System

Abstract

An object of a system according to an embodiment is to deliver articles quickly and efficiently while overcoming traffic congestion and transportation constraints.SOLUTION: A system according to an embodiment includes a drone, a delivery unit, and a support unit. The drone delivers the article. The delivery unit delivers the article by the drone. The support unit supports living at home using the article delivered by the delivery unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, traffic congestion and transportation restrictions made it difficult to quickly deliver supplies and medicines needed for nursing care and medical treatment.

[0005] The system according to the embodiment aims to deliver goods quickly and efficiently, overcoming traffic congestion and transportation constraints. [Means for solving the problem]

[0006] A system according to an embodiment includes a drone, a delivery unit, and a support unit. The drone delivers an item. The delivery unit delivers the item by the drone. The support unit supports at-home living using the item delivered by the delivery unit. [Effects of the Invention]

[0007] The system according to the embodiment can deliver goods quickly and efficiently, overcoming traffic congestion and transportation constraints. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A delivery system according to an embodiment of the present invention is a system that uses drones to quickly deliver items and medicines necessary for nursing care and medical treatment. This enables the delivery system to overcome traffic congestion and transportation constraints and deliver items quickly and efficiently.

[0029] A delivery system according to an embodiment includes a drone, a delivery unit, and a support unit. The drone delivers items and medicines necessary for nursing care and medical treatment. For example, the drone can deliver medicines from a pharmacy to an elderly person's home. The drone can also periodically deliver nursing care supplies and items necessary for daily life. The drone can deliver items while avoiding traffic congestion. For example, the drone flies through the air to avoid traffic congestion in urban areas and delivers items quickly. The delivery unit supports home life using items delivered by the drone. For example, the delivery unit hands over delivered medicines to the elderly. The delivery unit can also install delivered nursing care supplies at the elderly's home. The delivery unit can also provide delivered items necessary for daily life to the elderly. The support unit provides services to support home life. For example, the support unit can provide health management services. The support unit can also provide rehabilitation support services. The support unit can also provide life assistance services. This enables the delivery system according to an embodiment to quickly and efficiently deliver items and support home life. For example, drones can be used to overcome traffic congestion and geographical constraints and deliver goods quickly, while support functions can be used to support elderly people and people with disabilities living at home.

[0030] Drones can fly loaded with medicine and deliver it to a specified location. For example, drones can fly loaded with medicine from a pharmacy to an elderly person's home and deliver it to the specified location. For example, drones can land precisely at a specified location based on GPS coordinates. Drones can also deliver medicine to a specified address. For example, drones can deliver medicine to a specified location based on address information. Furthermore, drones can avoid obstacles when delivering medicine to a specified location. For example, drones can detect obstacles during flight and automatically avoid them to deliver the medicine to the specified location. This enables rapid delivery of medicine.

[0031] Drones can deliver nursing care supplies and items necessary for daily life on a regular basis. Drones, for example, regularly deliver nursing care supplies such as nursing beds and wheelchairs. Drones can also regularly deliver items necessary for daily life. For example, drones regularly deliver items necessary for daily life such as food and household goods. Drones can also set regular delivery schedules. For example, drones deliver nursing care supplies and items necessary for daily life on a specific day of the week each week. This makes it possible to regularly deliver nursing care supplies and items necessary for daily life.

[0032] Drones can deliver goods by avoiding traffic congestion. For example, drones can deliver goods by avoiding traffic congestion in urban areas. For example, drones can fly in the air to avoid traffic congestion. Drones can also avoid congestion on specific roads. For example, drones can deliver goods by avoiding roads with heavy traffic congestion. Furthermore, drones can monitor traffic congestion conditions in real time. For example, drones can select the optimal route based on traffic congestion information and deliver goods. This allows for quicker delivery by avoiding traffic congestion.

[0033] Drones can deliver goods by overcoming geographical constraints. For example, drones can deliver goods to areas that are difficult to access, such as mountainous regions or remote islands. For example, drones can deliver goods to the homes of elderly people in mountainous regions. Drones can also deliver goods to residents of remote islands. For example, drones can deliver medical supplies and items necessary for daily life to residents of remote islands. Furthermore, drones can optimize flight routes to overcome geographical constraints. For example, drones can select the optimal flight route taking into account the topography of mountainous regions or remote islands to deliver goods. This enables quick delivery by overcoming geographical constraints.

[0034] Drones can quickly deliver needed medicines and medical supplies in emergencies. For example, drones can quickly deliver needed medicines and medical supplies in disasters. For example, drones can quickly supply needed first aid kits and medicines in disasters. Drones can also quickly deliver needed medicines and medical supplies in medical emergencies. For example, drones can quickly supply needed first aid kits and medicines in medical emergencies. Drones can also optimize flight routes to quickly deliver needed medicines and medical supplies in emergencies. For example, drones can select the optimal flight route to quickly deliver needed medicines and medical supplies in emergencies. This enables the quick delivery of medicines and medical supplies in emergencies.

[0035] Drones can use sensors to scan the delivery destination environment in real time and automatically select the optimal landing spot. For example, drones can use LIDAR sensors to scan the destination's terrain and obstacles in real time and select the optimal landing spot. For example, drones can automatically determine safe locations such as yards or parking lots. Drones can also combine cameras and image analysis technology to monitor the destination environment in real time and select the optimal landing spot. For example, drones can identify safe locations such as in front of the recipient's door or on a balcony. Furthermore, drones can use ultrasonic sensors to measure the ground level and obstacles at the destination in real time and select the optimal landing spot. For example, drones can avoid uneven ground and obstacles to land safely. This allows drones to scan the destination environment in real time and automatically select the optimal landing spot.

[0036] To optimize drone flight routes, generative AI can analyze weather data and traffic conditions in real time and calculate the shortest route. For example, generative AI analyzes weather data in real time and calculates the shortest route taking wind speed and rainfall into account. For example, generative AI selects routes that avoid strong winds and heavy rain. Generative AI can also analyze traffic data in real time and optimize drone flight routes. For example, generative AI calculates the optimal aerial route to avoid traffic congestion and road construction. Furthermore, generative AI can integrate weather data and traffic data to optimize drone flight routes. For example, generative AI adjusts routes in real time depending on weather changes and traffic conditions. This allows for the analysis of weather data and traffic conditions in real time and the calculation of the shortest route.

[0037] Drones can be used to regularly deliver maintenance parts and consumables for medical equipment, improving the uptime of medical equipment. For example, drones can regularly deliver maintenance parts for medical equipment. For example, drones can regularly supply parts needed for medical equipment in hospitals. Drones can also regularly deliver consumables for medical equipment. For example, drones can regularly supply reagents for blood tests and disinfectants. Drones can also quickly deliver maintenance parts and consumables for medical equipment. For example, drones can quickly supply parts for broken equipment. This enables regular deliveries to improve the uptime of medical equipment.

[0038] A camera mounted on a drone can be used to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, a drone can use a high-resolution camera to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered correctly. A drone can also use a 360-degree camera to capture a panoramic view of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered safely. Furthermore, a drone can use a night vision camera to capture images of the surrounding environment of the delivery destination at night and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered correctly even at night. This allows the accuracy of the delivery to be confirmed by capturing images of the surrounding environment of the delivery destination and providing confirmation images to the recipient after delivery is complete.

[0039] The drone can monitor the recipient's health condition using sensors mounted on the drone, and issue an emergency notification if an abnormality is detected. For example, the drone can monitor the recipient's heart rate using a heart rate sensor, and issue an emergency notification if an abnormality is detected. For example, the drone can send an alert if the heart rate is abnormally high. The drone can also monitor the recipient's body temperature using a body temperature sensor, and issue an emergency notification if an abnormality is detected. For example, the drone can send an alert if the body temperature is abnormally high. The drone can also monitor the recipient's blood pressure using a blood pressure sensor, and issue an emergency notification if an abnormality is detected. For example, the drone can send an alert if the blood pressure is abnormally high. In this way, the recipient's health condition can be monitored, and an emergency notification can be issued if an abnormality is detected.

[0040] The generation AI can be used to analyze the recipient's lifestyle patterns and automatically generate an optimal delivery schedule. The generation AI can, for example, analyze the recipient's lifestyle patterns and automatically generate an optimal delivery schedule. For example, the generation AI can make deliveries during times when the recipient is most active. The generation AI can also analyze the recipient's past delivery history and automatically generate an optimal delivery schedule. For example, the generation AI can make deliveries during times when the recipient is most comfortable receiving deliveries. The generation AI can also analyze the recipient's lifestyle rhythm and automatically generate an optimal delivery schedule. For example, the generation AI can make deliveries during times when the recipient is most relaxed. In this way, the recipient's lifestyle patterns can be analyzed and an optimal delivery schedule can be automatically generated.

[0041] Drones can be used to regularly deliver rehabilitation equipment and exercise equipment to support at-home rehabilitation. Drones can regularly deliver rehabilitation equipment, for example, by regularly supplying dumbbells for strength training and stretch bands. Drones can also regularly deliver exercise equipment to support at-home rehabilitation, for example, by regularly supplying exercise bikes and yoga mats. Drones can also quickly deliver rehabilitation equipment and exercise equipment to support at-home rehabilitation. For example, drones can quickly deliver replacement parts for broken equipment. This makes it possible to regularly deliver rehabilitation equipment and exercise equipment to support at-home rehabilitation.

[0042] A drone can use a speaker mounted on the drone to provide an audio guide to the recipient, explaining how to use the delivery item. For example, the drone can use a speaker to provide an audio guide to the recipient, explaining how to use the delivery item. For example, the drone can provide an audio guide such as, "We will tell you how to take your medication." The drone can also provide an audio guide that tells you how to use nursing care products. For example, the drone can provide an audio guide such as, "We will tell you how to use your nursing care products." The drone can also provide an audio guide that tells you how to use exercise equipment. For example, the drone can provide an audio guide such as, "We will tell you how to use the exercise equipment." This allows the drone to provide an audio guide to the recipient, explaining how to use the delivery item.

[0043] Generative AI can be used to analyze traffic data and optimize drone flight routes in real time. Generative AI can, for example, analyze traffic data in real time and optimize drone flight routes. For example, generative AI calculates routes that avoid areas with heavy traffic congestion. Generative AI can also analyze past traffic data and optimize drone flight routes. For example, generative AI calculates routes that avoid areas that are prone to heavy traffic during certain times of the day. Generative AI can also integrate traffic data and weather data to optimize drone flight routes. For example, generative AI calculates the optimal route that avoids traffic congestion and bad weather. This makes it possible to analyze traffic data and optimize drone flight routes in real time.

[0044] Using sensors installed in drones, they can detect obstacles in real time during flight and automatically avoid them. Drones can use LIDAR sensors to detect obstacles in real time during flight and automatically avoid them. For example, drones can avoid obstacles such as buildings and trees. Drones can also use cameras and image analysis technology to detect obstacles in real time during flight and automatically avoid them. For example, drones can avoid obstacles such as other flying objects and birds. Drones can also use ultrasonic sensors to detect obstacles in real time during flight and automatically avoid them. For example, drones can avoid obstacles such as power lines and poles. This allows drones to detect obstacles in real time during flight and automatically avoid them.

[0045] Drones can be used to quickly deliver medical supplies and relief goods in emergencies and provide support during disasters. Drones can, for example, quickly deliver medical supplies in emergencies. For example, drones can quickly supply medicines and medical equipment needed during disasters. Drones can also quickly deliver relief goods and provide support during disasters. For example, drones can quickly supply relief goods such as food, drinking water, and blankets. Drones can also quickly deliver medical supplies and relief goods in emergencies and provide support to disaster-stricken areas. For example, drones can quickly supply necessary supplies to disaster-stricken areas. This allows for the rapid delivery of medical supplies and relief goods in emergencies and provide support during disasters.

[0046] Using a camera mounted on a drone, traffic congestion conditions can be monitored in real time and fed back to a traffic management system. For example, a drone can use a high-resolution camera to monitor traffic congestion conditions in real time and feed back the information to the traffic management system. For example, a drone can identify areas where congestion is occurring. A drone can also use a 360-degree camera to monitor the entire traffic congestion area in real time and feed back the information to the traffic management system. For example, a drone can identify the cause of the congestion. Furthermore, a drone can use a night-vision camera to monitor traffic congestion conditions at night in real time and feed back the information to the traffic management system. For example, a drone can grasp the congestion conditions even at night. This allows traffic congestion conditions to be monitored in real time and fed back to the traffic management system.

[0047] Using sensors mounted on drones, it is possible to analyze terrain data in real time and automatically generate optimal flight routes. Drones can analyze terrain data in real time using, for example, LIDAR sensors and automatically generate optimal flight routes. For example, drones calculate routes that avoid mountainous and forested areas. Drones can also analyze terrain data in real time using cameras and image analysis technology and automatically generate optimal flight routes. For example, drones calculate routes that avoid rivers and lakes. Drones can also analyze terrain data in real time using ultrasonic sensors and automatically generate optimal flight routes. For example, drones calculate routes that avoid steep slopes and cliffs. This allows drones to analyze terrain data in real time and automatically generate optimal flight routes.

[0048] The generation AI can be used to analyze the geographical conditions of the delivery destination and select the optimal drone model and flight altitude. The generation AI can, for example, analyze the geographical conditions of the delivery destination and select the optimal drone model. For example, the generation AI selects a drone that suits geographical conditions such as mountainous areas or remote islands. The generation AI can also analyze the geographical conditions of the delivery destination and select the optimal flight altitude. For example, the generation AI selects a flight altitude that suits geographical conditions such as urban areas or forested areas. The generation AI can also analyze the geographical conditions of the delivery destination and select the optimal drone model and flight altitude. For example, the generation AI selects the optimal drone and flight altitude based on the terrain and weather conditions. This makes it possible to analyze the geographical conditions of the delivery destination and select the optimal drone model and flight altitude.

[0049] Drones can be used to provide regular medical checkup services to residents of mountainous areas and remote islands. Drones can provide regular medical checkup services to residents of mountainous areas, for example. For example, drones can periodically deliver medical equipment and test kits and conduct remote health checks. Drones can also provide regular medical checkup services to residents of remote islands. For example, drones can periodically deliver medical equipment and test kits and conduct remote health checks. Drones can also provide regular medical checkup services to residents of mountainous areas and remote islands. For example, drones can quickly deliver medical equipment and test kits and conduct remote health checks. This makes it possible to provide regular medical checkup services to residents of mountainous areas and remote islands.

[0050] A camera mounted on a drone can be used to photograph the geographical conditions of the delivery destination and provide a confirmation image to the recipient after the delivery is complete. For example, a drone can use a high-resolution camera to photograph the geographical conditions of the delivery destination and provide a confirmation image to the recipient after the delivery is complete. For example, the drone can confirm that the delivery was delivered correctly. A drone can also use a 360-degree camera to photograph the entire view of the delivery destination and provide a confirmation image to the recipient after the delivery is complete. For example, the drone can confirm that the delivery was delivered safely. Furthermore, a drone can use a night-vision camera to photograph the geographical conditions of the delivery destination at night and provide a confirmation image to the recipient after the delivery is complete. For example, the drone can confirm that the delivery was delivered correctly even at night. This allows the drone to photograph the geographical conditions of the delivery destination and provide a confirmation image to the recipient after the delivery is complete.

[0051] Generative AI can be used to analyze weather data and traffic conditions and optimize drone flight routes in real time. For example, generative AI can analyze weather data in real time and calculate the shortest route taking wind speed and rainfall into account. For example, generative AI can select a route that avoids strong winds and heavy rain. Generative AI can also analyze traffic data in real time and optimize drone flight routes. For example, generative AI can calculate the optimal aerial route to avoid traffic congestion and road construction. Furthermore, generative AI can integrate weather data and traffic data to optimize drone flight routes. For example, generative AI can adjust routes in real time depending on weather changes and traffic conditions. This allows drone flight routes to be optimized in real time by analyzing weather data and traffic conditions.

[0052] Drones can use sensors to scan the delivery destination environment in real time and automatically select the optimal landing spot. For example, drones can use LIDAR sensors to scan the destination's terrain and obstacles in real time and select the optimal landing spot. For example, drones can automatically determine safe locations such as yards or parking lots. Drones can also combine cameras and image analysis technology to monitor the destination environment in real time and select the optimal landing spot. For example, drones can identify safe locations such as in front of the recipient's door or on a balcony. Furthermore, drones can use ultrasonic sensors to measure the ground level and obstacles at the destination in real time and select the optimal landing spot. For example, drones can avoid uneven ground and obstacles to land safely. This allows drones to scan the destination environment in real time and automatically select the optimal landing spot.

[0053] Drones can be used to quickly deliver medical supplies and relief goods in emergencies and provide support during disasters. Drones can, for example, quickly deliver medical supplies in emergencies. For example, drones can quickly supply medicines and medical equipment needed during disasters. Drones can also quickly deliver relief goods and provide support during disasters. For example, drones can quickly supply relief goods such as food, drinking water, and blankets. Drones can also quickly deliver medical supplies and relief goods in emergencies and provide support to disaster-stricken areas. For example, drones can quickly supply necessary supplies to disaster-stricken areas. This allows for the rapid delivery of medical supplies and relief goods in emergencies and provide support during disasters.

[0054] A camera mounted on a drone can be used to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, a drone can use a high-resolution camera to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered correctly. A drone can also use a 360-degree camera to capture a panoramic view of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered safely. Furthermore, a drone can use a night vision camera to capture images of the surrounding environment of the delivery destination at night and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered correctly even at night. This allows the drone to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The delivery system may further include an energy management unit to improve energy efficiency. The energy management unit may optimize the drone's battery consumption and extend its flight time. For example, the energy management unit may reduce energy consumption by optimizing the flight route. The energy management unit may also use solar panels to charge the battery during flight. Furthermore, the energy management unit may utilize wind power to generate energy and reduce battery consumption. This improves energy efficiency and extends the drone's flight time.

[0057] The delivery system may further include an environmental monitoring unit. The environmental monitoring unit may collect data on the surrounding environment during delivery and use the collected data to help protect the environment. For example, the environmental monitoring unit may use an air quality sensor to collect data on air pollution. The environmental monitoring unit may also use temperature and humidity sensors to collect weather data. The environmental monitoring unit may also use a noise sensor to measure noise levels and use the measured noise data to help protect the environment. In this way, environmental data may be collected during delivery, contributing to environmental protection.

[0058] The delivery system may further include a communication unit. The communication unit may communicate with the recipient in real time. For example, the communication unit may use a video call function to talk directly with the recipient. The communication unit may also use a chat function to send and receive text messages. The communication unit may also use a voice message function to send voice messages to the recipient. This allows for real-time communication with the recipient.

[0059] The delivery system may further include a security unit. The security unit provides a function for ensuring the safety of deliveries. For example, the security unit may monitor the drone's location in real time using a GPS tracking function. The security unit may also monitor the status of deliveries using a camera. Furthermore, the security unit may detect unauthorized access to deliveries and send an alert. This ensures the safety of deliveries.

[0060] The delivery system may further include an automatic maintenance unit. The automatic maintenance unit may monitor the status of the drone and automatically perform necessary maintenance. For example, the automatic maintenance unit may monitor the battery status and charge it as needed. The automatic maintenance unit may also monitor the status of the propellers and replace them as needed. Furthermore, the automatic maintenance unit may monitor the status of the sensors and perform calibration as needed. This allows the drone to always be kept in optimal condition.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: Drones deliver supplies and medicines necessary for nursing care and medical treatment. For example, drones can deliver medicine from pharmacies to the homes of elderly people. Drones can also make scheduled deliveries of nursing care supplies and items necessary for daily life. Furthermore, drones can deliver goods while avoiding traffic congestion. For example, drones can fly through the air to avoid traffic congestion in urban areas and deliver goods quickly. Step 2: The delivery department uses the items delivered by the drone to support home life. For example, the delivery department hands over the delivered medicine to the elderly. The delivery department can also install the delivered nursing care products in the elderly's home. Furthermore, the delivery department can provide the elderly with the delivered items necessary for daily life. Step 3: The support department provides services to support home life. For example, the support department may provide health management services. The support department may also provide rehabilitation support services. Furthermore, the support department may also provide life assistance services.

[0063] (Example 2) A delivery system according to an embodiment of the present invention is a system that uses drones to quickly deliver items and medicines necessary for nursing care and medical treatment. This enables the delivery system to overcome traffic congestion and transportation constraints and deliver items quickly and efficiently.

[0064] A delivery system according to an embodiment includes a drone, a delivery unit, and a support unit. The drone delivers items and medicines necessary for nursing care and medical treatment. For example, the drone can deliver medicines from a pharmacy to an elderly person's home. The drone can also periodically deliver nursing care supplies and items necessary for daily life. The drone can deliver items while avoiding traffic congestion. For example, the drone flies through the air to avoid traffic congestion in urban areas and delivers items quickly. The delivery unit supports home life using items delivered by the drone. For example, the delivery unit hands over delivered medicines to the elderly. The delivery unit can also install delivered nursing care supplies at the elderly's home. The delivery unit can also provide delivered items necessary for daily life to the elderly. The support unit provides services to support home life. For example, the support unit can provide health management services. The support unit can also provide rehabilitation support services. The support unit can also provide life assistance services. This enables the delivery system according to an embodiment to quickly and efficiently deliver items and support home life. For example, drones can be used to overcome traffic congestion and geographical constraints and deliver goods quickly, while support functions can be used to support elderly people and people with disabilities living at home.

[0065] Drones can fly loaded with medicine and deliver it to a specified location. For example, drones can fly loaded with medicine from a pharmacy to an elderly person's home and deliver it to the specified location. For example, drones can land precisely at a specified location based on GPS coordinates. Drones can also deliver medicine to a specified address. For example, drones can deliver medicine to a specified location based on address information. Furthermore, drones can avoid obstacles when delivering medicine to a specified location. For example, drones can detect obstacles during flight and automatically avoid them to deliver the medicine to the specified location. This enables rapid delivery of medicine.

[0066] Drones can deliver nursing care supplies and items necessary for daily life on a regular basis. Drones, for example, regularly deliver nursing care supplies such as nursing beds and wheelchairs. Drones can also regularly deliver items necessary for daily life. For example, drones regularly deliver items necessary for daily life such as food and household goods. Drones can also set regular delivery schedules. For example, drones deliver nursing care supplies and items necessary for daily life on a specific day of the week each week. This makes it possible to regularly deliver nursing care supplies and items necessary for daily life.

[0067] Drones can deliver goods by avoiding traffic congestion. For example, drones can deliver goods by avoiding traffic congestion in urban areas. For example, drones can fly in the air to avoid traffic congestion. Drones can also avoid congestion on specific roads. For example, drones can deliver goods by avoiding roads with heavy traffic congestion. Furthermore, drones can monitor traffic congestion conditions in real time. For example, drones can select the optimal route based on traffic congestion information and deliver goods. This allows for quicker delivery by avoiding traffic congestion.

[0068] Drones can deliver goods by overcoming geographical constraints. For example, drones can deliver goods to areas that are difficult to access, such as mountainous regions or remote islands. For example, drones can deliver goods to the homes of elderly people in mountainous regions. Drones can also deliver goods to residents of remote islands. For example, drones can deliver medical supplies and items necessary for daily life to residents of remote islands. Furthermore, drones can optimize flight routes to overcome geographical constraints. For example, drones can select the optimal flight route taking into account the topography of mountainous regions or remote islands to deliver goods. This enables quick delivery by overcoming geographical constraints.

[0069] Drones can quickly deliver needed medicines and medical supplies in emergencies. For example, drones can quickly deliver needed medicines and medical supplies in disasters. For example, drones can quickly supply needed first aid kits and medicines in disasters. Drones can also quickly deliver needed medicines and medical supplies in medical emergencies. For example, drones can quickly supply needed first aid kits and medicines in medical emergencies. Drones can also optimize flight routes to quickly deliver needed medicines and medical supplies in emergencies. For example, drones can select the optimal flight route to quickly deliver needed medicines and medical supplies in emergencies. This enables the quick delivery of medicines and medical supplies in emergencies.

[0070] Drones can use sensors to scan the delivery destination environment in real time and automatically select the optimal landing spot. For example, drones can use LIDAR sensors to scan the destination's terrain and obstacles in real time and select the optimal landing spot. For example, drones can automatically determine safe locations such as yards or parking lots. Drones can also combine cameras and image analysis technology to monitor the destination environment in real time and select the optimal landing spot. For example, drones can identify safe locations such as in front of the recipient's door or on a balcony. Furthermore, drones can use ultrasonic sensors to measure the ground level and obstacles at the destination in real time and select the optimal landing spot. For example, drones can avoid uneven ground and obstacles to land safely. This allows drones to scan the destination environment in real time and automatically select the optimal landing spot.

[0071] To optimize drone flight routes, generative AI can analyze weather data and traffic conditions in real time and calculate the shortest route. For example, generative AI analyzes weather data in real time and calculates the shortest route taking wind speed and rainfall into account. For example, generative AI selects routes that avoid strong winds and heavy rain. Generative AI can also analyze traffic data in real time and optimize drone flight routes. For example, generative AI calculates the optimal aerial route to avoid traffic congestion and road construction. Furthermore, generative AI can integrate weather data and traffic data to optimize drone flight routes. For example, generative AI adjusts routes in real time depending on weather changes and traffic conditions. This allows for the analysis of weather data and traffic conditions in real time and the calculation of the shortest route.

[0072] Using a camera and emotion estimation algorithm installed on the drone, the drone can estimate the recipient's emotional state and send a notification at the optimal time for delivery. For example, a drone can use a camera and emotion estimation algorithm to analyze the recipient's facial expressions to estimate their emotional state. For example, the drone will send a delivery notification when the recipient is relaxed. A drone can also use a microphone and voice analysis technology to analyze the tone of the recipient's voice to estimate their emotional state. For example, the drone will send a delivery notification when the recipient is not feeling stressed. Furthermore, a drone can use sensors to measure the recipient's heart rate and body temperature to estimate their emotional state. For example, the drone will send a delivery notification when the recipient is calm. This allows the drone to send a notification at the optimal time depending on the recipient's emotional state.

[0073] Drones can be used to regularly deliver maintenance parts and consumables for medical equipment, improving the uptime of medical equipment. For example, drones can regularly deliver maintenance parts for medical equipment. For example, drones can regularly supply parts needed for medical equipment in hospitals. Drones can also regularly deliver consumables for medical equipment. For example, drones can regularly supply reagents for blood tests and disinfectants. Drones can also quickly deliver maintenance parts and consumables for medical equipment. For example, drones can quickly supply parts for broken equipment. This enables regular deliveries to improve the uptime of medical equipment.

[0074] A camera mounted on a drone can be used to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, a drone can use a high-resolution camera to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered correctly. A drone can also use a 360-degree camera to capture a panoramic view of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered safely. Furthermore, a drone can use a night vision camera to capture images of the surrounding environment of the delivery destination at night and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered correctly even at night. This allows the accuracy of the delivery to be confirmed by capturing images of the surrounding environment of the delivery destination and providing confirmation images to the recipient after delivery is complete.

[0075] Using a camera and emotion estimation algorithm installed on a drone, the drone can analyze the recipient's emotions when receiving a delivery and automatically generate a message to elicit positive emotions. For example, the drone can use a camera and emotion estimation algorithm to analyze the recipient's facial expressions and automatically generate a message to elicit positive emotions. For example, the drone can display a message such as, "Your delivery has arrived safely!". The drone can also analyze the tone of the recipient's voice using a microphone and voice analysis technology and automatically generate a message to elicit positive emotions. For example, the drone can display a message such as, "Enjoy your delivery!". Furthermore, the drone can measure the recipient's heart rate and body temperature using sensors and automatically generate a message to elicit positive emotions. For example, the drone can display a message such as, "Your delivery has arrived safely. Don't worry!". This makes it possible to analyze the recipient's emotions and automatically generate a message to elicit positive emotions.

[0076] The drone can monitor the recipient's health condition using sensors mounted on the drone, and issue an emergency notification if an abnormality is detected. For example, the drone can monitor the recipient's heart rate using a heart rate sensor, and issue an emergency notification if an abnormality is detected. For example, the drone can send an alert if the heart rate is abnormally high. The drone can also monitor the recipient's body temperature using a body temperature sensor, and issue an emergency notification if an abnormality is detected. For example, the drone can send an alert if the body temperature is abnormally high. The drone can also monitor the recipient's blood pressure using a blood pressure sensor, and issue an emergency notification if an abnormality is detected. For example, the drone can send an alert if the blood pressure is abnormally high. In this way, the recipient's health condition can be monitored, and an emergency notification can be issued if an abnormality is detected.

[0077] The generation AI can be used to analyze the recipient's lifestyle patterns and automatically generate an optimal delivery schedule. The generation AI can, for example, analyze the recipient's lifestyle patterns and automatically generate an optimal delivery schedule. For example, the generation AI can make deliveries during times when the recipient is most active. The generation AI can also analyze the recipient's past delivery history and automatically generate an optimal delivery schedule. For example, the generation AI can make deliveries during times when the recipient is most comfortable receiving deliveries. The generation AI can also analyze the recipient's lifestyle rhythm and automatically generate an optimal delivery schedule. For example, the generation AI can make deliveries during times when the recipient is most relaxed. In this way, the recipient's lifestyle patterns can be analyzed and an optimal delivery schedule can be automatically generated.

[0078] Using a camera and emotion estimation algorithm installed on the drone, the recipient's emotional state can be analyzed and a customized message can be attached to the delivery. For example, a drone can use a camera and emotion estimation algorithm to analyze the recipient's facial expression and attach a customized message to the delivery. For example, the drone can display a message such as, "Your medicine has arrived. Take care!". A drone can also analyze the tone of the recipient's voice using a microphone and voice analysis technology and attach a customized message to the delivery. For example, the drone can display a message such as, "Your nursing care products have arrived. Please use them!". Furthermore, a drone can measure the recipient's heart rate and body temperature using sensors and attach a customized message to the delivery. For example, the drone can display a message such as, "Your daily necessities have arrived. Don't worry!". This makes it possible to analyze the recipient's emotional state and attach a customized message to the delivery.

[0079] Drones can be used to regularly deliver rehabilitation equipment and exercise equipment to support at-home rehabilitation. Drones can regularly deliver rehabilitation equipment, for example, by regularly supplying dumbbells for strength training and stretch bands. Drones can also regularly deliver exercise equipment to support at-home rehabilitation, for example, by regularly supplying exercise bikes and yoga mats. Drones can also quickly deliver rehabilitation equipment and exercise equipment to support at-home rehabilitation. For example, drones can quickly deliver replacement parts for broken equipment. This makes it possible to regularly deliver rehabilitation equipment and exercise equipment to support at-home rehabilitation.

[0080] A drone can use a speaker mounted on the drone to provide an audio guide to the recipient, explaining how to use the delivery item. For example, the drone can use a speaker to provide an audio guide to the recipient, explaining how to use the delivery item. For example, the drone can provide an audio guide such as, "We will tell you how to take your medication." The drone can also provide an audio guide that tells you how to use nursing care products. For example, the drone can provide an audio guide such as, "We will tell you how to use your nursing care products." The drone can also provide an audio guide that tells you how to use exercise equipment. For example, the drone can provide an audio guide such as, "We will tell you how to use the exercise equipment." This allows the drone to provide an audio guide to the recipient, explaining how to use the delivery item.

[0081] Using a camera and emotion estimation algorithms mounted on the drone, the drone can analyze the recipient's emotional state and deliver music or messages designed to elicit positive emotions. For example, the drone can use a camera and emotion estimation algorithms to analyze the recipient's facial expressions and deliver music designed to elicit positive emotions. For example, the drone can play relaxing music. The drone can also analyze the recipient's tone of voice using a microphone and voice analysis technology and deliver music designed to elicit positive emotions. For example, the drone can play uplifting music. Furthermore, the drone can measure the recipient's heart rate and body temperature using sensors and deliver messages designed to elicit positive emotions. For example, the drone can display the message, "How are you? Your delivery has arrived!" This allows the drone to analyze the recipient's emotional state and deliver music or messages designed to elicit positive emotions.

[0082] Generative AI can be used to analyze traffic data and optimize drone flight routes in real time. Generative AI can, for example, analyze traffic data in real time and optimize drone flight routes. For example, generative AI calculates routes that avoid areas with heavy traffic congestion. Generative AI can also analyze past traffic data and optimize drone flight routes. For example, generative AI calculates routes that avoid areas that are prone to heavy traffic during certain times of the day. Generative AI can also integrate traffic data and weather data to optimize drone flight routes. For example, generative AI calculates the optimal route that avoids traffic congestion and bad weather. This makes it possible to analyze traffic data and optimize drone flight routes in real time.

[0083] Using sensors installed in drones, they can detect obstacles in real time during flight and automatically avoid them. Drones can use LIDAR sensors to detect obstacles in real time during flight and automatically avoid them. For example, drones can avoid obstacles such as buildings and trees. Drones can also use cameras and image analysis technology to detect obstacles in real time during flight and automatically avoid them. For example, drones can avoid obstacles such as other flying objects and birds. Drones can also use ultrasonic sensors to detect obstacles in real time during flight and automatically avoid them. For example, drones can avoid obstacles such as power lines and poles. This allows drones to detect obstacles in real time during flight and automatically avoid them.

[0084] Using a camera and emotion estimation algorithm mounted on the drone, the drone can analyze the recipient's emotional state and provide appropriate notifications in the event of a delivery delay. For example, the drone can analyze the recipient's facial expressions using a camera and emotion estimation algorithm to provide appropriate notifications in the event of a delivery delay. For example, the drone can display a message such as, "Sorry for the wait. We will arrive soon." The drone can also analyze the recipient's tone of voice using a microphone and voice analysis technology to provide appropriate notifications in the event of a delivery delay. For example, the drone can display a message such as, "The delivery is delayed, but we will arrive soon." Furthermore, the drone can measure the recipient's heart rate and body temperature using sensors to provide appropriate notifications in the event of a delivery delay. For example, the drone can display a message such as, "The delivery is delayed, but please rest assured. We will arrive soon." This allows the drone to analyze the recipient's emotional state in the event of a delivery delay and provide appropriate notifications.

[0085] Drones can be used to quickly deliver medical supplies and relief goods in emergencies and provide support during disasters. Drones can, for example, quickly deliver medical supplies in emergencies. For example, drones can quickly supply medicines and medical equipment needed during disasters. Drones can also quickly deliver relief goods and provide support during disasters. For example, drones can quickly supply relief goods such as food, drinking water, and blankets. Drones can also quickly deliver medical supplies and relief goods in emergencies and provide support to disaster-stricken areas. For example, drones can quickly supply necessary supplies to disaster-stricken areas. This allows for the rapid delivery of medical supplies and relief goods in emergencies and provide support during disasters.

[0086] Using a camera mounted on a drone, traffic congestion conditions can be monitored in real time and fed back to a traffic management system. For example, a drone can use a high-resolution camera to monitor traffic congestion conditions in real time and feed back the information to the traffic management system. For example, a drone can identify areas where congestion is occurring. A drone can also use a 360-degree camera to monitor the entire traffic congestion area in real time and feed back the information to the traffic management system. For example, a drone can identify the cause of the congestion. Furthermore, a drone can use a night-vision camera to monitor traffic congestion conditions at night in real time and feed back the information to the traffic management system. For example, a drone can grasp the congestion conditions even at night. This allows traffic congestion conditions to be monitored in real time and fed back to the traffic management system.

[0087] Using a camera and emotion estimation algorithm mounted on a drone, a message designed to elicit positive emotions can be automatically generated to evoke positive emotions in the event of a delivery delay. For example, a drone can analyze a recipient's facial expressions using a camera and emotion estimation algorithm to automatically generate a message designed to elicit positive emotions in the event of a delivery delay. For example, the drone can display a message such as, "Sorry for the wait. Your delivery will arrive soon." A drone can also analyze the tone of the recipient's voice using a microphone and voice analysis technology to automatically generate a message designed to elicit positive emotions in the event of a delivery delay. For example, the drone can display a message such as, "The delivery is delayed, but it will arrive soon." Furthermore, a drone can measure the recipient's heart rate and body temperature using sensors to automatically generate a message designed to elicit positive emotions in the event of a delivery delay. For example, the drone can display a message such as, "The delivery is delayed, but please don't worry. It will arrive soon." This allows a drone to analyze a recipient's emotions in the event of a delivery delay and automatically generate a message designed to elicit positive emotions in the event of a delivery delay.

[0088] Using sensors mounted on drones, it is possible to analyze terrain data in real time and automatically generate optimal flight routes. Drones can analyze terrain data in real time using, for example, LIDAR sensors and automatically generate optimal flight routes. For example, drones calculate routes that avoid mountainous and forested areas. Drones can also analyze terrain data in real time using cameras and image analysis technology and automatically generate optimal flight routes. For example, drones calculate routes that avoid rivers and lakes. Drones can also analyze terrain data in real time using ultrasonic sensors and automatically generate optimal flight routes. For example, drones calculate routes that avoid steep slopes and cliffs. This allows drones to analyze terrain data in real time and automatically generate optimal flight routes.

[0089] The generation AI can be used to analyze the geographical conditions of the delivery destination and select the optimal drone model and flight altitude. The generation AI can, for example, analyze the geographical conditions of the delivery destination and select the optimal drone model. For example, the generation AI selects a drone that suits geographical conditions such as mountainous areas or remote islands. The generation AI can also analyze the geographical conditions of the delivery destination and select the optimal flight altitude. For example, the generation AI selects a flight altitude that suits geographical conditions such as urban areas or forested areas. The generation AI can also analyze the geographical conditions of the delivery destination and select the optimal drone model and flight altitude. For example, the generation AI selects the optimal drone and flight altitude based on the terrain and weather conditions. This makes it possible to analyze the geographical conditions of the delivery destination and select the optimal drone model and flight altitude.

[0090] Using a camera and emotion estimation algorithm installed on the drone, the recipient's emotional state can be analyzed and a notification can be sent at the optimal time for receiving the delivery. For example, a drone can analyze the recipient's facial expressions using a camera and emotion estimation algorithm, and send a notification at the optimal time for receiving the delivery. For example, a drone can send a delivery notification when the recipient is relaxed. A drone can also analyze the tone of the recipient's voice using a microphone and voice analysis technology, and send a notification at the optimal time for receiving the delivery. For example, a drone can send a delivery notification when the recipient is not feeling stressed. Furthermore, a drone can measure the recipient's heart rate and body temperature using sensors, and send a notification at the optimal time for receiving the delivery. For example, a drone can send a delivery notification when the recipient is calm. This allows the drone to analyze the recipient's emotional state and send a notification at the optimal time for receiving the delivery.

[0091] Drones can be used to provide regular medical checkup services to residents of mountainous areas and remote islands. Drones can provide regular medical checkup services to residents of mountainous areas, for example. For example, drones can periodically deliver medical equipment and test kits and conduct remote health checks. Drones can also provide regular medical checkup services to residents of remote islands. For example, drones can periodically deliver medical equipment and test kits and conduct remote health checks. Drones can also provide regular medical checkup services to residents of mountainous areas and remote islands. For example, drones can quickly deliver medical equipment and test kits and conduct remote health checks. This makes it possible to provide regular medical checkup services to residents of mountainous areas and remote islands.

[0092] A camera mounted on a drone can be used to photograph the geographical conditions of the delivery destination and provide a confirmation image to the recipient after the delivery is complete. For example, a drone can use a high-resolution camera to photograph the geographical conditions of the delivery destination and provide a confirmation image to the recipient after the delivery is complete. For example, the drone can confirm that the delivery was delivered correctly. A drone can also use a 360-degree camera to photograph the entire view of the delivery destination and provide a confirmation image to the recipient after the delivery is complete. For example, the drone can confirm that the delivery was delivered safely. Furthermore, a drone can use a night-vision camera to photograph the geographical conditions of the delivery destination at night and provide a confirmation image to the recipient after the delivery is complete. For example, the drone can confirm that the delivery was delivered correctly even at night. This allows the drone to photograph the geographical conditions of the delivery destination and provide a confirmation image to the recipient after the delivery is complete.

[0093] Using a camera and emotion estimation algorithm installed on a drone, the drone can analyze the recipient's emotions when receiving a delivery and automatically generate a message to elicit positive emotions. For example, the drone can use a camera and emotion estimation algorithm to analyze the recipient's facial expressions and automatically generate a message to elicit positive emotions. For example, the drone can display a message such as, "Your delivery has arrived safely!". The drone can also analyze the recipient's tone of voice using a microphone and voice analysis technology and automatically generate a message to elicit positive emotions. For example, the drone can display a message such as, "Enjoy your delivery!". Furthermore, the drone can measure the recipient's heart rate and body temperature using sensors and automatically generate a message to elicit positive emotions. For example, the drone can display a message such as, "Your delivery has arrived safely. Don't worry!". This makes it possible to analyze the recipient's emotions when receiving a delivery and automatically generate a message to elicit positive emotions.

[0094] Generative AI can be used to analyze weather data and traffic conditions and optimize drone flight routes in real time. For example, generative AI can analyze weather data in real time and calculate the shortest route taking wind speed and rainfall into account. For example, generative AI can select a route that avoids strong winds and heavy rain. Generative AI can also analyze traffic data in real time and optimize drone flight routes. For example, generative AI can calculate the optimal aerial route to avoid traffic congestion and road construction. Furthermore, generative AI can integrate weather data and traffic data to optimize drone flight routes. For example, generative AI can adjust routes in real time depending on weather changes and traffic conditions. This allows drone flight routes to be optimized in real time by analyzing weather data and traffic conditions.

[0095] Drones can use sensors to scan the delivery destination environment in real time and automatically select the optimal landing spot. For example, drones can use LIDAR sensors to scan the destination's terrain and obstacles in real time and select the optimal landing spot. For example, drones can automatically determine safe locations such as yards or parking lots. Drones can also combine cameras and image analysis technology to monitor the destination environment in real time and select the optimal landing spot. For example, drones can identify safe locations such as in front of the recipient's door or on a balcony. Furthermore, drones can use ultrasonic sensors to measure the ground level and obstacles at the destination in real time and select the optimal landing spot. For example, drones can avoid uneven ground and obstacles to land safely. This allows drones to scan the destination environment in real time and automatically select the optimal landing spot.

[0096] Using a camera and emotion estimation algorithm installed on the drone, the drone can estimate the recipient's emotional state and send a notification at the optimal time for delivery. For example, a drone can use a camera and emotion estimation algorithm to analyze the recipient's facial expressions to estimate their emotional state. For example, a drone can send a delivery notification when the recipient is relaxed. A drone can also use a microphone and voice analysis technology to analyze the tone of the recipient's voice to estimate their emotional state. For example, a drone can send a delivery notification when the recipient is not feeling stressed. Furthermore, a drone can use sensors to measure the recipient's heart rate and body temperature to estimate their emotional state. For example, a drone can send a delivery notification when the recipient is calm. This allows the drone to estimate the recipient's emotional state and send a delivery notification at the optimal time for delivery.

[0097] Drones can be used to quickly deliver medical supplies and relief goods in emergencies and provide support during disasters. Drones can, for example, quickly deliver medical supplies in emergencies. For example, drones can quickly supply medicines and medical equipment needed during disasters. Drones can also quickly deliver relief goods and provide support during disasters. For example, drones can quickly supply relief goods such as food, drinking water, and blankets. Drones can also quickly deliver medical supplies and relief goods in emergencies and provide support to disaster-stricken areas. For example, drones can quickly supply necessary supplies to disaster-stricken areas. This allows for the rapid delivery of medical supplies and relief goods in emergencies and provide support during disasters.

[0098] A camera mounted on a drone can be used to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, a drone can use a high-resolution camera to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered correctly. A drone can also use a 360-degree camera to capture a panoramic view of the delivery destination and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered safely. Furthermore, a drone can use a night vision camera to capture images of the surrounding environment of the delivery destination at night and provide confirmation images to the recipient after delivery is complete. For example, the drone can confirm that the delivery was delivered correctly even at night. This allows the drone to capture images of the surrounding environment of the delivery destination and provide confirmation images to the recipient after delivery is complete.

[0099] Using a camera and emotion estimation algorithm installed on a drone, the drone can analyze the recipient's emotions when receiving a delivery and automatically generate a message to elicit positive emotions. For example, the drone can use a camera and emotion estimation algorithm to analyze the recipient's facial expressions and automatically generate a message to elicit positive emotions. For example, the drone can display a message such as, "Your delivery has arrived safely!". The drone can also analyze the recipient's tone of voice using a microphone and voice analysis technology and automatically generate a message to elicit positive emotions. For example, the drone can display a message such as, "Enjoy your delivery!". Furthermore, the drone can measure the recipient's heart rate and body temperature using sensors and automatically generate a message to elicit positive emotions. For example, the drone can display a message such as, "Your delivery has arrived safely. Don't worry!". This makes it possible to analyze the recipient's emotions when receiving a delivery and automatically generate a message to elicit positive emotions.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The delivery system may further include an energy management unit to improve energy efficiency. The energy management unit may optimize the drone's battery consumption and extend its flight time. For example, the energy management unit may reduce energy consumption by optimizing the flight route. The energy management unit may also use solar panels to charge the battery during flight. Furthermore, the energy management unit may utilize wind power to generate energy and reduce battery consumption. This improves energy efficiency and extends the drone's flight time.

[0102] The delivery system may further include an environmental monitoring unit. The environmental monitoring unit may collect data on the surrounding environment during delivery and use the collected data to help protect the environment. For example, the environmental monitoring unit may use an air quality sensor to collect data on air pollution. The environmental monitoring unit may also use temperature and humidity sensors to collect weather data. The environmental monitoring unit may also use a noise sensor to measure noise levels and use the measured noise data to help protect the environment. In this way, environmental data may be collected during delivery, contributing to environmental protection.

[0103] The delivery system may further include a communication unit. The communication unit may communicate with the recipient in real time. For example, the communication unit may use a video call function to talk directly with the recipient. The communication unit may also use a chat function to send and receive text messages. The communication unit may also use a voice message function to send voice messages to the recipient. This allows for real-time communication with the recipient.

[0104] The delivery system may further include a security unit. The security unit provides a function for ensuring the safety of deliveries. For example, the security unit may monitor the drone's location in real time using a GPS tracking function. The security unit may also monitor the status of deliveries using a camera. Furthermore, the security unit may detect unauthorized access to deliveries and send an alert. This ensures the safety of deliveries.

[0105] The delivery system may further include an automatic maintenance unit. The automatic maintenance unit may monitor the status of the drone and automatically perform necessary maintenance. For example, the automatic maintenance unit may monitor the battery status and charge it as needed. The automatic maintenance unit may also monitor the status of the propellers and replace them as needed. Furthermore, the automatic maintenance unit may monitor the status of the sensors and perform calibration as needed. This allows the drone to always be kept in optimal condition.

[0106] The delivery system can further use the emotion estimation function to provide a customized delivery experience based on the recipient's emotions. For example, if the emotion estimation function is used, relaxing music can be played. If the recipient is feeling happy, a congratulatory message can be displayed. If the recipient is feeling anxious, a reassuring message can be displayed. This allows for a customized delivery experience to be provided based on the recipient's emotions.

[0107] The delivery system can further use the emotion estimation function to adjust the delivery schedule based on the recipient's emotions. For example, the emotion estimation function can be used to make deliveries during times when the recipient is relaxed. The emotion estimation function can also be used to make deliveries that avoid times when the recipient is busy. The emotion estimation function can also be used to make deliveries that avoid times when the recipient is feeling stressed. In this way, the delivery schedule can be adjusted based on the recipient's emotions.

[0108] The delivery system can further use an emotion estimation function to issue a delivery notification based on the recipient's emotion. For example, the emotion estimation function can be used to issue a delivery notification when the recipient is relaxed. The emotion estimation function can also be used to issue a delivery notification when the recipient is not feeling stressed. The emotion estimation function can also be used to issue a delivery notification when the recipient is calm. This makes it possible to issue a delivery notification based on the recipient's emotion.

[0109] The delivery system can further use the emotion estimation function to suggest a method for receiving the delivery based on the recipient's emotions. For example, if the emotion estimation function is used to suggest that the recipient receive the delivery in person if the recipient is relaxed, the emotion estimation function can also be used to suggest that the delivery be left at the recipient's front door if the recipient is busy. Furthermore, if the emotion estimation function is used to suggest a nearby pickup location if the recipient is not at home, the delivery system can suggest a method for receiving the delivery based on the recipient's emotions.

[0110] The delivery system can further use the emotion estimation function to customize the delivery based on the recipient's emotions. For example, if the emotion estimation function is used to determine if the recipient is relaxed, it can add a relaxing item. If the emotion estimation function is used to determine if the recipient is happy, it can also add a congratulatory message. If the emotion estimation function is used to determine if the recipient is anxious, it can also add an item that gives a sense of security. In this way, it is possible to customize the delivery based on the recipient's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: Drones deliver supplies and medicines necessary for nursing care and medical treatment. For example, drones can deliver medicine from pharmacies to the homes of elderly people. Drones can also make scheduled deliveries of nursing care supplies and items necessary for daily life. Furthermore, drones can deliver goods while avoiding traffic congestion. For example, drones can fly through the air to avoid traffic congestion in urban areas and deliver goods quickly. Step 2: The delivery department uses the items delivered by the drone to support home life. For example, the delivery department hands over the delivered medicine to the elderly. The delivery department can also install the delivered nursing care products in the elderly's home. Furthermore, the delivery department can provide the elderly with the delivered items necessary for daily life. Step 3: The support department provides services to support home life. For example, the support department may provide health management services. The support department may also provide rehabilitation support services. Furthermore, the support department may also provide life assistance services.

[0113] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Drones and a delivery unit that delivers items by the drone; a support unit that supports home life using the items delivered by the delivery unit A system characterized by:

2. The drone is The medicine is loaded onto a plane and delivered to the designated location.

2. The system of claim 1.

3. The drone is Regular delivery of nursing care supplies and items necessary for daily life 2. The system of claim 1.

4. The drone is Delivering the item while avoiding traffic congestion 2. The system of claim 1.

5. The drone is Overcoming geographical constraints to deliver said items 2. The system of claim 1.

6. The drone is Rapid delivery of medicines and medical supplies needed in emergencies 2. The system of claim 1.

Images