysis means, and information provision means.

[0314] The server utilizes a generative AI model to analyze voice tone, facial recognition data, and text input acquired from users, functioning as a means of emotion analysis. In this process, the AI ​​model comprehensively analyzes the data to accurately grasp the user's emotional state, identifying emotions such as dissatisfaction or joy. For example, it estimates emotions based on specific keywords or emojis in the text, as well as voice tone, and adjusts the service accordingly.

[0315] The terminal controls the power unit mounted on the unmanned aerial vehicle, ensuring accurate handling of the delivery target. To achieve this, it employs an algorithm that measures location, weight, and size of the delivery target in real time and calculates the optimal handling method. The power unit is a crucial mechanism within the unmanned aerial vehicle, enabling stable delivery of the target target to its destination.

[0316] Users can check the delivery progress through the application and receive real-time information from the server, including delivery details and estimated arrival times. By receiving notifications based on sentiment analysis results, users can understand the situation with peace of mind, even in cases of delays due to bad weather, and take appropriate action.

[0317] For example, if a user orders a specific product and checks the progress through the application during the delivery process, and becomes anxious about delays, the sentiment analysis tool will identify that emotion. The system will then recalculate the estimated arrival date and suggest alternative delivery methods. This allows the user to use the service with peace of mind.

[0318] An example of a prompt might be, "Please tell me how to use the emotion engine to analyze the emotions based on the data the user provided when ordering a product, and how to improve the delivery process."

[0319] This makes it possible to significantly improve user satisfaction by providing a unique delivery method that reflects user emotions and optimizes the delivery experience.

[0320] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0321] Step 1:

[0322] The server receives order information from the user. This input consists of data such as product information and desired delivery time, which the user submits through the application. Based on this information, the server uses a generative AI model to analyze it and create a basic delivery plan by comparing it with the user's past order history and current delivery schedule. The output is the data for the initial delivery plan.

[0323] Step 2:

[0324] The server receives the user's voice tone, facial recognition data, and text input as input data, and analyzes their emotions through emotion analysis tools. Here, data calculations that combine voice data analysis, facial expression analysis, and text emotion analysis are performed to determine the emotion. The output is data indicating the user's emotional state.

[0325] Step 3:

[0326] The server customizes the delivery plan based on the user's emotional state analyzed in step 2. The inputs are emotional state data and the initial delivery plan. Using a generative AI model, the server optimizes the delivery according to the emotion, for example, selecting the shortest route and rearranging the processing steps for urgent requests, resulting in an optimized custom delivery plan as output.

[0327] Step 4:

[0328] The terminal controls the propulsion system of the unmanned aerial vehicle (UAV) to handle the delivery target based on an optimized delivery plan. The input is the optimized delivery plan, and the terminal utilizes real-time weather and location information to perform actions to ensure stable transport of the delivery target. This includes balanced lifting actions by the robotic arm and navigation operations to the destination. The output is the progress of the delivery.

[0329] Step 5:

[0330] Users receive delivery progress updates via the application. Based on the ongoing information and sentiment, the server provides real-time notifications and service adjustments as needed. For example, if delays are anticipated, it updates the estimated arrival time and offers alternative options. The output consists of detailed delivery information and recommendations sent to the user.

[0331] These steps enable the system to consider user emotions and provide an optimal delivery experience tailored to diverse needs.

[0332] (Application Example 2)

[0333] 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."

[0334] In recent years, delivery services using unmanned aerial vehicles have expanded, but conventional systems are unable to provide flexible services that respond to the emotions of users, resulting in challenges in user satisfaction. Therefore, it is necessary to dynamically adjust the delivery process based on the emotions of users to provide a more comfortable and optimized delivery experience.

[0335] 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.

[0336] In this invention, the server includes an aerial transport device for automatically transporting items to be delivered, an intelligent control device for optimizing the flight path of the aerial transport device, a drive mechanism for attaching items to be delivered to the aerial transport device, a user interaction interface device, and an emotion analysis mechanism for recognizing the user's emotions and dynamically adjusting the delivery service. This makes it possible to provide flexible delivery services according to the user's emotional state.

[0337] "Delivery target items" refer to items or goods that are transported by unmanned aerial transport devices.

[0338] "Air transport device" refers to an unmanned aerial vehicle used to automatically transport goods for delivery.

[0339] An "intelligent control system" is a control system equipped with artificial intelligence to optimize the flight path of an aerial transport device.

[0340] A "driving mechanical device" is a mechanical device that supplies power to attach or detach objects to be delivered to an aerial transport device.

[0341] A "user interaction interface device" is an interface that exchanges information with users to manage the receipt, departure, delivery, and confirmation of items to be delivered.

[0342] The "emotion analysis mechanism" is a system that recognizes the user's emotions and dynamically adjusts the delivery service based on those emotions.

[0343] A system specifically implementing this invention includes an aerial transport device, an intelligent control device, a drive mechanism, a user interaction interface device, and an emotion analysis mechanism.

[0344] The server first transports the items to be delivered via an aerial transport system. The intelligent control unit utilizes real-time traffic and weather data to optimize the transport system's flight path. At this time, an emotion analysis mechanism dynamically adjusts the delivery process based on the user's emotional information. The server performs emotion analysis using voice data, facial recognition data, and text data collected from the user's smartphone or other devices. This allows the server to adjust the delivery priority and method, providing the user with the best possible experience.

[0345] The terminal provides delivery progress and emotion-based feedback via a user interaction interface. Utilizing an emotion analysis mechanism, it appropriately offers anxiety reduction and specific countermeasures as requested by the user. For example, if delivery is delayed due to bad weather, the system can send a notification to the user, providing an accurate estimated arrival time and alternative options.

[0346] As a concrete example, consider a case where a user orders food delivery at night. In this case, the server analyzes the user's voice tone and the degree of urgency and anxiety from the entered text, and adjusts the route to ensure prompt delivery. It also makes suggestions about benefits and services to increase customer satisfaction.

[0347] An example of a prompt for a generative AI model is, "Use the user's voice and facial expressions to analyze their emotions and adjust the delivery method accordingly."

[0348] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0349] Step 1:

[0350] The server receives voice data, facial recognition data, and text data from the user as input. The server sends this data to an emotion analysis mechanism to analyze the user's emotional state. As part of the data processing, tone analysis is performed on the voice data, facial features are extracted, and emotion analysis is performed on the text data, and the results are output as the emotional state.

[0351] Step 2:

[0352] After the server receives the user's emotional state, it uses an intelligent control unit to acquire real-time traffic information and weather data as input. The input environmental data and the user's emotional state are combined to perform data calculations to determine the optimal flight path and delivery method. The output of this process is the adjusted flight path and delivery priority.

[0353] Step 3:

[0354] The terminal receives the coordinated flight path and delivery priority transmitted from the server, and operates the drive mechanism to attach the delivery target to the aerial transport device. The terminal controls the necessary power during this process and prepares for delivery. Furthermore, this information is notified to the user via the user interaction interface device. As output, information confirming the start of delivery and its progress is displayed to the user.

[0355] Step 4:

[0356] The user receives updates on the delivery progress and suggestions based on their emotions through the device. If the user feels anxious during delivery, the device accepts accurate arrival predictions and alternative suggestions as input, processes this information using a generating AI model, and provides information to reassure the user as output.

[0357] Step 5:

[0358] Ultimately, the server collects feedback after delivery is complete, records the user's emotions and experience, and analyzes the data to improve the service next time. This feedback serves as foundational data for continuously optimizing the service and for proposing further recommendations using generative AI models.

[0359] 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.

[0360] 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.

[0361] 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.

[0362] [Third Embodiment]

[0363] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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).

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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".

[0375] The system of the present invention performs automated delivery using unmanned aerial vehicles and is operated by combining artificial intelligence control means, power mechanism means, and user interface means.

[0376] The server receives order information from users through the application and plans the optimal flight path for that order. This planning uses artificial intelligence analysis based on real-time traffic information and weather conditions. As a result, the delivery process is made more efficient, reducing both time and costs.

[0377] The system's terminal performs maintenance on the unmanned aerial vehicle (UAV) and safely loads the delivered items using a robotic arm as the power mechanism. The robotic arm uses sensors to confirm the shape and weight of the delivered item, grasps it with the optimal force to match its shape, and places it on the UAV. This reduces human error and enhances the safety of delivery operations.

[0378] Users can manage their orders and check the delivery status in real time through a dedicated application. The user interface is intuitive and easy to use, allowing users to check their order details and delivery status at any time. For example, users can receive notifications when their delivery is approaching its destination, allowing them to plan and adjust their delivery timing accordingly.

[0379] This system integrates the necessary functions to provide fast and efficient delivery services and can be operated without relying on existing human resources. Because it operates autonomously in this way, it can further improve the efficiency of delivery operations.

[0380] The following describes the processing flow.

[0381] Step 1:

[0382] The server receives order information from the user through the application. The user selects the items they want delivered and enters the delivery address details. The server records this data in a database.

[0383] Step 2:

[0384] The server uses artificial intelligence to plan the optimal flight path based on the received order information. This process takes into account real-time data such as traffic information and weather conditions. Based on these results, it generates an efficient delivery schedule and prepares instructions for the unmanned aerial vehicle.

[0385] Step 3:

[0386] The terminal starts up the unmanned aerial vehicle and performs basic function checks. After checking the battery status and sensor operation status and confirming that the aircraft is functioning correctly, it resets the robotic arm to its initial settings and prepares it to safely grasp the delivery target.

[0387] Step 4:

[0388] The terminal uses a robotic arm to handle the items to be delivered and load them properly onto the unmanned aerial vehicle. Based on sensor information from the items, the robotic arm grasps the items with the appropriate force and secures them while adjusting the power mechanism.

[0389] Step 5:

[0390] Once all preparations are complete, the server sends an automated flight commencement command to the unmanned aerial vehicle (UAV). The UAV then proceeds to its destination according to the planned flight path.

[0391] Step 6:

[0392] Users can check the current location of the unmanned aerial vehicle and the progress of the delivery through the application. They will receive notifications from the server regarding the estimated arrival time of the delivery and ongoing updates.

[0393] Step 7:

[0394] The terminal confirms that the unmanned aerial vehicle has arrived at its delivery destination and issues instructions for a safe landing. Subsequently, a robotic arm handles the handover of the delivered items, completing the delivery. The user can confirm this handover and report the completion of receipt via the application.

[0395] (Example 1)

[0396] 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."

[0397] In automated transport systems, improving the efficiency and safety of transporting goods is a key challenge. In particular, there is a need to plan optimal routes using real-time traffic and weather data to achieve efficient transportation, while also ensuring the safe and reliable loading of goods onto aircraft.

[0398] 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.

[0399] In this invention, the server includes intelligent control means for optimizing the aircraft's movement path, power engine means for mounting the delivery goods onto the aircraft, and detection means for verifying the shape and weight of the delivery goods for safe loading. This enables efficient transportation and safe handling of the delivery goods.

[0400] "Delivered goods" refer to items that are transported automatically using aircraft.

[0401] An "aircraft" is an unmanned aerial vehicle used to transport goods in the air.

[0402] "Intelligent control means" refers to a technological means that uses artificial intelligence to analyze information and plan in order to optimize the flight path of an aircraft.

[0403] "Powering mechanism" refers to the mechanical mechanism used to securely attach goods to an aircraft.

[0404] The "operation interface means" is an interface that allows the user to operate the system and manage the receipt, dispatch, delivery, and confirmation of delivered goods.

[0405] "Detection means" refers to sensors and other measuring devices used to verify the shape and weight of the delivered goods.

[0406] This invention's system uses unmanned aerial vehicles to automatically transport goods. Specifically, it streamlines the delivery process through collaboration between a server, a terminal, and a user, ensuring safe and reliable transportation.

[0407] The server receives order information transmitted by the user and uses a generative AI model to plan the optimal flight path based on that information. This process utilizes services such as the Google Maps API and OpenWeatherMap API to collect and analyze real-time traffic and weather data. Furthermore, the generative AI model assists in path planning through the input of prompts. For example, prompts may include phrases like "Calculate the optimal route to deliver a package to the city center at night."

[0408] The terminal helps to safely load goods onto aircraft. Using sensor technology, it detects the shape and weight of the goods, and based on this data, a robotic arm, which acts as a power source, accurately grasps the items and places them on the aircraft. Control devices such as Arduino and Raspberry Pi manage the operation of the sensors and robotic arm.

[0409] Users can manage their orders and delivery process through a dedicated application. This application, implemented using Swift and Android Studio, features a visual interface for tracking delivery status. Users receive notifications when their delivery is approaching its destination, allowing them to prepare for receipt.

[0410] This system configuration allows for efficient and safe delivery using unmanned aerial vehicles, enabling operations with minimal human resources.

[0411] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0412] Step 1:

[0413] The server receives order information entered by the user through the application. This input data includes details of the items to be delivered, the destination, and the desired arrival time. The server stores this information in an internal database in preparation for the next processing step.

[0414] Step 2:

[0415] The server plans the flight path based on the received order information. In this step, real-time traffic and weather data is obtained from external APIs. Inputs include information from the Google Maps API and the OpenWeatherMap API. The prompt message "Calculate the optimal flight path" is input to the generating AI model, which analyzes and processes this data to output the optimal flight route.

[0416] Step 3:

[0417] The terminal begins preparing the shipment. The input data includes the shape and weight of the shipment. Sensors scan the shipment, and a robotic arm, the power source, grasps the item with the optimal force and loads it onto the aircraft. This ensures safe and secure handling of goods.

[0418] Step 4:

[0419] Users can check the delivery progress through the application. The server outputs the current location of the delivery and the estimated arrival time. Based on this, a notification is sent to the user's device. Users can use this information to prepare for receipt.

[0420] Step 5:

[0421] The server confirms that the delivery has reached its destination. It verifies the endpoint's arrival data and completes the final delivery step. A notification is sent to the user for receipt confirmation, and the process ends.

[0422] (Application Example 1)

[0423] 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."

[0424] Traditional delivery systems suffered from low delivery accuracy and efficiency, making it difficult to improve customer satisfaction. Furthermore, real-time status monitoring was challenging, making it difficult for customers to understand delivery schedules and progress. This could lead to inconveniences regarding delivery timing and potential delays during the delivery process.

[0425] 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.

[0426] In this invention, the server includes an aerial mobile unit that automatically transports the delivery target, machine intelligence control means for optimizing the aerial mobile unit's flight path, user interaction means for managing the receipt, departure, delivery, and confirmation of the delivery target, and information processing means for calculating and notifying the delivery time based on order information. This enables improved delivery accuracy and real-time monitoring of the delivery progress.

[0427] "Items to be delivered" refers to items or goods that are scheduled to be transported by an aerial vehicle.

[0428] "Airborne vehicles" is a general term for aircraft and equipment that fly through the air, move autonomously, and transport cargo.

[0429] "Machine intelligence control means" refers to an artificial intelligence system that automatically plans and adjusts shipping routes to improve delivery efficiency.

[0430] "Power mechanism means" refers to a mechanical device for safely and securely attaching and fixing the object to be delivered to an aerial moving body.

[0431] A "user interaction mechanism" is a system that provides an interface for users to check and manage the delivery status.

[0432] "Information processing means" refers to a computing device or program that calculates delivery times based on order information and provides accurate notifications to users.

[0433] This invention's system achieves efficient delivery using an aerial vehicle that automatically transports items to be delivered. The server receives order information from users through an application and uses machine intelligence to plan the optimal route based on that information. In this process, it collects real-time traffic and weather data and analyzes it using an AI algorithm. This makes it possible to generate the most efficient and safe route for the aerial vehicle.

[0434] The aerial vehicle is equipped with a power mechanism for safely transporting items. This mechanical device has the function of accurately gripping items and preventing them from falling during transport. Sensor technology is used to control the power mechanism, allowing it to sense the shape and weight of the items and handle them with the optimal amount of force.

[0435] Furthermore, the user interface, which serves as a means of user interaction, allows users to easily check the status of their orders and the progress of their deliveries. This makes it easier for users to adjust delivery times and plan their receipts, thereby improving convenience.

[0436] The server uses information processing tools to calculate delivery times based on order information and provides accurate notifications to users. This ensures that users receive their orders at the appropriate time, further improving user convenience. A concrete example of this system is when a user orders food delivery through an app; the AI ​​calculates the optimal delivery route and estimated arrival time, and informs the user of the progress in real time.

[0437] An example of a prompt to input into a generating AI model is: "Explain how an unmanned aerial vehicle food delivery system works. Pay particular attention to how the AI ​​plans the optimal delivery route and sends real-time notifications to users." This allows the AI ​​to properly interpret the system's operation and suggest effective operational methods.

[0438] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0439] Step 1:

[0440] The server receives order information from the user. It receives the user's order information (delivery address, product details, etc.) as input and uses this to initiate the delivery process. The server organizes and stores this information for subsequent processing.

[0441] Step 2:

[0442] The server collects real-time traffic and weather data. In this step, real-time traffic and weather data is obtained from publicly available APIs and databases. This data is used for route planning by machine intelligence control systems.

[0443] Step 3:

[0444] The server uses machine intelligence to calculate the optimal route based on the collected information. The AI ​​algorithm generates the shortest possible delivery route while avoiding traffic congestion and bad weather. It then generates efficient route data as output and transmits it to the airborne vehicle.

[0445] Step 4:

[0446] The aerial vehicle departs for its delivery target based on the received route data. Using a powered mechanism, it begins flight with the delivery target safely loaded. Sensor information is utilized to perform appropriate flight control.

[0447] Step 5:

[0448] The server monitors the delivery progress in real time and notifies the user of the progress. The server receives route data from the airborne vehicle, analyzes the progress, and then sends a push notification to the user's device. This allows the user to know the estimated arrival time of their delivery.

[0449] Step 6:

[0450] The user confirms receipt of the delivered item, and the delivery is complete. In this step, the user confirms receipt by tapping on the application on their device. The server records this information and terminates the entire delivery process.

[0451] 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.

[0452] The system of the present invention not only transports objects using unmanned aerial vehicles, but also has the function of recognizing the user's emotions and dynamically adjusting the delivery service. This system is realized by combining artificial intelligence control means, power mechanism means, user interface means, and emotion engine.

[0453] The server receives order information from the user and uses an emotion engine to analyze the user's emotional state. Emotion recognition uses voice tone, facial recognition data, and text input provided by the user's device. The results of this analysis are then used to adjust the delivery process based on the user's emotional state. For example, if a user is dissatisfied, the system can customize the delivery experience to meet the user's needs in order to provide the best possible delivery experience.

[0454] The terminal operates a robotic arm as a power mechanism mounted on an unmanned aerial vehicle, accurately handling the items to be delivered. Furthermore, based on data provided by an emotion engine, the terminal selects the appropriate delivery timing and method, and provides feedback to improve the user experience.

[0455] Through the application, users can check the real-time progress of their delivery and receive personalized service based on their emotional state. The emotion engine provides users with the information and responses they need in a timely manner, ensuring a comfortable delivery experience. For example, if a user is anxious about delivery delays due to bad weather, the system will provide an accurate arrival forecast and alternative options.

[0456] Thus, the present invention automatically provides a flexible delivery service that responds to the user's emotions and adapts to the situation. This makes it possible to move away from conventional one-sided delivery services and significantly improve user satisfaction.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] Users create orders and enter delivery preferences through the application. Simultaneously, the application collects data necessary for emotion recognition, such as the user's facial recognition data and voice input.

[0460] Step 2:

[0461] The server receives order information and sentiment data from the user. The sentiment engine analyzes this data to estimate the user's emotional state and devise the most suitable delivery method.

[0462] Step 3:

[0463] The server plans the optimal flight path and delivery schedule through AI control mechanisms, taking into account the analyzed emotional state. In this case, if the user is feeling anxious or stressed, a rapid response is prioritized.

[0464] Step 4:

[0465] The terminal prepares the unmanned aerial vehicle and uses a robotic arm to securely attach the delivery target to the aircraft. During this process, the delivery preparation is adjusted based on emotional data.

[0466] Step 5:

[0467] The server issues commands to the unmanned aerial vehicle to begin flight and to carry out deliveries along the planned route. It monitors the flight path and status in real time and provides users with timely, emotionally relevant information.

[0468] Step 6:

[0469] Users can check the delivery status through the application and update their emotional state if it changes. The system uses this information to adjust the content and timing of notifications sent to the user.

[0470] Step 7:

[0471] The terminal confirms that the unmanned aerial vehicle has arrived at its destination and handles the handover of the delivered goods. After the handover, the user's new emotional state is sent to the system as feedback to help improve the experience.

[0472] (Example 2)

[0473] 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."

[0474] Conventional delivery systems using unmanned aerial vehicles (UAVs) are limited to one-way delivery services and lack the flexibility to respond based on the user's emotional state, making improving user satisfaction a strategic challenge. Furthermore, route planning that only considers weather and traffic information makes it difficult to provide an optimal delivery experience tailored to individual user needs.

[0475] 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.

[0476] In this invention, the server includes an unmanned aerial vehicle (UAV) that automatically transports the delivery target, intelligent control means for optimizing the UAV's movement path, and power unit means mounted on the UAV for equipping the delivery target. This makes it possible to provide personalized service that takes into account the user's emotional state through emotion analysis, thereby realizing an optimal delivery service that improves user satisfaction.

[0477] "Delivery target" refers to the goods or packages to be transported, specifically the items to be transported by unmanned aerial vehicles.

[0478] An "unmanned aerial vehicle" is an aircraft that is operated remotely or automatically to transport items to their destination.

[0479] "Intelligent control means" refers to a control system that includes artificial intelligence technology used to optimize the flight path and operation of an unmanned aerial vehicle.

[0480] "Powered device means" refers to a device mounted on an unmanned aerial vehicle that includes a mechanism for equipping the delivery target and releasing or securing it at a predetermined location.

[0481] A "user connection means" is an interface for notifying users of the status of delivery services and enables the exchange of information between users and the system.

[0482] An "emotion analysis tool" is a system component that analyzes a user's voice, facial expressions, and text data to evaluate their emotional state.

[0483] "Information provision means" refers to a feedback system that provides users with notifications regarding the progress of delivery and based on their emotions.

[0484] This invention relates to a delivery system utilizing unmanned aerial vehicles, aiming to flexibly adjust services according to the emotional state of the user. The system is realized by combining an unmanned aerial vehicle, intelligent control means, power unit means, user connection means, emotion analysis means, and information provision means.

[0485] The server utilizes a generative AI model to analyze voice tone, facial recognition data, and text input acquired from users, functioning as a means of emotion analysis. In this process, the AI ​​model comprehensively analyzes the data to accurately grasp the user's emotional state, identifying emotions such as dissatisfaction or joy. For example, it estimates emotions based on specific keywords or emojis in the text, as well as voice tone, and adjusts the service accordingly.

[0486] The terminal controls the power unit mounted on the unmanned aerial vehicle, ensuring accurate handling of the delivery target. To achieve this, it employs an algorithm that measures location, weight, and size of the delivery target in real time and calculates the optimal handling method. The power unit is a crucial mechanism within the unmanned aerial vehicle, enabling stable delivery of the target target to its destination.

[0487] Users can check the delivery progress through the application and receive real-time information from the server, including delivery details and estimated arrival times. By receiving notifications based on sentiment analysis results, users can understand the situation with peace of mind, even in cases of delays due to bad weather, and take appropriate action.

[0488] For example, if a user orders a specific product and checks the progress through the application during the delivery process, and becomes anxious about delays, the sentiment analysis tool will identify that emotion. The system will then recalculate the estimated arrival date and suggest alternative delivery methods. This allows the user to use the service with peace of mind.

[0489] An example of a prompt might be, "Please tell me how to use the emotion engine to analyze the emotions based on the data the user provided when ordering a product, and how to improve the delivery process."

[0490] This makes it possible to significantly improve user satisfaction by providing a unique delivery method that reflects user emotions and optimizes the delivery experience.

[0491] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0492] Step 1:

[0493] The server receives order information from the user. This input consists of data such as product information and desired delivery time, which the user submits through the application. Based on this information, the server uses a generative AI model to analyze it and create a basic delivery plan by comparing it with the user's past order history and current delivery schedule. The output is the data for the initial delivery plan.

[0494] Step 2:

[0495] The server receives the user's voice tone, facial recognition data, and text input as input data, and analyzes their emotions through emotion analysis tools. Here, data calculations that combine voice data analysis, facial expression analysis, and text emotion analysis are performed to determine the emotion. The output is data indicating the user's emotional state.

[0496] Step 3:

[0497] The server customizes the delivery plan based on the user's emotional state analyzed in step 2. The inputs are emotional state data and the initial delivery plan. Using a generative AI model, the server optimizes the delivery according to the emotion, for example, selecting the shortest route and rearranging the processing steps for urgent requests, resulting in an optimized custom delivery plan as output.

[0498] Step 4:

[0499] The terminal controls the propulsion system of the unmanned aerial vehicle (UAV) to handle the delivery target based on an optimized delivery plan. The input is the optimized delivery plan, and the terminal utilizes real-time weather and location information to perform actions to ensure stable transport of the delivery target. This includes balanced lifting actions by the robotic arm and navigation operations to the destination. The output is the progress of the delivery.

[0500] Step 5:

[0501] Users receive delivery progress updates via the application. Based on the ongoing information and sentiment, the server provides real-time notifications and service adjustments as needed. For example, if delays are anticipated, it updates the estimated arrival time and offers alternative options. The output consists of detailed delivery information and recommendations sent to the user.

[0502] These steps enable the system to consider user emotions and provide an optimal delivery experience tailored to diverse needs.

[0503] (Application Example 2)

[0504] 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."

[0505] In recent years, delivery services using unmanned aerial vehicles have expanded, but conventional systems are unable to provide flexible services that respond to the emotions of users, resulting in challenges in user satisfaction. Therefore, it is necessary to dynamically adjust the delivery process based on the emotions of users to provide a more comfortable and optimized delivery experience.

[0506] 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.

[0507] In this invention, the server includes an aerial transport device for automatically transporting items to be delivered, an intelligent control device for optimizing the flight path of the aerial transport device, a drive mechanism for attaching items to be delivered to the aerial transport device, a user interaction interface device, and an emotion analysis mechanism for recognizing the user's emotions and dynamically adjusting the delivery service. This makes it possible to provide flexible delivery services according to the user's emotional state.

[0508] "Delivery target items" refer to items or goods that are transported by unmanned aerial transport devices.

[0509] "Air transport device" refers to an unmanned aerial vehicle used to automatically transport goods for delivery.

[0510] An "intelligent control system" is a control system equipped with artificial intelligence to optimize the flight path of an aerial transport device.

[0511] A "driving mechanical device" is a mechanical device that supplies power to attach or detach objects to be delivered to an aerial transport device.

[0512] A "user interaction interface device" is an interface that exchanges information with users to manage the receipt, departure, delivery, and confirmation of items to be delivered.

[0513] The "emotion analysis mechanism" is a system that recognizes the user's emotions and dynamically adjusts the delivery service based on those emotions.

[0514] A system specifically implementing this invention includes an aerial transport device, an intelligent control device, a drive mechanism, a user interaction interface device, and an emotion analysis mechanism.

[0515] The server first transports the items to be delivered via an aerial transport system. The intelligent control unit utilizes real-time traffic and weather data to optimize the transport system's flight path. At this time, an emotion analysis mechanism dynamically adjusts the delivery process based on the user's emotional information. The server performs emotion analysis using voice data, facial recognition data, and text data collected from the user's smartphone or other devices. This allows the server to adjust the delivery priority and method, providing the user with the best possible experience.

[0516] The terminal provides delivery progress and emotion-based feedback via a user interaction interface. Utilizing an emotion analysis mechanism, it appropriately offers anxiety reduction and specific countermeasures as requested by the user. For example, if delivery is delayed due to bad weather, the system can send a notification to the user, providing an accurate estimated arrival time and alternative options.

[0517] As a concrete example, consider a case where a user orders food delivery at night. In this case, the server analyzes the user's voice tone and the degree of urgency and anxiety from the entered text, and adjusts the route to ensure prompt delivery. It also makes suggestions about benefits and services to increase customer satisfaction.

[0518] An example of a prompt for a generative AI model is, "Use the user's voice and facial expressions to analyze their emotions and adjust the delivery method accordingly."

[0519] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0520] Step 1:

[0521] The server receives voice data, facial recognition data, and text data from the user as input. The server sends this data to an emotion analysis mechanism to analyze the user's emotional state. As part of the data processing, tone analysis is performed on the voice data, facial features are extracted, and emotion analysis is performed on the text data, and the results are output as the emotional state.

[0522] Step 2:

[0523] After the server receives the user's emotional state, it uses an intelligent control unit to acquire real-time traffic information and weather data as input. The input environmental data and the user's emotional state are combined to perform data calculations to determine the optimal flight path and delivery method. The output of this process is the adjusted flight path and delivery priority.

[0524] Step 3:

[0525] The terminal receives the coordinated flight path and delivery priority transmitted from the server, and operates the drive mechanism to attach the delivery target to the aerial transport device. The terminal controls the necessary power during this process and prepares for delivery. Furthermore, this information is notified to the user via the user interaction interface device. As output, information confirming the start of delivery and its progress is displayed to the user.

[0526] Step 4:

[0527] The user receives updates on the delivery progress and suggestions based on their emotions through the device. If the user feels anxious during delivery, the device accepts accurate arrival predictions and alternative suggestions as input, processes this information using a generating AI model, and provides information to reassure the user as output.

[0528] Step 5:

[0529] Ultimately, the server collects feedback after delivery is complete, records the user's emotions and experience, and analyzes the data to improve the service next time. This feedback serves as foundational data for continuously optimizing the service and for proposing further recommendations using generative AI models.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] [Fourth Embodiment]

[0534] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0535] 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.

[0536] 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).

[0537] 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.

[0538] 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.

[0539] 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).

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] 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".

[0547] The system of the present invention performs automated delivery using unmanned aerial vehicles and is operated by combining artificial intelligence control means, power mechanism means, and user interface means.

[0548] The server receives order information from users through the application and plans the optimal flight path for that order. This planning uses artificial intelligence analysis based on real-time traffic information and weather conditions. As a result, the delivery process is made more efficient, reducing both time and costs.

[0549] The system's terminal performs maintenance on the unmanned aerial vehicle (UAV) and safely loads the delivered items using a robotic arm as the power mechanism. The robotic arm uses sensors to confirm the shape and weight of the delivered item, grasps it with the optimal force to match its shape, and places it on the UAV. This reduces human error and enhances the safety of delivery operations.

[0550] Users can manage their orders and check the delivery status in real time through a dedicated application. The user interface is intuitive and easy to use, allowing users to check their order details and delivery status at any time. For example, users can receive notifications when their delivery is approaching its destination, allowing them to plan and adjust their delivery timing accordingly.

[0551] This system integrates the necessary functions to provide fast and efficient delivery services and can be operated without relying on existing human resources. Because it operates autonomously in this way, it can further improve the efficiency of delivery operations.

[0552] The following describes the processing flow.

[0553] Step 1:

[0554] The server receives order information from the user through the application. The user selects the items they want delivered and enters the delivery address details. The server records this data in a database.

[0555] Step 2:

[0556] The server uses artificial intelligence to plan the optimal flight path based on the received order information. This process takes into account real-time data such as traffic information and weather conditions. Based on these results, it generates an efficient delivery schedule and prepares instructions for the unmanned aerial vehicle.

[0557] Step 3:

[0558] The terminal starts up the unmanned aerial vehicle and performs basic function checks. After checking the battery status and sensor operation status and confirming that the aircraft is functioning correctly, it resets the robotic arm to its initial settings and prepares it to safely grasp the delivery target.

[0559] Step 4:

[0560] The terminal uses a robotic arm to handle the items to be delivered and load them properly onto the unmanned aerial vehicle. Based on sensor information from the items, the robotic arm grasps the items with the appropriate force and secures them while adjusting the power mechanism.

[0561] Step 5:

[0562] Once all preparations are complete, the server sends an automated flight commencement command to the unmanned aerial vehicle (UAV). The UAV then proceeds to its destination according to the planned flight path.

[0563] Step 6:

[0564] Users can check the current location of the unmanned aerial vehicle and the progress of the delivery through the application. They will receive notifications from the server regarding the estimated arrival time of the delivery and ongoing updates.

[0565] Step 7:

[0566] The terminal confirms that the unmanned aerial vehicle has arrived at its delivery destination and issues instructions for a safe landing. Subsequently, a robotic arm handles the handover of the delivered items, completing the delivery. The user can confirm this handover and report the completion of receipt via the application.

[0567] (Example 1)

[0568] 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".

[0569] In automated transport systems, improving the efficiency and safety of transporting goods is a key challenge. In particular, there is a need to plan optimal routes using real-time traffic and weather data to achieve efficient transportation, while also ensuring the safe and reliable loading of goods onto aircraft.

[0570] 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.

[0571] In this invention, the server includes intelligent control means for optimizing the aircraft's movement path, power engine means for mounting the delivery goods onto the aircraft, and detection means for verifying the shape and weight of the delivery goods for safe loading. This enables efficient transportation and safe handling of the delivery goods.

[0572] "Delivered goods" refer to items that are transported automatically using aircraft.

[0573] An "aircraft" is an unmanned aerial vehicle used to transport goods in the air.

[0574] "Intelligent control means" refers to a technological means that uses artificial intelligence to analyze information and plan in order to optimize the flight path of an aircraft.

[0575] "Powering mechanism" refers to the mechanical mechanism used to securely attach goods to an aircraft.

[0576] The "operation interface means" is an interface that allows the user to operate the system and manage the receipt, dispatch, delivery, and confirmation of delivered goods.

[0577] "Detection means" refers to sensors and other measuring devices used to verify the shape and weight of the delivered goods.

[0578] This invention's system uses unmanned aerial vehicles to automatically transport goods. Specifically, it streamlines the delivery process through collaboration between a server, a terminal, and a user, ensuring safe and reliable transportation.

[0579] The server receives order information transmitted by the user and uses a generative AI model to plan the optimal flight path based on that information. This process utilizes services such as the Google Maps API and OpenWeatherMap API to collect and analyze real-time traffic and weather data. Furthermore, the generative AI model assists in path planning through the input of prompts. For example, prompts may include phrases like "Calculate the optimal route to deliver a package to the city center at night."

[0580] The terminal helps to safely load goods onto aircraft. Using sensor technology, it detects the shape and weight of the goods, and based on this data, a robotic arm, which acts as a power source, accurately grasps the items and places them on the aircraft. Control devices such as Arduino and Raspberry Pi manage the operation of the sensors and robotic arm.

[0581] Users can manage their orders and delivery process through a dedicated application. This application, implemented using Swift and Android Studio, features a visual interface for tracking delivery status. Users receive notifications when their delivery is approaching its destination, allowing them to prepare for receipt.

[0582] This system configuration allows for efficient and safe delivery using unmanned aerial vehicles, enabling operations with minimal human resources.

[0583] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0584] Step 1:

[0585] The server receives order information entered by the user through the application. This input data includes details of the items to be delivered, the destination, and the desired arrival time. The server stores this information in an internal database in preparation for the next processing step.

[0586] Step 2:

[0587] The server plans the flight path based on the received order information. In this step, real-time traffic and weather data is obtained from external APIs. Inputs include information from the Google Maps API and the OpenWeatherMap API. The prompt message "Calculate the optimal flight path" is input to the generating AI model, which analyzes and processes this data to output the optimal flight route.

[0588] Step 3:

[0589] The terminal begins preparing the shipment. The input data includes the shape and weight of the shipment. Sensors scan the shipment, and a robotic arm, the power source, grasps the item with the optimal force and loads it onto the aircraft. This ensures safe and secure handling of goods.

[0590] Step 4:

[0591] Users can check the delivery progress through the application. The server outputs the current location of the delivery and the estimated arrival time. Based on this, a notification is sent to the user's device. Users can use this information to prepare for receipt.

[0592] Step 5:

[0593] The server confirms that the delivery has reached its destination. It verifies the endpoint's arrival data and completes the final delivery step. A notification is sent to the user for receipt confirmation, and the process ends.

[0594] (Application Example 1)

[0595] 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".

[0596] Traditional delivery systems suffered from low delivery accuracy and efficiency, making it difficult to improve customer satisfaction. Furthermore, real-time status monitoring was challenging, making it difficult for customers to understand delivery schedules and progress. This could lead to inconveniences regarding delivery timing and potential delays during the delivery process.

[0597] 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.

[0598] In this invention, the server includes an aerial mobile unit that automatically transports the delivery target, machine intelligence control means for optimizing the aerial mobile unit's flight path, user interaction means for managing the receipt, departure, delivery, and confirmation of the delivery target, and information processing means for calculating and notifying the delivery time based on order information. This enables improved delivery accuracy and real-time monitoring of the delivery progress.

[0599] "Items to be delivered" refers to items or goods that are scheduled to be transported by an aerial vehicle.

[0600] "Airborne vehicles" is a general term for aircraft and equipment that fly through the air, move autonomously, and transport cargo.

[0601] "Machine intelligence control means" refers to an artificial intelligence system that automatically plans and adjusts shipping routes to improve delivery efficiency.

[0602] "Power mechanism means" refers to a mechanical device for safely and securely attaching and fixing the object to be delivered to an aerial moving body.

[0603] A "user interaction mechanism" is a system that provides an interface for users to check and manage the delivery status.

[0604] "Information processing means" refers to a computing device or program that calculates delivery times based on order information and provides accurate notifications to users.

[0605] This invention's system achieves efficient delivery using an aerial vehicle that automatically transports items to be delivered. The server receives order information from users through an application and uses machine intelligence to plan the optimal route based on that information. In this process, it collects real-time traffic and weather data and analyzes it using an AI algorithm. This makes it possible to generate the most efficient and safe route for the aerial vehicle.

[0606] The aerial vehicle is equipped with a power mechanism for safely transporting items. This mechanical device has the function of accurately gripping items and preventing them from falling during transport. Sensor technology is used to control the power mechanism, allowing it to sense the shape and weight of the items and handle them with the optimal amount of force.

[0607] Furthermore, the user interface, which serves as a means of user interaction, allows users to easily check the status of their orders and the progress of their deliveries. This makes it easier for users to adjust delivery times and plan their receipts, thereby improving convenience.

[0608] The server uses information processing tools to calculate delivery times based on order information and provides accurate notifications to users. This ensures that users receive their orders at the appropriate time, further improving user convenience. A concrete example of this system is when a user orders food delivery through an app; the AI ​​calculates the optimal delivery route and estimated arrival time, and informs the user of the progress in real time.

[0609] An example of a prompt to input into a generating AI model is: "Explain how an unmanned aerial vehicle food delivery system works. Pay particular attention to how the AI ​​plans the optimal delivery route and sends real-time notifications to users." This allows the AI ​​to properly interpret the system's operation and suggest effective operational methods.

[0610] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0611] Step 1:

[0612] The server receives order information from the user. It receives the user's order information (delivery address, product details, etc.) as input and uses this to initiate the delivery process. The server organizes and stores this information for subsequent processing.

[0613] Step 2:

[0614] The server collects real-time traffic and weather data. In this step, real-time traffic and weather data is obtained from publicly available APIs and databases. This data is used for route planning by machine intelligence control systems.

[0615] Step 3:

[0616] The server uses machine intelligence to calculate the optimal route based on the collected information. The AI ​​algorithm generates the shortest possible delivery route while avoiding traffic congestion and bad weather. It then generates efficient route data as output and transmits it to the airborne vehicle.

[0617] Step 4:

[0618] The aerial vehicle departs for its delivery target based on the received route data. Using a powered mechanism, it begins flight with the delivery target safely loaded. Sensor information is utilized to perform appropriate flight control.

[0619] Step 5:

[0620] The server monitors the delivery progress in real time and notifies the user of the progress. The server receives route data from the airborne vehicle, analyzes the progress, and then sends a push notification to the user's device. This allows the user to know the estimated arrival time of their delivery.

[0621] Step 6:

[0622] The user confirms receipt of the delivered item, and the delivery is complete. In this step, the user confirms receipt by tapping on the application on their device. The server records this information and terminates the entire delivery process.

[0623] 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.

[0624] The system of the present invention not only transports objects using unmanned aerial vehicles, but also has the function of recognizing the user's emotions and dynamically adjusting the delivery service. This system is realized by combining artificial intelligence control means, power mechanism means, user interface means, and emotion engine.

[0625] The server receives order information from the user and uses an emotion engine to analyze the user's emotional state. Emotion recognition uses voice tone, facial recognition data, and text input provided by the user's device. The results of this analysis are then used to adjust the delivery process based on the user's emotional state. For example, if a user is dissatisfied, the system can customize the delivery experience to meet the user's needs in order to provide the best possible delivery experience.

[0626] The terminal operates a robotic arm as a power mechanism mounted on an unmanned aerial vehicle, accurately handling the items to be delivered. Furthermore, based on data provided by an emotion engine, the terminal selects the appropriate delivery timing and method, and provides feedback to improve the user experience.

[0627] Through the application, users can check the real-time progress of their delivery and receive personalized service based on their emotional state. The emotion engine provides users with the information and responses they need in a timely manner, ensuring a comfortable delivery experience. For example, if a user is anxious about delivery delays due to bad weather, the system will provide an accurate arrival forecast and alternative options.

[0628] Thus, the present invention automatically provides a flexible delivery service that responds to the user's emotions and adapts to the situation. This makes it possible to move away from conventional one-sided delivery services and significantly improve user satisfaction.

[0629] The following describes the processing flow.

[0630] Step 1:

[0631] Users create orders and enter delivery preferences through the application. Simultaneously, the application collects data necessary for emotion recognition, such as the user's facial recognition data and voice input.

[0632] Step 2:

[0633] The server receives order information and sentiment data from the user. The sentiment engine analyzes this data to estimate the user's emotional state and devise the most suitable delivery method.

[0634] Step 3:

[0635] The server plans the optimal flight path and delivery schedule through AI control mechanisms, taking into account the analyzed emotional state. In this case, if the user is feeling anxious or stressed, a rapid response is prioritized.

[0636] Step 4:

[0637] The terminal prepares the unmanned aerial vehicle and uses a robotic arm to securely attach the delivery target to the aircraft. During this process, the delivery preparation is adjusted based on emotional data.

[0638] Step 5:

[0639] The server issues commands to the unmanned aerial vehicle to begin flight and to carry out deliveries along the planned route. It monitors the flight path and status in real time and provides users with timely, emotionally relevant information.

[0640] Step 6:

[0641] Users can check the delivery status through the application and update their emotional state if it changes. The system uses this information to adjust the content and timing of notifications sent to the user.

[0642] Step 7:

[0643] The terminal confirms that the unmanned aerial vehicle has arrived at its destination and handles the handover of the delivered goods. After the handover, the user's new emotional state is sent to the system as feedback to help improve the experience.

[0644] (Example 2)

[0645] 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".

[0646] Conventional delivery systems using unmanned aerial vehicles (UAVs) are limited to one-way delivery services and lack the flexibility to respond based on the user's emotional state, making improving user satisfaction a strategic challenge. Furthermore, route planning that only considers weather and traffic information makes it difficult to provide an optimal delivery experience tailored to individual user needs.

[0647] 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.

[0648] In this invention, the server includes an unmanned aerial vehicle (UAV) that automatically transports the delivery target, intelligent control means for optimizing the UAV's movement path, and power unit means mounted on the UAV for equipping the delivery target. This makes it possible to provide personalized service that takes into account the user's emotional state through emotion analysis, thereby realizing an optimal delivery service that improves user satisfaction.

[0649] "Delivery target" refers to the goods or packages to be transported, specifically the items to be transported by unmanned aerial vehicles.

[0650] An "unmanned aerial vehicle" is an aircraft that is operated remotely or automatically to transport items to their destination.

[0651] "Intelligent control means" refers to a control system that includes artificial intelligence technology used to optimize the flight path and operation of an unmanned aerial vehicle.

[0652] "Powered device means" refers to a device mounted on an unmanned aerial vehicle that includes a mechanism for equipping the delivery target and releasing or securing it at a predetermined location.

[0653] A "user connection means" is an interface for notifying users of the status of delivery services and enables the exchange of information between users and the system.

[0654] An "emotion analysis tool" is a system component that analyzes a user's voice, facial expressions, and text data to evaluate their emotional state.

[0655] "Information provision means" refers to a feedback system that provides users with notifications regarding the progress of delivery and based on their emotions.

[0656] This invention relates to a delivery system utilizing unmanned aerial vehicles, aiming to flexibly adjust services according to the emotional state of the user. The system is realized by combining an unmanned aerial vehicle, intelligent control means, power unit means, user connection means, emotion analysis means, and information provision means.

[0657] The server utilizes a generative AI model to analyze voice tone, facial recognition data, and text input acquired from users, functioning as a means of emotion analysis. In this process, the AI ​​model comprehensively analyzes the data to accurately grasp the user's emotional state, identifying emotions such as dissatisfaction or joy. For example, it estimates emotions based on specific keywords or emojis in the text, as well as voice tone, and adjusts the service accordingly.

[0658] The terminal controls the power unit mounted on the unmanned aerial vehicle, ensuring accurate handling of the delivery target. To achieve this, it employs an algorithm that measures location, weight, and size of the delivery target in real time and calculates the optimal handling method. The power unit is a crucial mechanism within the unmanned aerial vehicle, enabling stable delivery of the target target to its destination.

[0659] Users can check the delivery progress through the application and receive real-time information from the server, including delivery details and estimated arrival times. By receiving notifications based on sentiment analysis results, users can understand the situation with peace of mind, even in cases of delays due to bad weather, and take appropriate action.

[0660] For example, if a user orders a specific product and checks the progress through the application during the delivery process, and becomes anxious about delays, the sentiment analysis tool will identify that emotion. The system will then recalculate the estimated arrival date and suggest alternative delivery methods. This allows the user to use the service with peace of mind.

[0661] An example of a prompt might be, "Please tell me how to use the emotion engine to analyze the emotions based on the data the user provided when ordering a product, and how to improve the delivery process."

[0662] This makes it possible to significantly improve user satisfaction by providing a unique delivery method that reflects user emotions and optimizes the delivery experience.

[0663] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0664] Step 1:

[0665] The server receives order information from the user. This input consists of data such as product information and desired delivery time, which the user submits through the application. Based on this information, the server uses a generative AI model to analyze it and create a basic delivery plan by comparing it with the user's past order history and current delivery schedule. The output is the data for the initial delivery plan.

[0666] Step 2:

[0667] The server receives the user's voice tone, facial recognition data, and text input as input data, and analyzes their emotions through emotion analysis tools. Here, data calculations that combine voice data analysis, facial expression analysis, and text emotion analysis are performed to determine the emotion. The output is data indicating the user's emotional state.

[0668] Step 3:

[0669] The server customizes the delivery plan based on the user's emotional state analyzed in step 2. The inputs are emotional state data and the initial delivery plan. Using a generative AI model, the server optimizes the delivery according to the emotion, for example, selecting the shortest route and rearranging the processing steps for urgent requests, resulting in an optimized custom delivery plan as output.

[0670] Step 4:

[0671] The terminal controls the propulsion system of the unmanned aerial vehicle (UAV) to handle the delivery target based on an optimized delivery plan. The input is the optimized delivery plan, and the terminal utilizes real-time weather and location information to perform actions to ensure stable transport of the delivery target. This includes balanced lifting actions by the robotic arm and navigation operations to the destination. The output is the progress of the delivery.

[0672] Step 5:

[0673] Users receive delivery progress updates via the application. Based on the ongoing information and sentiment, the server provides real-time notifications and service adjustments as needed. For example, if delays are anticipated, it updates the estimated arrival time and offers alternative options. The output consists of detailed delivery information and recommendations sent to the user.

[0674] These steps enable the system to consider user emotions and provide an optimal delivery experience tailored to diverse needs.

[0675] (Application Example 2)

[0676] 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".

[0677] In recent years, delivery services using unmanned aerial vehicles have expanded, but conventional systems are unable to provide flexible services that respond to the emotions of users, resulting in challenges in user satisfaction. Therefore, it is necessary to dynamically adjust the delivery process based on the emotions of users to provide a more comfortable and optimized delivery experience.

[0678] 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.

[0679] In this invention, the server includes an aerial transport device for automatically transporting items to be delivered, an intelligent control device for optimizing the flight path of the aerial transport device, a drive mechanism for attaching items to be delivered to the aerial transport device, a user interaction interface device, and an emotion analysis mechanism for recognizing the user's emotions and dynamically adjusting the delivery service. This makes it possible to provide flexible delivery services according to the user's emotional state.

[0680] "Delivery target items" refer to items or goods that are transported by unmanned aerial transport devices.

[0681] "Air transport device" refers to an unmanned aerial vehicle used to automatically transport goods for delivery.

[0682] An "intelligent control system" is a control system equipped with artificial intelligence to optimize the flight path of an aerial transport device.

[0683] A "driving mechanical device" is a mechanical device that supplies power to attach or detach objects to be delivered to an aerial transport device.

[0684] A "user interaction interface device" is an interface that exchanges information with users to manage the receipt, departure, delivery, and confirmation of items to be delivered.

[0685] The "emotion analysis mechanism" is a system that recognizes the user's emotions and dynamically adjusts the delivery service based on those emotions.

[0686] A system specifically implementing this invention includes an aerial transport device, an intelligent control device, a drive mechanism, a user interaction interface device, and an emotion analysis mechanism.

[0687] The server first transports the items to be delivered via an aerial transport system. The intelligent control unit utilizes real-time traffic and weather data to optimize the transport system's flight path. At this time, an emotion analysis mechanism dynamically adjusts the delivery process based on the user's emotional information. The server performs emotion analysis using voice data, facial recognition data, and text data collected from the user's smartphone or other devices. This allows the server to adjust the delivery priority and method, providing the user with the best possible experience.

[0688] The terminal provides delivery progress and emotion-based feedback via a user interaction interface. Utilizing an emotion analysis mechanism, it appropriately offers anxiety reduction and specific countermeasures as requested by the user. For example, if delivery is delayed due to bad weather, the system can send a notification to the user, providing an accurate estimated arrival time and alternative options.

[0689] As a concrete example, consider a case where a user orders food delivery at night. In this case, the server analyzes the user's voice tone and the degree of urgency and anxiety from the entered text, and adjusts the route to ensure prompt delivery. It also makes suggestions about benefits and services to increase customer satisfaction.

[0690] An example of a prompt for a generative AI model is, "Use the user's voice and facial expressions to analyze their emotions and adjust the delivery method accordingly."

[0691] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0692] Step 1:

[0693] The server receives voice data, facial recognition data, and text data from the user as input. The server sends this data to an emotion analysis mechanism to analyze the user's emotional state. As part of the data processing, tone analysis is performed on the voice data, facial features are extracted, and emotion analysis is performed on the text data, and the results are output as the emotional state.

[0694] Step 2:

[0695] After the server receives the user's emotional state, it uses an intelligent control unit to acquire real-time traffic information and weather data as input. The input environmental data and the user's emotional state are combined to perform data calculations to determine the optimal flight path and delivery method. The output of this process is the adjusted flight path and delivery priority.

[0696] Step 3:

[0697] The terminal receives the coordinated flight path and delivery priority transmitted from the server, and operates the drive mechanism to attach the delivery target to the aerial transport device. The terminal controls the necessary power during this process and prepares for delivery. Furthermore, this information is notified to the user via the user interaction interface device. As output, information confirming the start of delivery and its progress is displayed to the user.

[0698] Step 4:

[0699] The user receives updates on the delivery progress and suggestions based on their emotions through the device. If the user feels anxious during delivery, the device accepts accurate arrival predictions and alternative suggestions as input, processes this information using a generating AI model, and provides information to reassure the user as output.

[0700] Step 5:

[0701] Ultimately, the server collects feedback after delivery is complete, records the user's emotions and experience, and analyzes the data to improve the service next time. This feedback serves as foundational data for continuously optimizing the service and for proposing further recommendations using generative AI models.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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."

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0723] The following is further disclosed regarding the embodiments described above.

[0724] (Claim 1)

[0725] Unmanned aerial vehicles that automatically transport items to be delivered,

[0726] An artificial intelligence control means for optimizing the flight path of an unmanned aerial vehicle,

[0727] A power mechanism means for mounting a delivery target on an unmanned aerial vehicle and attaching the delivery target to the unmanned aerial vehicle,

[0728] A user interface for managing the receipt, departure, delivery, and confirmation of items to be delivered,

[0729] A system that includes this.

[0730] (Claim 2)

[0731] The system according to claim 1, which utilizes real-time traffic information and weather data to generate a flight path for an unmanned aerial vehicle.

[0732] (Claim 3)

[0733] The system according to claim 1, comprising a method for providing notifications to the user upon receipt and delivery of the delivered items.

[0734] "Example 1"

[0735] (Claim 1)

[0736] Aircraft that automatically transport goods,

[0737] An intelligent control means for optimizing the aircraft's flight path,

[0738] A power source for mounting goods onto an aircraft, and for attaching goods to the aircraft.

[0739] An operating interface means for managing the receipt, dispatch, delivery, and confirmation of delivered goods,

[0740] A detection means for checking the shape and weight of the goods to safely load them for delivery,

[0741] ...

[0742] A system that includes this.

[0743] (Claim 2)

[0744] The system according to claim 1, which utilizes real-time traffic information and weather data to generate an aircraft's travel path and plans an optimal path using a generative model.

[0745] (Claim 3)

[0746] The system according to claim 1, comprising a method for providing a notification to the user upon receipt and delivery of the delivered goods.

[0747] "Application Example 1"

[0748] (Claim 1)

[0749] An aerial vehicle that automatically transports items to be delivered,

[0750] A machine intelligence control means for optimizing the flight path of an aerial mobile object,

[0751] A power mechanism mounted on an aerial vehicle for attaching the object to be delivered to the aerial vehicle,

[0752] A user interaction tool for managing the receipt, departure, delivery, and confirmation of items to be delivered,

[0753] Information processing means that calculates and notifies the delivery time based on order information,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, which utilizes current traffic information and weather data to generate a flight path for an aerial vehicle.

[0757] (Claim 3)

[0758] The system according to claim 1, further comprising a method for providing notifications to the user and displaying the delivery progress when receiving and delivering the item to be delivered.

[0759] "Example 2 of combining an emotion engine"

[0760] (Claim 1)

[0761] Unmanned aerial vehicles that automatically transport items for delivery,

[0762] An intelligent control means for optimizing the movement path of an unmanned aerial vehicle,

[0763] A power unit and means for mounting on an unmanned aerial vehicle and equipping the target for delivery,

[0764] A user connection method for managing the receipt, initiation, delivery, and confirmation of items to be delivered,

[0765] An emotion analysis tool that analyzes the emotional state of users and adjusts delivery accordingly,

[0766] A means of providing information that notifies users of the progress regarding items to be delivered and responds in accordance with their emotions,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, which uses real-time traffic information and weather information to generate flight paths for unmanned aerial vehicles, and further adjusts the delivery plan based on user sentiment data.

[0770] (Claim 3)

[0771] The system according to claim 1, comprising a method for providing the user with information based on sentiment analysis results when receiving and delivering items for delivery.

[0772] "Application example 2 when combining with an emotional engine"

[0773] (Claim 1)

[0774] An aerial transport system that automatically transports items to be delivered,

[0775] An intelligent control device that optimizes the flight path of an aerial transport device,

[0776] A drive mechanism mounted on an aerial transport device for attaching the object to be delivered to the aerial transport device,

[0777] A user interaction interface device for managing the receipt, departure, delivery, and confirmation of items to be delivered,

[0778] An emotion analysis mechanism that recognizes the user's emotions and dynamically adjusts the delivery service,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, which uses time-of-day traffic information and weather data to generate a flight path for an aerial transport device, and also takes into account emotional information obtained by an emotional analysis mechanism.

[0782] (Claim 3)

[0783] The system according to claim 1, comprising a method for providing users with notifications and emotionally-based feedback upon receipt and delivery of items to be delivered. [Explanation of Symbols]

[0784] 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. An aerial vehicle that automatically transports items to be delivered, A machine intelligence control means for optimizing the flight path of an aerial mobile object, A power mechanism mounted on an aerial vehicle for attaching the object to be delivered to the aerial vehicle, A user interaction tool for managing the receipt, departure, delivery, and confirmation of items to be delivered, Information processing means that calculates and notifies the delivery time based on order information, A system that includes this.

2. The system according to claim 1, which utilizes current traffic information and weather data to generate a flight path for an aerial vehicle.

3. The system according to claim 1, further comprising a method for providing notifications to the user and displaying the delivery progress when receiving and delivering the item to be delivered.

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