system

Abstract

The system according to this embodiment aims to solve the problems of delivery delays and errors caused by labor shortages and rising costs in the logistics industry, as well as the waiting times for consumers. [Solution] The system according to the embodiment comprises a reception unit, a calculation unit, a control unit, and a delivery unit. The reception unit receives order information. The calculation unit calculates the optimal delivery route based on the information received by the reception unit. The control unit controls the drone based on the route calculated by the calculation unit. The delivery unit provides delivery status in real time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 the conventional technology, problems such as delays and mistakes in delivery due to labor shortages and cost increases in the logistics industry, and the waiting time of consumers have not been solved, and there is room for improvement.

[0005] The system according to the embodiment aims to solve problems such as delays and mistakes in delivery due to labor shortages and cost increases in the logistics industry, and the waiting time of consumers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a calculation unit, a control unit, and a delivery unit. The reception unit receives order information. The calculation unit calculates the optimal delivery route based on the information received by the reception unit. The control unit controls the drone based on the route calculated by the calculation unit. The delivery unit provides delivery status in real time. [Effects of the Invention]

[0007] The system according to this embodiment can solve the problems of labor shortages and cost increases in the logistics industry, which can lead to delivery delays and errors, as well as waiting times for consumers. [Brief explanation of the drawing]

[0008] [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] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication among a plurality of 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network). <00​The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The delivery system according to an embodiment of the present invention is a novel delivery system that utilizes generative AI and drone technology. This delivery system aims to solve problems for the logistics industry and consumers. Specifically, a user orders goods from an online shopping site. The order information is processed in real time by AI and the delivery destination information is provided to the drone. Next, the AI ​​calculates the optimal delivery route, and the drone automatically delivers the goods. The drone flies over the city and heads to the destination via the shortest route. During delivery, the customer can use the AI ​​management system to check the current location of the delivery and the estimated delivery time in real time. This allows the customer to properly prepare for receiving the goods. Furthermore, AI drones can also be used for takeout orders and ingredient procurement for restaurants. Restaurants can use AI-powered drones to deliver takeout orders directly to customers' homes. It can also be used for deliveries from local farms or wholesalers that provide the ingredients restaurants need. This reduces the effort required for early morning procurement work for restaurants and enables them to deliver fresh ingredients to their stores in a short time. This system provides an effective solution for a wide range of targets, including logistics companies, delivery companies, online shoppers, and restaurants. AI and drone automation reduce human error, improve delivery efficiency, and lower costs. Furthermore, optimizing delivery schedules shortens consumer waiting times and addresses issues of labor shortages and rising costs. 24 / 7 delivery, anytime, anywhere, is possible, ensuring consistent service quality. This delivery system can solve problems for both the logistics industry and consumers, improving delivery efficiency.

[0029] The delivery system according to this embodiment comprises a reception unit, a calculation unit, a control unit, and a delivery unit. The reception unit receives order information. Order information includes, but is not limited to, the type and quantity of goods, and delivery address information. The reception unit receives, for example, order information from online shopping sites in real time. The reception unit can also process order information received by other means such as telephone or email. Furthermore, the reception unit can automatically classify and process order information using AI. For example, the reception unit uses AI to analyze order information and check the inventory status of goods. The calculation unit calculates the optimal delivery route based on the information received by the reception unit. The optimal delivery route is calculated based on, for example, criteria such as distance, time, and traffic conditions, but is not limited to. The calculation unit uses, for example, AI to collect real-time traffic information and calculate the optimal route. The calculation unit can also improve the accuracy of the route by referring to past delivery data. For example, the calculation unit proposes the optimal route for a specific time period based on past delivery data. The control unit controls the drone based on the route calculated by the calculation unit. The control unit adjusts, for example, the drone's flight pattern and speed. The control unit can also ensure a safe flight path using an obstacle detection system. For example, the control unit detects obstacles during flight and automatically avoids them. The delivery unit provides delivery status in real time. For example, the delivery unit notifies the customer of the current location of the delivery and the estimated delivery time. The delivery unit can also provide an interface for customers to check the delivery status in real time. For example, the delivery unit displays the delivery status through a web application or mobile application. Thus, the delivery system according to this embodiment can solve problems for the logistics industry and consumers and improve delivery efficiency.

[0030] The reception department receives order information. This order information includes, but is not limited to, product type, quantity, and delivery address information. For example, the reception department receives order information from online shopping sites in real time. Specifically, it retrieves order information through the API of online shopping sites and stores it in a database. The reception department can also process order information received through other means such as telephone and email. For example, telephone orders are entered by operators, while email orders are automatically analyzed. Furthermore, the reception department can use AI to automatically classify and process order information. For example, it can use natural language processing technology to analyze the content of emails and extract product types and quantities. The AI ​​analyzes the order information and checks the inventory status of products. Specifically, it works in conjunction with the inventory management system to check in real time whether the ordered products are in stock. If the inventory is insufficient, the AI ​​can also automatically arrange for replenishment. This allows the reception department to efficiently process diverse order information and respond quickly. Furthermore, the reception department can manage the history of order information and analyze customer purchasing trends. This can be used to develop marketing strategies and optimize inventory management.

[0031] The calculation unit calculates the optimal delivery route based on the information received by the reception unit. The optimal delivery route is calculated based on criteria such as distance, time, and traffic conditions, but is not limited to these examples. For example, the calculation unit uses AI to collect real-time traffic information and calculate the optimal route. Specifically, it obtains data from traffic information services via APIs, and the AI ​​analyzes it. The AI ​​considers road congestion and accident information to propose the shortest and fastest route. The calculation unit can also improve route accuracy by referring to past delivery data. For example, it can propose the optimal route for a specific time period based on past delivery data. This allows the calculation unit to maximize delivery efficiency and shorten delivery times to customers. Furthermore, if there are multiple delivery destinations, the calculation unit can also calculate the optimal delivery order. For example, the AI ​​considers the location and delivery time of each destination to propose the most efficient order. This reduces fuel consumption and environmental impact. Based on this information, the calculation unit provides specific instructions to drivers and drones.

[0032] The control unit controls the drone based on the route calculated by the calculation unit. For example, the control unit adjusts the drone's flight pattern and speed. Specifically, it works in conjunction with the drone's flight control system to automatically fly the drone according to the calculated route. The control unit can also ensure a safe flight path using an obstacle detection system. For example, the drone can detect obstacles during flight and automatically avoid them. Obstacle detection involves monitoring the surrounding environment in real time using cameras and sensors, and the AI ​​analyzes the data. The AI ​​predicts the position and movement of obstacles and calculates the optimal avoidance route. Furthermore, the control unit can monitor the drone's battery level and issue instructions to return to the charging station if necessary. This allows for efficient deliveries without interruptions to the drone's flight. The control unit can also record the drone's flight data and analyze it later. This can be used to optimize flight patterns and plan maintenance.

[0033] The service provider will provide real-time delivery status updates. For example, they will notify customers of the current location of their delivery and the estimated delivery time. Specifically, they will track the drone's current location using GPS and notify the customer. The service provider can also provide an interface that allows customers to check the delivery status in real time. For example, they can display the delivery status through a web application or mobile application. This allows customers to check the location of their order in real time. Furthermore, the service provider can collect customer feedback after delivery is complete. For example, they can accept evaluations of delivery speed and quality and use this to improve the system. Based on this information, the service provider can take measures to improve delivery efficiency and customer satisfaction. In addition, the service provider can quickly notify customers and take appropriate action if there are delivery delays or problems. This allows customers to use the service with peace of mind.

[0034] The reception desk can accept takeout orders from restaurants. For example, the reception desk can accept takeout orders in real time from restaurants' online ordering systems. The reception desk can also process takeout orders received through other means such as telephone or email. Furthermore, the reception desk can use AI to automatically classify and process takeout order information. For example, the reception desk can use AI to analyze takeout order information and check the restaurant's inventory status. This allows for efficient processing of restaurant takeout orders. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input takeout order information from restaurants' online ordering systems into a generating AI and have the generating AI classify and process the order information.

[0035] The delivery department can deliver ingredients needed by restaurants from local farms or wholesalers. For example, the delivery department can receive order information from local farms or wholesalers, calculate the optimal delivery route, and control drones to deliver the ingredients. The delivery department can also use AI to optimize delivery routes and achieve efficient delivery. For example, the delivery department can use AI to collect real-time traffic information and calculate the optimal route. This allows restaurants to receive fresh ingredients in a short amount of time. Some or all of the processes described above in the delivery department may be performed using AI, or not. For example, the delivery department can input order information from local farms or wholesalers into a generating AI and have the generating AI perform the calculation of delivery routes and control drones.

[0036] The Promotion Department can handle promotions and events. For example, the Promotion Department can conduct promotions in conjunction with specific campaigns or festivals. Furthermore, the Promotion Department can use AI to analyze the effectiveness of promotions and select the most suitable promotional methods. For instance, the Promotion Department can use AI to analyze past promotional data and propose the most effective promotional methods, making them suitable for use in promotions and events. Some or all of the above-described processes in the Promotion Department may be performed using AI, or not. For example, the Promotion Department can input past promotional data into a generating AI and have the generating AI select promotional methods.

[0037] The tracking unit can enable customers to check the delivery status in real time. For example, the tracking unit can notify customers of the current location and estimated delivery time of the package. The tracking unit can also provide an interface for customers to check the delivery status in real time. For example, the tracking unit can display the delivery status through a web application or a mobile application. This allows customers to check the current location and estimated delivery time of the package in real time. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the current location and estimated delivery time of the package into a generating AI and have the generating AI perform real-time notifications.

[0038] The scheduling unit can optimize delivery schedules. For example, the scheduling unit can use AI to collect real-time traffic information and create an optimal delivery schedule. The scheduling unit can also improve the accuracy of the schedule by referring to past delivery data. For example, the scheduling unit can propose an optimal schedule for a specific time period based on past delivery data. This optimizes the delivery schedule and reduces waiting times for consumers. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input real-time traffic information and past delivery data into a generating AI and have the generating AI create an optimal delivery schedule.

[0039] The reception desk can analyze past order history and suggest recommended products based on user preferences when taking an order. For example, the reception desk can display related products as recommendations based on products the user has purchased in the past. The reception desk can also suggest products related to specific seasons or events based on the user's past order history. Furthermore, the reception desk can analyze the user's purchase frequency and automatically add regularly purchased items to the cart. This improves order satisfaction by suggesting products based on user preferences. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past order history data into a generating AI and have the generating AI perform the task of suggesting recommended products.

[0040] The reception desk can present the optimal delivery option when receiving an order, taking into account the user's current location. For example, the reception desk can present the delivery option from the nearest distribution center based on the user's current location. The reception desk can also suggest the option that allows for the shortest delivery time based on the user's location. Furthermore, the reception desk can suggest the optimal delivery time slot, taking into account the user's current location and traffic conditions. This improves delivery efficiency by providing the optimal delivery option based on the user's location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current location information into a generating AI and have the generating AI perform the task of presenting the optimal delivery option.

[0041] The reception desk can analyze the user's social media activity when receiving an order and present relevant promotions. For example, the reception desk can suggest special promotions based on products the user has shown interest in on social media. It can also analyze the user's social media posts and display relevant products as recommendations. Furthermore, the reception desk can offer special discounts and campaigns considering the user's social media follower count and influence. This improves order satisfaction by providing promotions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the promotion presentation.

[0042] The order processing unit can optimize the interface when receiving an order, taking into account past user feedback. For example, the order processing unit can adjust the interface design based on feedback previously provided by the user. It can also add or remove specific features or options based on user feedback. Furthermore, the order processing unit can analyze user feedback and incorporate improvements to enhance usability. This improves the efficiency of order processing by providing an interface based on user feedback. Some or all of the above processes in the order processing unit may be performed using AI, for example, or not. For example, the order processing unit can input past user feedback data into a generating AI and have the generating AI perform interface optimization.

[0043] The calculation unit can select the optimal route by considering traffic conditions and weather information in real time when calculating delivery routes. For example, the calculation unit calculates the optimal route based on real-time traffic congestion information. The calculation unit can also select a safe route by considering real-time weather information. Furthermore, the calculation unit can suggest detour routes based on real-time road construction information. This improves delivery efficiency by providing the optimal route based on real-time information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input real-time traffic conditions and weather information into a generating AI and have the generating AI perform the selection of the optimal route.

[0044] The calculation unit can improve the accuracy of delivery routes by referring to past delivery data when calculating delivery routes. For example, the calculation unit calculates the most efficient route based on past delivery data. The calculation unit can also suggest the optimal route for a specific time period based on past delivery data. Furthermore, the calculation unit can analyze past delivery data and improve the route calculation algorithm. This improves delivery accuracy by providing routes based on past data. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past delivery data into a generating AI and have the generating AI perform route accuracy improvements.

[0045] The calculation unit can optimize delivery routes by considering geographical characteristics. For example, the calculation unit calculates the optimal route by considering the topography. It can also select efficient routes by considering the characteristics of urban and suburban areas. Furthermore, the calculation unit can suggest routes that avoid geographical obstacles. This improves delivery efficiency by providing routes based on geographical characteristics. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input geographical characteristic data into a generating AI and have the generating AI perform route optimization.

[0046] The calculation unit can adjust delivery routes by considering the operational status of other drones when calculating delivery routes. For example, the calculation unit can monitor the operational status of other drones in real time and calculate routes that avoid collisions. The calculation unit can also select efficient routes by considering the operational schedules of other drones. Furthermore, the calculation unit can communicate with other drones and coordinate route adjustments. This improves delivery efficiency by providing routes based on the operational status of other drones. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input operational status data of other drones into a generating AI and have the generating AI perform route adjustments.

[0047] The control unit can ensure a safe flight path using an obstacle detection system when controlling the drone. For example, the control unit can detect obstacles while the drone is in flight and automatically avoid them. The control unit can also set the optimal flight altitude using the obstacle detection system. Furthermore, the control unit can monitor the obstacle detection system in real time and maintain a safe flight path. In this way, a safe flight path can be provided by using the obstacle detection system. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input obstacle detection data into a generating AI and have the generating AI perform the task of ensuring a safe flight path.

[0048] The control unit can monitor the battery level in real time during drone control and select the optimal charging point. For example, if the drone's battery level is low, the control unit can suggest the nearest charging point. The control unit can also monitor the battery level in real time and set an efficient charging schedule. Furthermore, the control unit can adjust the flight route according to the battery level and head towards the charging point. This improves flight efficiency by providing the optimal charging point based on the battery level. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input battery level data into a generating AI and have the generating AI select the optimal charging point.

[0049] The control unit can communicate with other drones during drone control to achieve coordinated flight. For example, the control unit can communicate with other drones and adjust flight routes to avoid collisions. The control unit can also achieve coordinated flight with other drones to enable efficient delivery. Furthermore, the control unit can monitor the operational status of other drones in real time and optimize flight routes. This improves delivery efficiency by enabling coordinated flight with other drones. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input operational status data of other drones into a generating AI and have the generating AI perform the implementation of coordinated flight.

[0050] The control unit can collect environmental data during flight while controlling the drone and utilize it for the next flight. For example, the control unit can optimize the next flight route based on the environmental data collected during flight. The control unit can also collect weather data during flight and reflect it in the next flight plan. Furthermore, the control unit can collect obstacle data during flight and set avoidance routes for the next flight. In this way, the efficiency of the next flight is improved by utilizing the environmental data during flight. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the environmental data during flight into a generating AI and have the generating AI perform the optimization of the next flight plan.

[0051] The service provider can improve prediction accuracy by referring to past delivery data when providing delivery status. For example, the service provider can improve the accuracy of delivery time predictions based on past delivery data. The service provider can also analyze trends in delivery delays during specific time periods from past delivery data and reflect this in the predictions. Furthermore, the service provider can analyze past delivery data and improve the prediction algorithm. This improves delivery efficiency by providing prediction accuracy based on past data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past delivery data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0052] The delivery unit can customize notification content when providing delivery status, taking into account the user's current location information. For example, the delivery unit can notify the user of the delivery status from the nearest delivery center based on the user's current location. The delivery unit can also notify the user of the shortest estimated arrival time based on the user's location information. Furthermore, the delivery unit can notify the user of the optimal delivery time slot, taking into account the user's current location and traffic conditions. This improves delivery efficiency by providing notification content based on the user's location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's current location information into a generating AI and have the generating AI perform the customization of the notification content.

[0053] The service provider can select the optimal notification method when providing delivery status, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide delivery status using push notifications. Furthermore, if the user is using a tablet, the service provider can provide a notification method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible notification method. This makes it easier to check delivery status by providing notification methods based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0054] The service provider can optimize notification content by considering the user's past feedback when providing delivery status. For example, the service provider can adjust notification content based on feedback previously provided by the user. The service provider can also add or remove specific information from the user's feedback. Furthermore, the service provider can analyze user feedback and incorporate improvements to enhance usability. This makes it easier to check the delivery status by providing notifications based on user feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI perform the optimization of notification content.

[0055] The delivery department can collect environmental information about the delivery destination in real time during delivery and select the optimal delivery method. For example, the delivery department can collect weather information about the delivery destination in real time and select a safe delivery method. It can also collect traffic conditions at the delivery destination in real time and select an efficient delivery method. Furthermore, the delivery department can select the optimal delivery method considering the geographical characteristics of the delivery destination. This improves delivery efficiency by providing the optimal delivery method based on environmental information about the delivery destination. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input environmental information about the delivery destination into a generating AI and have the generating AI select the optimal delivery method.

[0056] The delivery department can check the delivery status at the recipient's location in real time and select the optimal delivery timing. For example, the delivery department can check the delivery status at the recipient's location in real time and select a time when delivery is possible. The delivery department can also suggest the optimal delivery time based on the recipient's location

[0057] The delivery department can achieve efficient delivery by coordinating with other delivery methods. For example, the delivery department can select the optimal delivery route in cooperation with other delivery methods. Furthermore, the delivery department can set an efficient delivery schedule through cooperation with other delivery methods. In addition, the delivery department can monitor the operational status of other delivery methods in real time and adjust delivery routes accordingly. This improves delivery efficiency through coordination with other delivery methods. Some or all of the above processes in the delivery department may be performed using AI, or not. For example, the delivery department can input operational status data of other delivery methods into a generating AI and have the generating AI execute the task of achieving efficient delivery.

[0058] The delivery department can provide customized delivery methods at the time of delivery, according to the specific requirements of the delivery destination. For example, the delivery department can provide special packaging methods according to the specific requirements of the delivery destination. The delivery department can also deliver at specific time slots according to the specific requirements of the delivery destination. Furthermore, the delivery department can use special delivery means according to the specific requirements of the delivery destination. This improves delivery efficiency by providing delivery methods tailored to the specific requirements of the delivery destination. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input specific request data from the delivery destination into a generating AI and have the generating AI perform the task of providing customized delivery methods.

[0059] The promotion department can select effective promotional methods by referring to past promotional data during promotions. For example, the promotion department can select the most effective promotional method based on past promotional data. The promotion department can also propose promotional methods related to specific seasons or events based on past promotional data. Furthermore, the promotion department can analyze past promotional data and reflect areas for improvement in promotional methods. This improves the effectiveness of promotions by providing promotional methods based on past data. Some or all of the above processes in the promotion department may be performed using AI, for example, or not. For example, the promotion department can input past promotional data into a generating AI and have the generating AI select effective promotional methods.

[0060] The promotion department can customize promotional content by taking into account the user's current location during a promotion. For example, the promotion department can provide promotional content for the nearest store based on the user's current location. It can also provide promotional content related to a specific region based on the user's location. Furthermore, the promotion department can provide optimal promotional content by considering the user's current location and traffic conditions. This improves the effectiveness of promotions by providing promotional content based on the user's location. Some or all of the above processing in the promotion department may be performed using AI, for example, or without AI. For example, the promotion department can input the user's current location information into a generating AI and have the generating AI customize the promotional content.

[0061] The promotion department can analyze users' social media activity during promotions and present relevant promotions. For example, the promotion department can suggest special promotions based on products that users have shown interest in on social media. The promotion department can also analyze the content of users' social media posts and display relevant products as recommendations. Furthermore, the promotion department can offer special discounts and campaigns considering the number of followers and influence of users on social media. This improves the effectiveness of promotions by providing promotions based on users' social media activity. Some or all of the above processes in the promotion department may be performed using AI, for example, or not. For example, the promotion department can input user social media activity data into a generating AI and have the generating AI perform the task of presenting relevant promotions.

[0062] The promotion department can optimize promotional content by considering past user feedback during the promotion process. For example, the promotion department can adjust promotional content based on feedback previously provided by users. The promotion department can also add or remove specific information from user feedback. Furthermore, the promotion department can analyze user feedback and reflect improvements to the promotional content. This improves the effectiveness of promotions by providing promotional content based on user feedback. Some or all of the above processes in the promotion department may be performed using AI, for example, or not. For example, the promotion department can input past user feedback data into a generating AI and have the generating AI perform the optimization of the promotional content.

[0063] The tracking unit can improve tracking accuracy by referring to past tracking data during tracking. For example, the tracking unit improves tracking accuracy based on past tracking data. The tracking unit can also analyze the trend of tracking accuracy in a specific time period from past tracking data and reflect it in tracking. Furthermore, the tracking unit can analyze past tracking data and improve the tracking algorithm. This improves tracking efficiency by providing tracking accuracy based on past data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input past tracking data into a generating AI and have the generating AI perform the improvement of tracking accuracy.

[0064] The tracking unit can improve tracking accuracy based on real-time location information during tracking. For example, the tracking unit can accurately display the current delivery status based on real-time location information. The tracking unit can also accurately predict the estimated delivery time based on real-time location information. Furthermore, the tracking unit can immediately reflect changes in the delivery route based on real-time location information. This improves tracking efficiency by providing tracking accuracy based on real-time location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input real-time location information into a generating AI and have the generating AI perform the task of improving tracking accuracy.

[0065] The tracking unit can select the optimal tracking method during tracking, taking into account the user's device information. For example, if the user is using a smartphone, the tracking unit can provide tracking information using push notifications. Furthermore, if the user is using a tablet, the tracking unit can provide a tracking method optimized for a larger screen. Additionally, if the user is using a smartwatch, the tracking unit can provide a concise and highly visible tracking method. This improves tracking efficiency by providing a tracking method based on the user's device information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's device information into a generating AI and have the generating AI select the optimal tracking method.

[0066] The tracking unit can optimize tracking information by considering the user's past feedback during tracking. For example, the tracking unit can adjust how tracking information is displayed based on feedback previously provided by the user. The tracking unit can also add or remove specific information from the user's feedback. Furthermore, the tracking unit can analyze the user's feedback and reflect improvements to enhance usability. This improves tracking efficiency by providing tracking information based on user feedback. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past feedback data into a generating AI and have the generating AI perform the optimization of tracking information.

[0067] The scheduling unit can create the optimal schedule by referring to past schedule data when creating a schedule. For example, the scheduling unit can create the most efficient schedule based on past schedule data. The scheduling unit can also suggest the optimal schedule for a specific time period based on past schedule data. Furthermore, the scheduling unit can analyze past schedule data and improve the schedule creation algorithm. This improves delivery efficiency by providing schedules based on past data. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input past schedule data into a generation AI and have the generation AI create the optimal schedule.

[0068] The scheduling unit can optimize schedules by considering real-time traffic information when creating them. For example, the scheduling unit can create an optimal schedule based on real-time traffic congestion information. The scheduling unit can also set a safe schedule by considering real-time weather information. Furthermore, the scheduling unit can propose a schedule that includes detour routes based on real-time road construction information. This improves delivery efficiency by providing schedules based on real-time information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input real-time traffic information into a generation AI and have the generation AI perform schedule optimization.

[0069] The scheduling unit can adjust the schedule when creating it, taking into account the operational status of other drones. For example, the scheduling unit can monitor the operational status of other drones in real time and create a schedule that avoids collisions. The scheduling unit can also set an efficient schedule by taking into account the operational schedules of other drones. Furthermore, the scheduling unit can communicate with other drones and coordinate schedule adjustments. This improves delivery efficiency by providing a schedule based on the operational status of other drones. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input operational status data of other drones into a generating AI and have the generating AI perform schedule adjustments.

[0070] The scheduling unit can create schedules based on the user's calendar information when creating a schedule. For example, the scheduling unit can automatically set a schedule by referring to appointments registered in the user's calendar. The scheduling unit can also suggest schedules related to specific events based on the user's calendar information. Furthermore, the scheduling unit can create an optimal schedule tailored to the user's appointments based on the user's calendar information. This improves delivery efficiency by providing schedules based on the user's appointments. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the user's calendar information into a generation AI and have the generation AI create a schedule based on appointments.

[0071] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0072] The reception desk can analyze past order history and suggest recommended products based on user preferences when taking an order. For example, it can display related products as recommendations based on products the user has purchased in the past. It can also suggest products related to specific seasons or events based on the user's past order history. Furthermore, it can analyze the user's purchase frequency and automatically add regularly purchased items to the cart. This improves order satisfaction by suggesting products based on user preferences. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past order history data into a generating AI and have the generating AI perform the task of suggesting recommended products.

[0073] The reception desk can present the optimal delivery option when receiving an order, taking into account the user's current location. For example, it can present the delivery option from the nearest distribution center based on the user's current location. It can also suggest the option that allows for the shortest delivery time based on the user's location. Furthermore, it can suggest the optimal delivery time slot, taking into account the user's current location and traffic conditions. This improves delivery efficiency by providing the optimal delivery option based on the user's location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current location information into a generating AI and have the generating AI perform the task of presenting the optimal delivery option.

[0074] The order processing department can analyze the user's social media activity when receiving an order and present relevant promotions. For example, it can suggest special promotions based on products the user has shown interest in on social media. It can also analyze the user's social media posts and display relevant products as recommendations. Furthermore, it can offer special discounts and campaigns considering the user's number of social media followers and influence. This improves order satisfaction by providing promotions based on the user's social media activity. Some or all of the above processing in the order processing department may be performed using AI, for example, or not. For example, the order processing department can input the user's social media activity data into a generating AI and have the generating AI perform the promotion presentation.

[0075] The calculation unit can select the optimal route by considering traffic conditions and weather information in real time when calculating delivery routes. For example, it can calculate the optimal route based on real-time traffic congestion information. It can also select a safe route by considering real-time weather information. Furthermore, it can suggest detour routes based on real-time road construction information. This improves delivery efficiency by providing the optimal route based on real-time information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input real-time traffic conditions and weather information into a generating AI and have the generating AI perform the selection of the optimal route.

[0076] The control unit can use an obstacle detection system to ensure a safe flight path when controlling the drone. For example, the drone can detect obstacles during flight and automatically avoid them. It can also use the obstacle detection system to set the optimal flight altitude. Furthermore, it can monitor the obstacle detection system in real time to maintain a safe flight path. In this way, a safe flight path can be provided by using the obstacle detection system. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input obstacle detection data into a generating AI and have the generating AI perform the task of ensuring a safe flight path.

[0077] The following briefly describes the processing flow for example form 1.

[0078] Step 1: The reception desk receives order information. This information includes product type, quantity, and shipping address. The reception desk can receive order information from online shopping sites in real time, as well as order information received through other means such as phone and email. Furthermore, the reception desk can use AI to automatically classify and process order information. For example, the reception desk can use AI to analyze order information and check product inventory status. Step 2: The calculation unit calculates the optimal delivery route based on the information received by the reception unit. The optimal delivery route is calculated based on criteria such as distance, time, and traffic conditions. The calculation unit uses AI to collect real-time traffic information and calculate the optimal route. The calculation unit can also improve the accuracy of the route by referring to past delivery data. For example, the calculation unit can suggest the optimal route for a specific time period based on past delivery data. Step 3: The control unit controls the drone based on the route calculated by the calculation unit. The control unit adjusts the drone's flight pattern and speed. The control unit can also ensure a safe flight path using an obstacle detection system. For example, the control unit can detect obstacles while the drone is flying and automatically avoid them. Step 4: The service provider provides real-time delivery status. The service provider notifies the customer of the current location of the delivery and the estimated delivery time. The service provider can also provide an interface that allows the customer to check the delivery status in real time. For example, the service provider can display the delivery status through a web application or a mobile application.

[0079] (Example of form 2) The delivery system according to an embodiment of the present invention is a novel delivery system that utilizes generative AI and drone technology. This delivery system aims to solve problems for the logistics industry and consumers. Specifically, a user orders goods from an online shopping site. The order information is processed in real time by AI and the delivery destination information is provided to the drone. Next, the AI ​​calculates the optimal delivery route, and the drone automatically delivers the goods. The drone flies over the city and heads to the destination via the shortest route. During delivery, the customer can use the AI ​​management system to check the current location of the delivery and the estimated delivery time in real time. This allows the customer to properly prepare for receiving the goods. Furthermore, AI drones can also be used for takeout orders and ingredient procurement for restaurants. Restaurants can use AI-powered drones to deliver takeout orders directly to customers' homes. It can also be used for deliveries from local farms or wholesalers that provide the ingredients restaurants need. This reduces the effort required for early morning procurement work for restaurants and enables them to deliver fresh ingredients to their stores in a short time. This system provides an effective solution for a wide range of targets, including logistics companies, delivery companies, online shoppers, and restaurants. AI and drone automation reduce human error, improve delivery efficiency, and lower costs. Furthermore, optimizing delivery schedules shortens consumer waiting times and addresses issues of labor shortages and rising costs. 24 / 7 delivery, anytime, anywhere, is possible, ensuring consistent service quality. This delivery system can solve problems for both the logistics industry and consumers, improving delivery efficiency.

[0080] The delivery system according to this embodiment comprises a reception unit, a calculation unit, a control unit, and a delivery unit. The reception unit receives order information. Order information includes, but is not limited to, the type and quantity of goods, and delivery address information. The reception unit receives, for example, order information from online shopping sites in real time. The reception unit can also process order information received by other means such as telephone or email. Furthermore, the reception unit can automatically classify and process order information using AI. For example, the reception unit uses AI to analyze order information and check the inventory status of goods. The calculation unit calculates the optimal delivery route based on the information received by the reception unit. The optimal delivery route is calculated based on, for example, criteria such as distance, time, and traffic conditions, but is not limited to. The calculation unit uses, for example, AI to collect real-time traffic information and calculate the optimal route. The calculation unit can also improve the accuracy of the route by referring to past delivery data. For example, the calculation unit proposes the optimal route for a specific time period based on past delivery data. The control unit controls the drone based on the route calculated by the calculation unit. The control unit adjusts, for example, the drone's flight pattern and speed. The control unit can also ensure a safe flight path using an obstacle detection system. For example, the control unit detects obstacles during flight and automatically avoids them. The delivery unit provides delivery status in real time. For example, the delivery unit notifies the customer of the current location of the delivery and the estimated delivery time. The delivery unit can also provide an interface for customers to check the delivery status in real time. For example, the delivery unit displays the delivery status through a web application or mobile application. Thus, the delivery system according to this embodiment can solve problems for the logistics industry and consumers and improve delivery efficiency.

[0081] The reception department receives order information. This order information includes, but is not limited to, product type, quantity, and delivery address information. For example, the reception department receives order information from online shopping sites in real time. Specifically, it retrieves order information through the API of online shopping sites and stores it in a database. The reception department can also process order information received through other means such as telephone and email. For example, telephone orders are entered by operators, while email orders are automatically analyzed. Furthermore, the reception department can use AI to automatically classify and process order information. For example, it can use natural language processing technology to analyze the content of emails and extract product types and quantities. The AI ​​analyzes the order information and checks the inventory status of products. Specifically, it works in conjunction with the inventory management system to check in real time whether the ordered products are in stock. If the inventory is insufficient, the AI ​​can also automatically arrange for replenishment. This allows the reception department to efficiently process diverse order information and respond quickly. Furthermore, the reception department can manage the history of order information and analyze customer purchasing trends. This can be used to develop marketing strategies and optimize inventory management.

[0082] The calculation unit calculates the optimal delivery route based on the information received by the reception unit. The optimal delivery route is calculated based on criteria such as distance, time, and traffic conditions, but is not limited to these examples. For example, the calculation unit uses AI to collect real-time traffic information and calculate the optimal route. Specifically, it obtains data from traffic information services via APIs, and the AI ​​analyzes it. The AI ​​considers road congestion and accident information to propose the shortest and fastest route. The calculation unit can also improve route accuracy by referring to past delivery data. For example, it can propose the optimal route for a specific time period based on past delivery data. This allows the calculation unit to maximize delivery efficiency and shorten delivery times to customers. Furthermore, if there are multiple delivery destinations, the calculation unit can also calculate the optimal delivery order. For example, the AI ​​considers the location and delivery time of each destination to propose the most efficient order. This reduces fuel consumption and environmental impact. Based on this information, the calculation unit provides specific instructions to drivers and drones.

[0083] The control unit controls the drone based on the route calculated by the calculation unit. For example, the control unit adjusts the drone's flight pattern and speed. Specifically, it works in conjunction with the drone's flight control system to automatically fly the drone according to the calculated route. The control unit can also ensure a safe flight path using an obstacle detection system. For example, the drone can detect obstacles during flight and automatically avoid them. Obstacle detection involves monitoring the surrounding environment in real time using cameras and sensors, and the AI ​​analyzes the data. The AI ​​predicts the position and movement of obstacles and calculates the optimal avoidance route. Furthermore, the control unit can monitor the drone's battery level and issue instructions to return to the charging station if necessary. This allows for efficient deliveries without interruptions to the drone's flight. The control unit can also record the drone's flight data and analyze it later. This can be used to optimize flight patterns and plan maintenance.

[0084] The service provider will provide real-time delivery status updates. For example, they will notify customers of the current location of their delivery and the estimated delivery time. Specifically, they will track the drone's current location using GPS and notify the customer. The service provider can also provide an interface that allows customers to check the delivery status in real time. For example, they can display the delivery status through a web application or mobile application. This allows customers to check the location of their order in real time. Furthermore, the service provider can collect customer feedback after delivery is complete. For example, they can accept evaluations of delivery speed and quality and use this to improve the system. Based on this information, the service provider can take measures to improve delivery efficiency and customer satisfaction. In addition, the service provider can quickly notify customers and take appropriate action if there are delivery delays or problems. This allows customers to use the service with peace of mind.

[0085] The reception desk can accept takeout orders from restaurants. For example, the reception desk can accept takeout orders in real time from restaurants' online ordering systems. The reception desk can also process takeout orders received through other means such as telephone or email. Furthermore, the reception desk can use AI to automatically classify and process takeout order information. For example, the reception desk can use AI to analyze takeout order information and check the restaurant's inventory status. This allows for efficient processing of restaurant takeout orders. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input takeout order information from restaurants' online ordering systems into a generating AI and have the generating AI classify and process the order information.

[0086] The delivery department can deliver ingredients needed by restaurants from local farms or wholesalers. For example, the delivery department can receive order information from local farms or wholesalers, calculate the optimal delivery route, and control drones to deliver the ingredients. The delivery department can also use AI to optimize delivery routes and achieve efficient delivery. For example, the delivery department can use AI to collect real-time traffic information and calculate the optimal route. This allows restaurants to receive fresh ingredients in a short amount of time. Some or all of the processes described above in the delivery department may be performed using AI, or not. For example, the delivery department can input order information from local farms or wholesalers into a generating AI and have the generating AI perform the calculation of delivery routes and control drones.

[0087] The Promotion Department can handle promotions and events. For example, the Promotion Department can conduct promotions in conjunction with specific campaigns or festivals. Furthermore, the Promotion Department can use AI to analyze the effectiveness of promotions and select the most suitable promotional methods. For instance, the Promotion Department can use AI to analyze past promotional data and propose the most effective promotional methods, making them suitable for use in promotions and events. Some or all of the above-described processes in the Promotion Department may be performed using AI, or not. For example, the Promotion Department can input past promotional data into a generating AI and have the generating AI select promotional methods.

[0088] The tracking unit can enable customers to check the delivery status in real time. For example, the tracking unit can notify customers of the current location and estimated delivery time of the package. The tracking unit can also provide an interface for customers to check the delivery status in real time. For example, the tracking unit can display the delivery status through a web application or a mobile application. This allows customers to check the current location and estimated delivery time of the package in real time. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the current location and estimated delivery time of the package into a generating AI and have the generating AI perform real-time notifications.

[0089] The scheduling unit can optimize delivery schedules. For example, the scheduling unit can use AI to collect real-time traffic information and create an optimal delivery schedule. The scheduling unit can also improve the accuracy of the schedule by referring to past delivery data. For example, the scheduling unit can propose an optimal schedule for a specific time period based on past delivery data. This optimizes the delivery schedule and reduces waiting times for consumers. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input real-time traffic information and past delivery data into a generating AI and have the generating AI create an optimal delivery schedule.

[0090] The reception desk can estimate the user's emotions and customize the order-taking interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick order completion. This improves the efficiency of order taking by providing an interface that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the interface customization.

[0091] The reception desk can analyze past order history and suggest recommended products based on user preferences when taking an order. For example, the reception desk can display related products as recommendations based on products the user has purchased in the past. The reception desk can also suggest products related to specific seasons or events based on the user's past order history. Furthermore, the reception desk can analyze the user's purchase frequency and automatically add regularly purchased items to the cart. This improves order satisfaction by suggesting products based on user preferences. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past order history data into a generating AI and have the generating AI perform the task of suggesting recommended products.

[0092] The reception desk can present the optimal delivery option when receiving an order, taking into account the user's current location. For example, the reception desk can present the delivery option from the nearest distribution center based on the user's current location. The reception desk can also suggest the option that allows for the shortest delivery time based on the user's location. Furthermore, the reception desk can suggest the optimal delivery time slot, taking into account the user's current location and traffic conditions. This improves delivery efficiency by providing the optimal delivery option based on the user's location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current location information into a generating AI and have the generating AI perform the task of presenting the optimal delivery option.

[0093] The reception desk can estimate the user's emotions and determine the order processing priority based on the estimated emotions. For example, if the user is in a hurry, the reception desk can prioritize the order and expedite the delivery process. If the user is relaxed, the reception desk can process the order with the normal priority. Furthermore, if the user is stressed, the reception desk can provide special support to ensure a smooth order process. This improves the efficiency of order processing by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priorities.

[0094] The reception desk can analyze the user's social media activity when receiving an order and present relevant promotions. For example, the reception desk can suggest special promotions based on products the user has shown interest in on social media. It can also analyze the user's social media posts and display relevant products as recommendations. Furthermore, the reception desk can offer special discounts and campaigns considering the user's social media follower count and influence. This improves order satisfaction by providing promotions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the promotion presentation.

[0095] The order processing unit can optimize the interface when receiving an order, taking into account past user feedback. For example, the order processing unit can adjust the interface design based on feedback previously provided by the user. It can also add or remove specific features or options based on user feedback. Furthermore, the order processing unit can analyze user feedback and incorporate improvements to enhance usability. This improves the efficiency of order processing by providing an interface based on user feedback. Some or all of the above processes in the order processing unit may be performed using AI, for example, or not. For example, the order processing unit can input past user feedback data into a generating AI and have the generating AI perform interface optimization.

[0096] The calculation unit can estimate the user's emotions and adjust the delivery route calculation method based on the estimated emotions. For example, if the user is in a hurry, the calculation unit will prioritize calculating the route that will get the user there in the shortest time. The calculation unit can also use the normal route calculation method if the user is relaxed. Furthermore, if the calculation unit is stressed, it can prioritize calculating a route that avoids traffic congestion. This improves delivery efficiency by providing a delivery route that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI or not using AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI adjust the delivery route calculation method.

[0097] The calculation unit can select the optimal route by considering traffic conditions and weather information in real time when calculating delivery routes. For example, the calculation unit calculates the optimal route based on real-time traffic congestion information. The calculation unit can also select a safe route by considering real-time weather information. Furthermore, the calculation unit can suggest detour routes based on real-time road construction information. This improves delivery efficiency by providing the optimal route based on real-time information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input real-time traffic conditions and weather information into a generating AI and have the generating AI perform the selection of the optimal route.

[0098] The calculation unit can improve the accuracy of delivery routes by referring to past delivery data when calculating delivery routes. For example, the calculation unit calculates the most efficient route based on past delivery data. The calculation unit can also suggest the optimal route for a specific time period based on past delivery data. Furthermore, the calculation unit can analyze past delivery data and improve the route calculation algorithm. This improves delivery accuracy by providing routes based on past data. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past delivery data into a generating AI and have the generating AI perform route accuracy improvements.

[0099] The calculation unit can estimate the user's emotions and determine the priority of delivery routes based on the estimated emotions. For example, if the user is in a hurry, the calculation unit will set a higher priority for the delivery route. If the user is relaxed, the calculation unit can also set a normal priority for the delivery route. Furthermore, if the user is stressed, the calculation unit can provide special consideration and adjust the priority accordingly. This improves delivery efficiency by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, or not using AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI determine the priority of delivery routes.

[0100] The calculation unit can optimize delivery routes by considering geographical characteristics. For example, the calculation unit calculates the optimal route by considering the topography. It can also select efficient routes by considering the characteristics of urban and suburban areas. Furthermore, the calculation unit can suggest routes that avoid geographical obstacles. This improves delivery efficiency by providing routes based on geographical characteristics. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input geographical characteristic data into a generating AI and have the generating AI perform route optimization.

[0101] The calculation unit can adjust delivery routes by considering the operational status of other drones when calculating delivery routes. For example, the calculation unit can monitor the operational status of other drones in real time and calculate routes that avoid collisions. The calculation unit can also select efficient routes by considering the operational schedules of other drones. Furthermore, the calculation unit can communicate with other drones and coordinate route adjustments. This improves delivery efficiency by providing routes based on the operational status of other drones. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input operational status data of other drones into a generating AI and have the generating AI perform route adjustments.

[0102] The control unit can estimate the user's emotions and adjust the drone's flight pattern based on the estimated emotions. For example, if the user is in a hurry, the control unit can set a flight pattern that will get the destination in the shortest time. The control unit can also use a normal flight pattern if the user is relaxed. Furthermore, if the user is stressed, the control unit can prioritize a stable flight pattern. This improves delivery efficiency by providing a flight pattern that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI adjust the flight pattern.

[0103] The control unit can ensure a safe flight path using an obstacle detection system when controlling the drone. For example, the control unit can detect obstacles while the drone is in flight and automatically avoid them. The control unit can also set the optimal flight altitude using the obstacle detection system. Furthermore, the control unit can monitor the obstacle detection system in real time and maintain a safe flight path. In this way, a safe flight path can be provided by using the obstacle detection system. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input obstacle detection data into a generating AI and have the generating AI perform the task of ensuring a safe flight path.

[0104] The control unit can monitor the battery level in real time during drone control and select the optimal charging point. For example, if the drone's battery level is low, the control unit can suggest the nearest charging point. The control unit can also monitor the battery level in real time and set an efficient charging schedule. Furthermore, the control unit can adjust the flight route according to the battery level and head towards the charging point. This improves flight efficiency by providing the optimal charging point based on the battery level. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input battery level data into a generating AI and have the generating AI select the optimal charging point.

[0105] The control unit can estimate the user's emotions and adjust the drone's flight speed based on the estimated emotions. For example, if the user is in a hurry, the control unit can increase the drone's flight speed. It can also maintain a normal flight speed if the user is relaxed. Furthermore, if the user is stressed, the control unit can prioritize a stable flight speed. This improves delivery efficiency by providing a flight speed that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input user emotion data into a generative AI and have the generative AI adjust the flight speed.

[0106] The control unit can communicate with other drones during drone control to achieve coordinated flight. For example, the control unit can communicate with other drones and adjust flight routes to avoid collisions. The control unit can also achieve coordinated flight with other drones to enable efficient delivery. Furthermore, the control unit can monitor the operational status of other drones in real time and optimize flight routes. This improves delivery efficiency by enabling coordinated flight with other drones. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input operational status data of other drones into a generating AI and have the generating AI perform the implementation of coordinated flight.

[0107] The control unit can collect environmental data during flight while controlling the drone and utilize it for the next flight. For example, the control unit can optimize the next flight route based on the environmental data collected during flight. The control unit can also collect weather data during flight and reflect it in the next flight plan. Furthermore, the control unit can collect obstacle data during flight and set avoidance routes for the next flight. In this way, the efficiency of the next flight is improved by utilizing the environmental data during flight. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the environmental data during flight into a generating AI and have the generating AI perform the optimization of the next flight plan.

[0108] The delivery unit can estimate the user's emotions and adjust the delivery status notification method based on the estimated emotions. For example, if the user is in a hurry, the delivery unit will notify the user of the delivery status more frequently. If the user is relaxed, the delivery unit can also provide delivery status at a normal notification frequency. Furthermore, if the user is stressed, the delivery unit can provide detailed delivery status to give them a sense of security. This makes it easier to check the delivery status by providing notification methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0109] The service provider can improve prediction accuracy by referring to past delivery data when providing delivery status. For example, the service provider can improve the accuracy of delivery time predictions based on past delivery data. The service provider can also analyze trends in delivery delays during specific time periods from past delivery data and reflect this in the predictions. Furthermore, the service provider can analyze past delivery data and improve the prediction algorithm. This improves delivery efficiency by providing prediction accuracy based on past data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past delivery data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0110] The delivery unit can customize notification content when providing delivery status, taking into account the user's current location information. For example, the delivery unit can notify the user of the delivery status from the nearest delivery center based on the user's current location. The delivery unit can also notify the user of the shortest estimated arrival time based on the user's location information. Furthermore, the delivery unit can notify the user of the optimal delivery time slot, taking into account the user's current location and traffic conditions. This improves delivery efficiency by providing notification content based on the user's location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's current location information into a generating AI and have the generating AI perform the customization of the notification content.

[0111] The delivery unit can estimate the user's emotions and adjust the frequency of notifications based on the estimated emotions. For example, if the user is in a hurry, the delivery unit will notify the user of the delivery status more frequently. If the user is relaxed, the delivery unit can also provide delivery status at a normal notification frequency. Furthermore, if the user is stressed, the delivery unit can provide detailed delivery status to give them a sense of security. This makes it easier to check the delivery status by providing notification frequencies that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the notification frequency.

[0112] The service provider can select the optimal notification method when providing delivery status, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide delivery status using push notifications. Furthermore, if the user is using a tablet, the service provider can provide a notification method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible notification method. This makes it easier to check delivery status by providing notification methods based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0113] The service provider can optimize notification content by considering the user's past feedback when providing delivery status. For example, the service provider can adjust notification content based on feedback previously provided by the user. The service provider can also add or remove specific information from the user's feedback. Furthermore, the service provider can analyze user feedback and incorporate improvements to enhance usability. This makes it easier to check the delivery status by providing notifications based on user feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI perform the optimization of notification content.

[0114] The delivery unit can estimate the user's emotions and adjust the delivery method based on those emotions. For example, if the user is in a hurry, the delivery unit will select the delivery method that will get them there in the shortest time. If the user is relaxed, the delivery unit can also use a standard delivery method. Furthermore, if the user is stressed, the delivery unit can take special care and adjust the delivery method accordingly. This improves delivery efficiency by providing a delivery method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the delivery method.

[0115] The delivery department can collect environmental information about the delivery destination in real time during delivery and select the optimal delivery method. For example, the delivery department can collect weather information about the delivery destination in real time and select a safe delivery method. It can also collect traffic conditions at the delivery destination in real time and select an efficient delivery method. Furthermore, the delivery department can select the optimal delivery method considering the geographical characteristics of the delivery destination. This improves delivery efficiency by providing the optimal delivery method based on environmental information about the delivery destination. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input environmental information about the delivery destination into a generating AI and have the generating AI select the optimal delivery method.

[0116] The delivery department can check the delivery status at the recipient's location in real time and select the optimal delivery timing. For example, the delivery department can check the delivery status at the recipient's location in real time and select a time when delivery is possible. The delivery department can also suggest the optimal delivery time based on the recipient's location

[0117] The delivery unit can estimate the user's emotions and determine delivery priorities based on those emotions. For example, if the user is in a hurry, the delivery unit will set a higher priority. Conversely, if the user is relaxed, the delivery unit can deliver with the normal priority. Furthermore, if the user is stressed, the delivery unit can provide special consideration and adjust the priority accordingly. This improves delivery efficiency by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI determine the delivery priority.

[0118] The delivery department can achieve efficient delivery by coordinating with other delivery methods. For example, the delivery department can select the optimal delivery route in cooperation with other delivery methods. Furthermore, the delivery department can set an efficient delivery schedule through cooperation with other delivery methods. In addition, the delivery department can monitor the operational status of other delivery methods in real time and adjust delivery routes accordingly. This improves delivery efficiency through coordination with other delivery methods. Some or all of the above processes in the delivery department may be performed using AI, or not. For example, the delivery department can input operational status data of other delivery methods into a generating AI and have the generating AI execute the task of achieving efficient delivery.

[0119] The delivery department can provide customized delivery methods at the time of delivery, according to the specific requirements of the delivery destination. For example, the delivery department can provide special packaging methods according to the specific requirements of the delivery destination. The delivery department can also deliver at specific time slots according to the specific requirements of the delivery destination. Furthermore, the delivery department can use special delivery means according to the specific requirements of the delivery destination. This improves delivery efficiency by providing delivery methods tailored to the specific requirements of the delivery destination. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input specific request data from the delivery destination into a generating AI and have the generating AI perform the task of providing customized delivery methods.

[0120] The promotion department can estimate the user's emotions and customize promotional content based on those emotions. For example, if the user is relaxed, the promotion department can provide detailed promotional content. If the user is in a hurry, the promotion department can provide concise promotional content. Furthermore, if the user is stressed, the promotion department can offer special discounts or campaigns. This improves the effectiveness of promotions by providing promotional content tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the promotion department may be performed using AI or not. For example, the promotion department can input user emotion data into a generative AI and have the generative AI customize the promotional content.

[0121] The promotion department can select effective promotional methods by referring to past promotional data during promotions. For example, the promotion department can select the most effective promotional method based on past promotional data. The promotion department can also propose promotional methods related to specific seasons or events based on past promotional data. Furthermore, the promotion department can analyze past promotional data and reflect areas for improvement in promotional methods. This improves the effectiveness of promotions by providing promotional methods based on past data. Some or all of the above processes in the promotion department may be performed using AI, for example, or not. For example, the promotion department can input past promotional data into a generating AI and have the generating AI select effective promotional methods.

[0122] The promotion department can customize promotional content by taking into account the user's current location during a promotion. For example, the promotion department can provide promotional content for the nearest store based on the user's current location. It can also provide promotional content related to a specific region based on the user's location. Furthermore, the promotion department can provide optimal promotional content by considering the user's current location and traffic conditions. This improves the effectiveness of promotions by providing promotional content based on the user's location. Some or all of the above processing in the promotion department may be performed using AI, for example, or without AI. For example, the promotion department can input the user's current location information into a generating AI and have the generating AI customize the promotional content.

[0123] The promotion department can estimate the user's emotions and adjust the timing of promotions based on those emotions. For example, if the user is relaxed, the promotion department can provide detailed promotional content. If the user is in a hurry, the promotion department can provide concise promotional content. Furthermore, if the user is stressed, the promotion department can offer special discounts or campaigns. This improves the effectiveness of promotions by providing timing that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the promotion department may be performed using AI or not. For example, the promotion department can input user emotion data into a generative AI and have the generative AI adjust the timing of promotions.

[0124] The promotion department can analyze users' social media activity during promotions and present relevant promotions. For example, the promotion department can suggest special promotions based on products that users have shown interest in on social media. The promotion department can also analyze the content of users' social media posts and display relevant products as recommendations. Furthermore, the promotion department can offer special discounts and campaigns considering the number of followers and influence of users on social media. This improves the effectiveness of promotions by providing promotions based on users' social media activity. Some or all of the above processes in the promotion department may be performed using AI, for example, or not. For example, the promotion department can input user social media activity data into a generating AI and have the generating AI perform the task of presenting relevant promotions.

[0125] The promotion department can optimize promotional content by considering past user feedback during the promotion process. For example, the promotion department can adjust promotional content based on feedback previously provided by users. The promotion department can also add or remove specific information from user feedback. Furthermore, the promotion department can analyze user feedback and reflect improvements to the promotional content. This improves the effectiveness of promotions by providing promotional content based on user feedback. Some or all of the above processes in the promotion department may be performed using AI, for example, or not. For example, the promotion department can input past user feedback data into a generating AI and have the generating AI perform the optimization of the promotional content.

[0126] The tracking unit can estimate the user's emotions and adjust how tracking information is displayed based on the estimated emotions. For example, if the user is in a hurry, the tracking unit can provide concise and easily visible tracking information. It can also provide detailed tracking information if the user is relaxed. Furthermore, if the user is stressed, the tracking unit can provide detailed tracking information to provide reassurance. This improves tracking efficiency by providing tracking information tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input user emotion data into the generative AI and have the generative AI adjust how tracking information is displayed.

[0127] The tracking unit can improve tracking accuracy by referring to past tracking data during tracking. For example, the tracking unit improves tracking accuracy based on past tracking data. The tracking unit can also analyze the trend of tracking accuracy in a specific time period from past tracking data and reflect it in tracking. Furthermore, the tracking unit can analyze past tracking data and improve the tracking algorithm. This improves tracking efficiency by providing tracking accuracy based on past data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input past tracking data into a generating AI and have the generating AI perform the improvement of tracking accuracy.

[0128] The tracking unit can improve tracking accuracy based on real-time location information during tracking. For example, the tracking unit can accurately display the current delivery status based on real-time location information. The tracking unit can also accurately predict the estimated delivery time based on real-time location information. Furthermore, the tracking unit can immediately reflect changes in the delivery route based on real-time location information. This improves tracking efficiency by providing tracking accuracy based on real-time location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input real-time location information into a generating AI and have the generating AI perform the task of improving tracking accuracy.

[0129] The tracking unit can estimate the user's emotions and adjust the frequency of tracking information notifications based on the estimated emotions. For example, if the user is in a hurry, the tracking unit will send tracking information frequently. If the user is relaxed, the tracking unit can also provide tracking information at a normal notification frequency. Furthermore, if the user is stressed, the tracking unit can send detailed tracking information frequently. This improves the efficiency of tracking by providing notification frequencies that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI adjust the notification frequency.

[0130] The tracking unit can select the optimal tracking method during tracking, taking into account the user's device information. For example, if the user is using a smartphone, the tracking unit can provide tracking information using push notifications. Furthermore, if the user is using a tablet, the tracking unit can provide a tracking method optimized for a larger screen. Additionally, if the user is using a smartwatch, the tracking unit can provide a concise and highly visible tracking method. This improves tracking efficiency by providing a tracking method based on the user's device information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's device information into a generating AI and have the generating AI select the optimal tracking method.

[0131] The tracking unit can optimize tracking information by considering the user's past feedback during tracking. For example, the tracking unit can adjust how tracking information is displayed based on feedback previously provided by the user. The tracking unit can also add or remove specific information from the user's feedback. Furthermore, the tracking unit can analyze the user's feedback and reflect improvements to enhance usability. This improves tracking efficiency by providing tracking information based on user feedback. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past feedback data into a generating AI and have the generating AI perform the optimization of tracking information.

[0132] The scheduling unit can estimate the user's emotions and adjust the delivery schedule based on those emotions. For example, if the user is in a hurry, the scheduling unit can set a schedule that will arrive in the shortest possible time. The scheduling unit can also use a normal schedule if the user is relaxed. Furthermore, if the user is stressed, the scheduling unit can provide special consideration and adjust the schedule accordingly. This improves delivery efficiency by providing a delivery schedule that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI adjust the delivery schedule.

[0133] The scheduling unit can create the optimal schedule by referring to past schedule data when creating a schedule. For example, the scheduling unit can create the most efficient schedule based on past schedule data. The scheduling unit can also suggest the optimal schedule for a specific time period based on past schedule data. Furthermore, the scheduling unit can analyze past schedule data and improve the schedule creation algorithm. This improves delivery efficiency by providing schedules based on past data. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input past schedule data into a generation AI and have the generation AI create the optimal schedule.

[0134] The scheduling unit can optimize schedules by considering real-time traffic information when creating them. For example, the scheduling unit can create an optimal schedule based on real-time traffic congestion information. The scheduling unit can also set a safe schedule by considering real-time weather information. Furthermore, the scheduling unit can propose a schedule that includes detour routes based on real-time road construction information. This improves delivery efficiency by providing schedules based on real-time information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input real-time traffic information into a generation AI and have the generation AI perform schedule optimization.

[0135] The scheduling unit can estimate the user's emotions and determine schedule priorities based on those emotions. For example, if the user is in a hurry, the scheduling unit will set a higher priority for that schedule. If the user is relaxed, the scheduling unit can set a normal priority for that schedule. Furthermore, if the user is stressed, the scheduling unit can provide special consideration and adjust the priorities accordingly. This improves delivery efficiency by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI determine the schedule priorities.

[0136] The scheduling unit can adjust the schedule when creating it, taking into account the operational status of other drones. For example, the scheduling unit can monitor the operational status of other drones in real time and create a schedule that avoids collisions. The scheduling unit can also set an efficient schedule by taking into account the operational schedules of other drones. Furthermore, the scheduling unit can communicate with other drones and coordinate schedule adjustments. This improves delivery efficiency by providing a schedule based on the operational status of other drones. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input operational status data of other drones into a generating AI and have the generating AI perform schedule adjustments.

[0137] The scheduling unit can create schedules based on the user's calendar information when creating a schedule. For example, the scheduling unit can automatically set a schedule by referring to appointments registered in the user's calendar. The scheduling unit can also suggest schedules related to specific events based on the user's calendar information. Furthermore, the scheduling unit can create an optimal schedule tailored to the user's appointments based on the user's calendar information. This improves delivery efficiency by providing schedules based on the user's appointments. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the user's calendar information into a generation AI and have the generation AI create a schedule based on appointments.

[0138] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0139] The reception desk can estimate the user's emotions and customize the order-taking interface based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick order completion. This improves the efficiency of order taking by providing an interface that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the interface customization.

[0140] The reception desk can analyze past order history and suggest recommended products based on user preferences when taking an order. For example, it can display related products as recommendations based on products the user has purchased in the past. It can also suggest products related to specific seasons or events based on the user's past order history. Furthermore, it can analyze the user's purchase frequency and automatically add regularly purchased items to the cart. This improves order satisfaction by suggesting products based on user preferences. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past order history data into a generating AI and have the generating AI perform the task of suggesting recommended products.

[0141] The reception desk can present the optimal delivery option when receiving an order, taking into account the user's current location. For example, it can present the delivery option from the nearest distribution center based on the user's current location. It can also suggest the option that allows for the shortest delivery time based on the user's location. Furthermore, it can suggest the optimal delivery time slot, taking into account the user's current location and traffic conditions. This improves delivery efficiency by providing the optimal delivery option based on the user's location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current location information into a generating AI and have the generating AI perform the task of presenting the optimal delivery option.

[0142] The reception desk can estimate the user's emotions and determine the order processing priority based on the estimated emotions. For example, if the user is in a hurry, the order can be processed preferentially and delivered quickly. If the user is relaxed, the order can be processed with the normal priority. Furthermore, if the user is stressed, special support can be provided to ensure the order process runs smoothly. This improves the efficiency of order processing by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priorities.

[0143] The order processing department can analyze the user's social media activity when receiving an order and present relevant promotions. For example, it can suggest special promotions based on products the user has shown interest in on social media. It can also analyze the user's social media posts and display relevant products as recommendations. Furthermore, it can offer special discounts and campaigns considering the user's number of social media followers and influence. This improves order satisfaction by providing promotions based on the user's social media activity. Some or all of the above processing in the order processing department may be performed using AI, for example, or not. For example, the order processing department can input the user's social media activity data into a generating AI and have the generating AI perform the promotion presentation.

[0144] The calculation unit can estimate the user's emotions and adjust the delivery route calculation method based on the estimated emotions. For example, if the user is in a hurry, the calculation unit prioritizes the route that will get them there in the shortest time. If the user is relaxed, the calculation unit can use the normal route calculation method. Furthermore, if the user is stressed, the calculation unit can prioritize a route that avoids traffic congestion. This improves delivery efficiency by providing a delivery route that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or not using AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI adjust the delivery route calculation method.

[0145] The calculation unit can select the optimal route by considering traffic conditions and weather information in real time when calculating delivery routes. For example, it can calculate the optimal route based on real-time traffic congestion information. It can also select a safe route by considering real-time weather information. Furthermore, it can suggest detour routes based on real-time road construction information. This improves delivery efficiency by providing the optimal route based on real-time information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input real-time traffic conditions and weather information into a generating AI and have the generating AI perform the selection of the optimal route.

[0146] The control unit can estimate the user's emotions and adjust the drone's flight pattern based on the estimated emotions. For example, if the user is in a hurry, it can set a flight pattern that will get them there in the shortest time. If the user is relaxed, it can use a normal flight pattern. Furthermore, if the user is stressed, it can prioritize a stable flight pattern. This improves delivery efficiency by providing a flight pattern that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input user emotion data into a generative AI and have the generative AI adjust the flight pattern.

[0147] The control unit can use an obstacle detection system to ensure a safe flight path when controlling the drone. For example, the drone can detect obstacles during flight and automatically avoid them. It can also use the obstacle detection system to set the optimal flight altitude. Furthermore, it can monitor the obstacle detection system in real time to maintain a safe flight path. In this way, a safe flight path can be provided by using the obstacle detection system. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input obstacle detection data into a generating AI and have the generating AI perform the task of ensuring a safe flight path.

[0148] The delivery unit can estimate the user's emotions and adjust the delivery status notification method based on the estimated emotions. For example, if the user is in a hurry, it can provide frequent delivery status notifications. If the user is relaxed, it can provide delivery status at a normal notification frequency. Furthermore, if the user is stressed, it can provide detailed delivery status to give them a sense of security. This makes it easier to check the delivery status by providing notification methods that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0149] The following briefly describes the processing flow for example form 2.

[0150] Step 1: The reception desk receives order information. This information includes product type, quantity, and shipping address. The reception desk can receive order information from online shopping sites in real time, as well as order information received through other means such as phone and email. Furthermore, the reception desk can use AI to automatically classify and process order information. For example, the reception desk can use AI to analyze order information and check product inventory status. Step 2: The calculation unit calculates the optimal delivery route based on the information received by the reception unit. The optimal delivery route is calculated based on criteria such as distance, time, and traffic conditions. The calculation unit uses AI to collect real-time traffic information and calculate the optimal route. The calculation unit can also improve the accuracy of the route by referring to past delivery data. For example, the calculation unit can suggest the optimal route for a specific time period based on past delivery data. Step 3: The control unit controls the drone based on the route calculated by the calculation unit. The control unit adjusts the drone's flight pattern and speed. The control unit can also ensure a safe flight path using an obstacle detection system. For example, the control unit can detect obstacles while the drone is flying and automatically avoid them. Step 4: The service provider provides real-time delivery status. The service provider notifies the customer of the current location of the delivery and the estimated delivery time. The service provider can also provide an interface that allows the customer to check the delivery status in real time. For example, the service provider can display the delivery status through a web application or a mobile application.

[0151] 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.

[0152] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0153] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, calculation unit, control unit, and delivery unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives order information from online shopping sites in real time. The calculation unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the optimal delivery route. The control unit is implemented by the control unit 46A of the smart device 14 and adjusts the flight pattern and speed of the drone. The delivery unit is implemented by the control unit 46A of the smart device 14 and notifies the customer of the current location of the delivery and the estimated delivery time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0156] 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.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0158] 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.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0160] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] 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.

[0162] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the reception unit, calculation unit, control unit, and delivery unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives order information from online shopping sites in real time. The calculation unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the optimal delivery route. The control unit is implemented by the control unit 46A of the smart glasses 214 and adjusts the flight pattern and speed of the drone. The delivery unit is implemented by the control unit 46A of the smart glasses 214 and notifies the customer of the current location of the delivery and the estimated delivery time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0172] 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.

[0173] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0174] 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.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0176] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0177] 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.

[0178] 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.

[0179] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0180] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0181] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0182] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0185] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0186] Each of the multiple elements described above, including the reception unit, calculation unit, control unit, and delivery unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives order information from online shopping sites in real time. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the optimal delivery route. The control unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the drone's flight pattern and speed. The delivery unit is implemented by the control unit 46A of the headset terminal 314 and notifies the customer of the current location of the delivery and the estimated delivery time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0187] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0190] 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.

[0191] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0192] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0193] 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.

[0194] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0195] 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.

[0196] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0197] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0198] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0199] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0200] 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.

[0201] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0202] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0203] Each of the multiple elements described above, including the reception unit, calculation unit, control unit, and delivery unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives order information from online shopping sites in real time. The calculation unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the optimal delivery route. The control unit is implemented by the control unit 46A of the robot 414 and adjusts the drone's flight pattern and speed. The delivery unit is implemented by the control unit 46A of the robot 414 and notifies the customer of the current location of the delivery and the estimated delivery time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0204] 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.

[0205] Figure 9 shows the 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.

[0206] 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.

[0207] 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.

[0208] 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, and motorcycles, 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 based, for example, 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.

[0209] 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."

[0210] 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.

[0211] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0220] 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 other things 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.

[0221] 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.

[0222] (Note 1) The reception desk that receives order information, A calculation unit that calculates the optimal delivery route based on the information received by the reception unit, A control unit that controls the drone based on the route calculated by the calculation unit, It includes a service section that provides delivery status in real time. A system characterized by the following features. (Note 2) It has a reception area for taking takeout orders from restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 3) The restaurant has a delivery department that handles deliveries from local farms or wholesalers that provide the necessary ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a promotions department to handle use for promotions and events. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a tracking unit that allows customers to check the delivery status in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) Features a scheduling unit to optimize delivery schedules. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and customizes the order acceptance interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is By analyzing past order history, the system recommends products based on user preferences when taking an order. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When an order is placed, the system will consider the user's current location to suggest the most suitable shipping option. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the order acceptance priority based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When an order is placed, the system analyzes the user's social media activity and presents relevant promotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When taking an order, the interface is optimized based on the user's past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 13) The calculation unit, The system estimates the user's emotions and adjusts the delivery route calculation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The calculation unit, When calculating delivery routes, the system selects the optimal route by considering traffic conditions and weather information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 15) The calculation unit, When calculating delivery routes, we improve route accuracy by referencing past delivery data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit, The system estimates the user's emotions and prioritizes delivery routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit, When calculating delivery routes, the route is optimized by taking geographical characteristics into account. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit, When calculating delivery routes, the route is adjusted to take into account the operating status of other drones. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, The system estimates the user's emotions and adjusts the drone's flight pattern based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, When controlling a drone, an obstacle detection system is used to ensure a safe flight path. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, During drone control, the system monitors battery level in real time and selects the optimal charging point. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, The system estimates the user's emotions and adjusts the drone's flight speed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, During drone control, communication with other drones is performed to enable coordinated flight. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, During drone control, environmental data is collected during flight and used for future flights. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the delivery status notification method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing delivery status information, we improve prediction accuracy by referring to past delivery data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing delivery status information, the notification content will be customized based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the frequency of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing delivery status, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing delivery status updates, we optimize notification content by taking into account the user's past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned delivery department, The system estimates the user's emotions and adjusts the delivery method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned delivery department, During delivery, environmental information of the delivery destination is collected in real time to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned delivery department, During delivery, the system checks the delivery status at the recipient's address in real time and selects the optimal delivery time. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned delivery department, The system estimates the user's emotions and determines delivery priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned delivery department, During delivery, we aim to achieve efficient delivery by coordinating with other delivery methods. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned delivery department, During delivery, we provide customized delivery methods according to the specific requirements of the delivery destination. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned promotion department, It estimates user sentiment and customizes promotional content based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned promotion department, During promotions, we select effective promotional methods by referring to past promotional data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned promotion department, During promotions, customize the promotion content by taking into account the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned promotion department, We estimate user sentiment and adjust the timing of promotions based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned promotion department, During promotions, analyze users' social media activity and present relevant promotions. The system according to Appendix 1, characterized in that... (Appendix 42) The promotion unit optimizes the promotion content in consideration of the user's past feedback during promotion. The system according to Appendix 1, characterized in that... (Appendix 43) The tracking unit estimates the user's emotion and adjusts the display method of the tracking information based on the estimated user emotion. The system according to Appendix 1, characterized in that... (Appendix 44) The tracking unit improves the tracking accuracy by referring to past tracking data during tracking. The system according to Appendix 1, characterized in that... (Appendix 45) The tracking unit improves the tracking accuracy based on real-time position information during tracking. The system according to Appendix 1, characterized in that... (Appendix 46) The tracking unit estimates the user's emotion and adjusts the notification frequency of the tracking information based on the estimated user emotion. The system according to Appendix 1, characterized in that... (Appendix 47) <00C0901>The tracking unit selects an optimal tracking method by considering the user's device information during tracking. The system according to Appendix 1, characterized in that... (Appendix 48) The tracking unit optimizes the tracking information by considering the user's past feedback during tracking. The system according to Appendix 1, characterized in that... (Appendix 49) The scheduling unit estimates the user's emotion and adjusts the delivery schedule based on the estimated user emotion. The system according to Appendix 1, characterized in that... (Note 50) The aforementioned scheduling unit is When creating a schedule, refer to past schedule data to create the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 51) The aforementioned scheduling unit is When creating a schedule, optimize it by taking real-time traffic information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned scheduling unit is It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned scheduling unit is When creating a schedule, adjust it while taking into account the operating status of other drones. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned scheduling unit is When creating a schedule, the system references the user's calendar information to create a schedule based on their appointments. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk that receives order information, A calculation unit that calculates the optimal delivery route based on the information received by the reception unit, A control unit that controls the drone based on the route calculated by the calculation unit, It includes a service section that provides delivery status in real time. A system characterized by the following features.

2. The aforementioned reception unit is Accepting takeout orders from restaurants. The system according to feature 1.

3. The restaurant has a delivery department that handles deliveries from local farms or wholesalers that provide the necessary ingredients. The system according to feature 1.

4. We have a promotions department to handle use for promotions and events. The system according to feature 1.

5. It features a tracking unit that allows customers to check the delivery status in real time. The system according to feature 1.

6. Features a scheduling unit to optimize delivery schedules. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and customizes the order acceptance interface based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is By analyzing past order history, the system recommends products based on user preferences when taking an order. The system according to feature 1.

9. The aforementioned reception unit is When an order is placed, the system will consider the user's current location to suggest the most suitable shipping option. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and determines the order acceptance priority based on those estimated emotions. The system according to feature 1.

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