system

Abstract

We provide the system. [Solution] A user interface means that receives user input information and obtains the flight area, aircraft type, flight purpose, and date and time, A data collection means that obtains relevant information from a past flight history database based on the aforementioned input information, A data processing means that automatically generates optimized flight routes and application information using the acquired information, A notification mechanism that provides the user with the generated flight route and application information, and allows for confirmation and modification. A feedback mechanism for receiving feedback after the completion of the aforementioned flight and utilizing it to improve the accuracy of future processes, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the flight preparation of a drone, the user has to repeat complex procedures at different locations for each new flight, which is a significant burden. Furthermore, due to the inability to fully utilize past flight records and information, problems have arisen in terms of safety and efficiency. This invention aims to solve these problems by enabling the user to easily perform flight preparation and improving the efficiency and safety of procedures.

Means for Solving the Problems

[0005] This invention is a system that collects past flight history data based on user input and automatically generates the optimal flight route and application information using a generative model. Through this system, users can check the optimal route for their flight area in advance and obtain the necessary application documents without hassle. Furthermore, by utilizing feedback after flight completion in preparation for the next flight, the system is designed to ensure optimal preparation at all times. This reduces the user's preparation time and enables safe and efficient drone operation.

[0006] "User interface means" refers to the general term for operation screens and input devices that allow users to provide input information to the system.

[0007] "Data collection methods" refer to the processes and mechanisms for obtaining past flight history and related information based on information provided by users.

[0008] A "generative model" is an algorithm and data processing program that takes collected data as input to automatically generate safe and efficient flight routes and related application information.

[0009] "Data processing means" refers to a function that uses a generative model to calculate and output the optimal flight route and application information.

[0010] A "notification mechanism" is a system that provides generated information to the user and offers an interface for confirmation and correction.

[0011] A "feedback mechanism" is a function that collects opinions and results provided by users after the completion of a flight and uses them to improve the future flight preparation process.

[0012] A "flight route" is a path that allows a drone to fly safely and efficiently.

[0013] "Application information" refers to information related to the permits and application documents required to conduct drone flights. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system for streamlining drone flight preparation, which automatically generates the optimal flight route and application details based on flight information provided by the user. The embodiments thereof are described below.

[0036] The user accesses the system through a terminal and enters necessary information such as the drone's flight area, model, flight purpose, and expected flight date and time. The terminal formats this data and establishes communication to send it to the server.

[0037] The server accesses a database of past flight history based on the received information and retrieves relevant data. This data includes the user's past flight patterns and selection tendencies, forming the basis for analysis by the generative model. The generative model utilizes this collected information to calculate a safe and optimal flight route for the user and generates application information as needed.

[0038] The server then sends the generated flight route and application information to the terminal and notifies the user. The user can review this information on the terminal and make corrections or approvals as needed. This allows the user to efficiently complete pre-flight preparations.

[0039] For example, if a user plans to fly over an urban area, the server cross-references map information of the urban area with historical flight data to provide the optimal route to avoid obstacles. Furthermore, necessary permit information for flying over commercial areas is automatically generated, simplifying the user's procedures.

[0040] After completing a flight, the user provides feedback to the system from their device. This feedback is received by the server and used to improve the accuracy of future flight routes and the application process. This allows the system to continuously learn and prepare users for more efficient and safer drone flights.

[0041] Thus, the present invention reduces the burden on users in preparing for flight and enables improvements in safety and efficiency.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user accesses the system from their terminal and enters the information necessary for drone flight, such as the flight area, aircraft type, flight purpose, and expected flight date and time. This information is appropriately formatted on the terminal and prepared for transmission.

[0045] Step 2:

[0046] The terminal sends the user's input information to the server. At this point, the terminal checks the stability of the data communication and, if necessary, resends the data or sends an acknowledgment.

[0047] Step 3:

[0048] The server accesses a database of past flight history based on the received information and collects relevant data. During this process, map information and safety margins related to the flight area are also obtained.

[0049] Step 4:

[0050] The server analyzes the collected data using a generative model and calculates the optimal flight route. It takes into account obstacles and local regulations to calculate the shortest distance while ensuring safety.

[0051] Step 5:

[0052] The server generates the calculated flight route and necessary application information and sends it to the terminal. The user is notified, and the terminal displays an interface for confirmation.

[0053] Step 6:

[0054] The user reviews the information provided on their device and makes any necessary corrections or approvals. At this point, the user makes final adjustments and completes the flight preparations.

[0055] Step 7:

[0056] After the flight is complete, the user provides feedback through their device. This feedback includes information about the flight's results and experience.

[0057] Step 8:

[0058] The server analyzes the received feedback and uses it to generate future flight routes and application information. The feedback data is then incorporated into the generation model to improve the system's accuracy.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] When flying drones, there is a need for a system that efficiently handles the formulation of appropriate flight routes and application procedures. However, current systems rely on manual processes for route optimization and the generation of application information, which are time-consuming and labor-intensive, and can sometimes lack accuracy. Furthermore, the system does not effectively utilize past flight history and feedback, which hinders continuous system improvement.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes information gathering means for acquiring relevant information from a past record database based on user input information, information processing means for automatically generating optimized routes and application information using a generation AI model based on the acquired information, and communication means for providing the generated routes and application information to the user and enabling confirmation and modification. This reduces the burden of preparing the drone for flight and enables efficient and safe flight.

[0064] "User input information" refers to data related to the area, equipment, purpose, and date and time of drone flight.

[0065] "Means of exchange between humans and machines" refers to methods of providing an interface for users to input and verify information.

[0066] "Information gathering means" refers to the processes and techniques for obtaining relevant data from historical record databases.

[0067] A "generative AI model" refers to artificial intelligence technology used to automatically generate optimized routes and application information based on available data.

[0068] "Information processing means" refers to a system that uses acquired data to calculate routes and application information.

[0069] "Communication means" refers to a method for providing the generated route and application information to the user and prompting them to confirm and correct it.

[0070] "Evaluation information" refers to feedback data provided by users after completing an action, and is information that can be used to improve the accuracy of future processes.

[0071] A "machine learning algorithm" refers to a data processing technique that uses evaluation information to improve the performance of a system.

[0072] "Route" refers to the trajectory or flight path optimized for drone flight.

[0073] "Application information" refers to data related to permits and document applications required for drone flight.

[0074] This invention is a system for streamlining drone flight preparation and achieving safe and optimal flight. The system consists of three main components: a server, a terminal, and a user.

[0075] The user uses a terminal to input information about the drone flight. This information includes the flight area, equipment, purpose, and date and time. The terminal is equipped with a user interface that collects the information entered by the user, and the input is performed through this interface.

[0076] The terminal formats the entered information appropriately and sends it to the server. Communication is established between the terminal and the server, and the data is converted into a format that the server can process.

[0077] Based on the received information, the server accesses a database containing historical records. Using information gathering tools, the server obtains relevant information, including the user's past flight patterns and selection tendencies. In this process, a generative AI model is utilized to automatically generate the optimal flight route and application information. The generative AI model incorporates machine learning algorithms, performing analysis and optimization tailored to the user's needs.

[0078] The generated information is sent back to the terminal and provided to the user via communication. The user can review the flight route and application information and make corrections as needed.

[0079] As a concrete example, consider a scenario where a user plans a flight over an urban area. The server uses urban map information and historical flight data to provide the optimal route to avoid obstacles. Furthermore, the necessary permit information for flying in commercial areas is automatically generated, allowing the user to proceed with the necessary procedures quickly.

[0080] After completing a flight, the user provides evaluation information from their terminal to the server. The server accepts this feedback and uses it to improve future flights and the application process. This evaluation information also contributes to improving the generated AI model, allowing the system to continuously learn.

[0081] An example of a prompt might be, "Automatically generate the optimal route and application procedures for drone flight in urban areas." Upon receiving this prompt, the system automatically performs the necessary analysis and procedures and provides the user with the best possible recommendations.

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

[0083] Step 1:

[0084] The user uses a terminal to input flight information. This input includes the flight area, equipment, purpose, and date and time. The terminal receives the input data and formats it into a format the program can understand. The input here is the user's flight plan, and the output is formatted data that the server can receive.

[0085] Step 2:

[0086] The terminal sends formatted data to the server. The terminal establishes a communication protocol and transfers the data to the server. In this process, the input is formatted user information, and the output is a state indicating that the transmission to the server is complete.

[0087] Step 3:

[0088] The server analyzes the data received from the terminal and accesses a database of past records. Here, the server uses SQL queries to extract the user's past flight patterns and selection tendencies. The input is user data from the terminal, and the output is the retrieved past record data.

[0089] Step 4:

[0090] The server uses acquired historical data as input to generate the optimal flight route and application information using a generative AI model. The generative AI model utilizes machine learning algorithms and performs analysis according to user needs. The output is the optimized route and application information.

[0091] Step 5:

[0092] The server sends the generated flight route and application information to the terminal. Here, the server uses communication methods to convert the data into a format that the terminal can display. The input is the generated information, and the output is the confirmation that the transmission to the terminal is complete.

[0093] Step 6:

[0094] The terminal displays received data on the user interface and notifies the user. The user can review the information and make corrections as needed. Input is data from the server, and output is a user-viewable information display.

[0095] Step 7:

[0096] After completing the flight, the user provides evaluation information from the terminal to the server. This evaluation information includes feedback based on the actual flight experience. The terminal receives this information and sends it to the server. The input is the user's feedback, and the output is the completion of the transmission to the server.

[0097] Step 8:

[0098] The server uses the evaluation data to improve the generated AI model. It leverages machine learning algorithms to improve the accuracy of future flight routes and application processes. The input for this step is the evaluation data, and the output is the improved model.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] In drone-based delivery services, selecting the optimal delivery route and obtaining necessary permits are crucial for ensuring fast and safe delivery to destinations. Furthermore, inefficient procedures can burden users and reduce the overall operational efficiency of the service. Additionally, it's essential to utilize the information gathered after delivery completion as feedback to improve future delivery processes.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes a user interface means for receiving user input information and acquiring the delivery area, equipment used, delivery purpose, and date and time; an information gathering means for acquiring relevant information from a past delivery history database based on the input information; and an information processing means for automatically generating optimized delivery routes and permission information using a generative model based on the acquired information. This enables efficient selection of optimal routes and permission acquisition procedures in delivery services.

[0104] "User input information" refers to data provided by the user in the delivery service regarding the delivery area, equipment used, purpose of delivery, and date and time.

[0105] "User interface means" refers to the interface used by a user to send input information to a server.

[0106] "Information gathering means" refers to methods used by a server to retrieve relevant information from a database of past delivery history based on user input.

[0107] A "generative model" is an algorithm that automatically generates the optimal delivery route and permission information based on past data and current information.

[0108] "Information processing means" refers to the process of creating optimal delivery routes and permission information using a generative model based on acquired information.

[0109] A "notification method" is a means of informing the user of the generated delivery route and permission information, allowing them to confirm and correct it.

[0110] "Improvement measures" refer to methods of receiving feedback after delivery is completed and using that feedback to improve the accuracy of the next delivery process.

[0111] This system efficiently generates optimal delivery routes and necessary permit information for drone-based delivery services. The following describes an embodiment of this system.

[0112] The server receives user input and provides a dedicated user interface to retrieve delivery area, equipment used, delivery purpose, and date and time. This is typically implemented as a smartphone app, making it easily accessible to users.

[0113] The server runs on cloud services such as AWS® Lambda and retrieves past delivery history data from Amazon RDS. This information gathering method collects relevant information based on user input.

[0114] As an information processing tool, the server utilizes a generative AI model and an optimization algorithm to automatically generate safe and efficient delivery routes. Simultaneously, it obtains necessary permission information for flying in urban areas through local government APIs. This information is then notified to the user's mobile device.

[0115] For example, when a user orders a pizza on a Sunday, the server instantly calculates the optimal route based on past data and obtains the necessary permissions. As a result, the pizza is delivered quickly, and user satisfaction increases.

[0116] After delivery is complete, users provide feedback. This feedback will be used to improve the delivery process for future deliveries.

[0117] Example prompt: "Calculate the optimal route and necessary permits for smooth food delivery using a drone this weekend. Situation: Sunday, 5 PM, delivery destination: an apartment in the city."

[0118] In this way, the system can improve efficiency and safety in delivery services.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The user uses their smartphone to input the delivery area, equipment used, delivery purpose, and date and time through the application's user interface. This data is properly formatted on the device and sent to the server. The input consists of detailed delivery information, and the output is formatted data. The device formats the data and establishes communication with the server.

[0122] Step 2:

[0123] The server uses information gathering tools running on AWS Lambda, based on the user input information received, to retrieve relevant historical delivery history data from Amazon RDS. The input is formatted user information, and the output is historical delivery data including related information. This involves querying the database and retrieving the results.

[0124] Step 3:

[0125] The server supplies acquired historical delivery data to a generating AI model, which uses an algorithm to automatically generate optimized delivery routes and necessary permission information. The input consists of historical delivery data and user request information, while the output is a specific delivery route and permission information. In this step, the AI ​​model performs data analysis and computational processing.

[0126] Step 4:

[0127] The server sends the generated delivery route and permission information to the user's device via a notification system. This includes using local government APIs to collect permission information. The input is the generated delivery route and permission information, and the output is a notification message presented to the user. The device displays this information on its screen, providing the user with an opportunity to review and correct it.

[0128] Step 5:

[0129] After delivery is complete, the user sends feedback information from their device to the server. The server receives this feedback and stores it in a database to use for improving future delivery processes. The input is user feedback information, and the output is reference data for future deliveries. Here, the feedback data is saved.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention is a system for streamlining drone flight preparation and improving the user experience, and in particular, by combining it with an emotion engine, it provides an even more personalized interface. The embodiments of the invention are described in detail below.

[0132] The user first accesses the system through a terminal and enters the information necessary for flight preparation. This information includes the flight area, aircraft type, flight purpose, and planned flight date and time. This information is sent from the terminal to the server, where relevant information is collected from the database.

[0133] The server references a database of past flight history based on the received information and retrieves relevant data. Furthermore, the emotion engine analyzes the user's responses during input and recognizes the user's emotional state. This emotional information is then used in a generative model to generate the optimal flight route and application information.

[0134] The generated flight route and application information are sent from the server to the terminal and notified to the user. Here, the emotion engine optimizes the user experience by adjusting the tone and responses of the interface according to the user's emotions, and providing additional explanations if the user is feeling anxious.

[0135] For example, if a user is planning their first flight in an urban area, the emotion engine will detect instability in the user's input and provide more detailed guidance. This allows the user to prepare for the flight with confidence.

[0136] After a flight is completed, the user provides feedback through their device. This feedback is processed by the server and used to improve the accuracy of the preparation process for future flights. In particular, emotion-based feedback strengthens the emotion engine model and helps improve the user experience in the future.

[0137] Thus, by incorporating an emotion engine, the present invention enables personalized responses tailored to the user's emotions, resulting in safer and more satisfying drone flight preparation.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user accesses the system using a terminal and enters the information necessary for drone flight. This includes the flight area, model of drone, purpose of flight, and scheduled flight date and time. The terminal prepares to send the entered information to the emotion engine.

[0141] Step 2:

[0142] The device passes the acquired user information to an emotion engine, which analyzes the user's facial expressions, operation speed, and input content during input. This allows the system to infer the user's emotional state.

[0143] Step 3:

[0144] The emotion engine determines the user's emotional state based on the analysis results and transfers this information, along with other data, to the server.

[0145] Step 4:

[0146] The server references a database of past flight history based on the received information and collects relevant data. Furthermore, it uses a generative model to create safe and efficient flight routes and application information. In this process, it also takes into account the user's emotional state identified by the emotion engine and adjusts the level of detail of the suggestions accordingly.

[0147] Step 5:

[0148] The server sends the generated flight route and application information to the terminal. The user is notified, and the emotion engine adjusts the interface according to the user's emotions. For example, if the user is feeling anxious, the explanation will be more detailed and the response will be reassuring.

[0149] Step 6:

[0150] The user reviews the information presented on the device and makes corrections or approvals as needed. At this point, the user completes final adjustments following the interface's guidance.

[0151] Step 7:

[0152] After the flight is complete, the user provides feedback to the system via their device. This feedback includes information about the flight experience and emotional state.

[0153] Step 8:

[0154] The server receives feedback and incorporates it into the emotion engine and generative model to improve the next flight preparation process. This allows the system to continuously learn and provide a higher quality user experience.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] This invention aims to streamline drone flight preparation and improve safety and user satisfaction. In particular, by taking into account the user's emotional state, it addresses the challenge of quickly responding to individual needs that conventional systems could not adequately address, thereby improving the flight experience.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes information receiving means for receiving user input information and acquiring the flight area, equipment used, flight purpose, and date and time; information gathering means for acquiring relevant information from a recording medium storing past flight records; and emotion analysis means for analyzing the user's reactions and recognizing their emotional state. This enables dynamic adjustment of the interface according to the user's emotions and the provision of optimized flight paths and application content.

[0160] "Information receiving means" refers to a function that allows the server to receive input information from the user and obtain details related to flight.

[0161] "Information gathering means" refers to a function that allows a server to retrieve relevant information from recording media containing past flight records and use it to prepare for the next flight.

[0162] An "emotion analysis tool" is an algorithm or program that analyzes user input and responses to recognize the user's current emotional state.

[0163] "Information generation means" refers to a function that automatically generates optimized flight paths and application details using a generative model based on collected data and recognized emotional states.

[0164] "Information provision means" refers to a function that presents the generated flight path and application details to the user and adjusts the interface according to the user's feelings.

[0165] "Information utilization means" refers to a function that receives feedback from users after the completion of a flight and uses this feedback to improve future processes.

[0166] This invention is a system designed to assist in drone flight preparation and aims to improve the user experience. Specifically, the user begins by accessing the system via a terminal and entering the necessary information. This information includes the flight area, the type of drone to be used, the purpose of the flight, and the scheduled flight date and time. The terminal then transmits this information to the server.

[0167] The server uses the received information to retrieve relevant data from recording media that store past flight records. This information is essential for optimizing the flight. Furthermore, the server's emotion analysis engine analyzes the user's responses during input to recognize the user's emotional state. Based on this, the server automatically generates the optimal flight route and application details using an AI model.

[0168] This system provides users with flight routes and application details generated via their terminals, and can adapt to the user's emotional state by adjusting the user interface. For example, if a user is planning a flight in an urban area for the first time, the system may sense their anxiety and display more detailed guidance.

[0169] As a concrete example, consider a scenario where a user is flying a drone in an urban area for the first time. In this case, the sentiment analysis engine would detect the user's anxiety in their input, and the system would provide the user with more detailed safety measures and map information. An example of a prompt might be, "This is my first time flying a drone in an urban area. Could you please provide a detailed guide regarding the flight area and safety?"

[0170] By incorporating emotion analysis and generative AI models in this way, more sophisticated individual responses become possible than before, resulting in drone flight preparations that are safer and more satisfying for users.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] Users access the system using a terminal and enter the flight area, equipment to be used, flight purpose, and scheduled flight date and time. The entered information is converted into a digital format by the terminal and sent to the server. The terminal's input interface provides text boxes and drop-down menus to allow users to easily select or enter the necessary information.

[0174] Step 2:

[0175] The server receives flight information transmitted from the terminal. Based on this data, the server accesses the database and searches for past flight history data. It executes SQL queries to extract relevant historical data and associates it with the current flight plan. This allows the server to obtain reference information based on past data.

[0176] Step 3:

[0177] The server activates the sentiment analysis engine based on the user's input. It analyzes the user's input speed and keystroke patterns to infer the user's emotional state. At this stage, the sentiment analysis algorithm is executed to determine whether the user is feeling anxious or stressed. The analysis results are used for subsequent processing.

[0178] Step 4:

[0179] The server combines acquired flight history data with sentiment analysis results and uses a generative AI model to automatically generate the optimal flight path and application information. In this process, the machine learning model considers past data and the current context to calculate the most efficient and safe route. The generated data is stored in a structured format.

[0180] Step 5:

[0181] The server sends the generated flight path and application information to the terminal. The terminal receives this information and adjusts the interface according to the user's emotional state. For example, if anxiety is detected, detailed safety information and operation guides are added. The graphical user interface (GUI) on the terminal is dynamically changed and provided to the user.

[0182] Step 6:

[0183] After completing a flight, users provide feedback through a terminal. The terminal sends this feedback as digital data to a server, which is used to improve the accuracy of preparations for the next flight. The server analyzes the feedback and uses it to further improve the sentiment analysis engine and generative AI models. This continuous feedback loop accelerates the evolution of the system.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] In drone-based food delivery services, users may feel anxious about using the service for the first time or about the new delivery method. This anxiety can become a barrier to service adoption. Furthermore, if delivery routes are not optimized, safety and efficiency may be reduced. In addition, there is a challenge in providing a better user experience due to the lack of personalized support that addresses the user's emotions.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes user connection means for receiving user input information and acquiring delivery area, equipment to be used, delivery purpose, and date and time; data collection means for acquiring relevant information from a past delivery performance database; and data processing means for automatically generating delivery routes and application information. This makes it possible to provide appropriate responses according to the user's emotional state in drone-based food delivery, thereby reducing anxiety and providing the optimal delivery route.

[0189] "User input information" refers to information provided by the user when using the service, including the delivery area, equipment used, delivery purpose, and date and time.

[0190] "User connection means" refers to a mechanism for users to interact with the system through an interface, and includes the function of receiving user input information.

[0191] A "data collection method" is a means that assists in current delivery preparations by extracting relevant information from a database of past delivery performance.

[0192] A "data processing method" is a means that has the function of optimizing delivery routes and application information using a generative model based on acquired data, and automatically generating them anew.

[0193] A "notification system" is a system that provides users with generated delivery routes and application information, and prompts them to confirm or correct the content.

[0194] An "emotion analysis tool" is a tool that analyzes a user's emotional state from their input information and has the function of providing an appropriate response tailored to each individual user.

[0195] A "feedback system" is a system for collecting user feedback after delivery is complete and using it to improve the process for the next time.

[0196] One embodiment of this invention involves a server receiving information entered by a user's terminal to efficiently carry out food delivery by drone. The terminal acquires information entered by the user, such as the delivery area, the equipment to be used, the purpose of delivery, and the time. This information is transmitted to the server, which then uses this information to refer to a database of past delivery performance and collects relevant information.

[0197] The server processes data using programming languages ​​such as Python, and optimizes and generates delivery routes and application information using navigation libraries and generative AI models. This optimization can utilize the computing power of Amazon AWS and Google Cloud. The server also uses an emotion analysis engine to analyze the user's emotional state and adjust the delivery process based on this analysis.

[0198] The generated information is sent from the server to the user's device, where they are prompted to review and correct it through the interface. For example, if a user is using the service for the first time, detailed notifications such as the drone's progress and estimated arrival time can help reduce anxiety. User feedback is returned to the server via the device and used to further improve the delivery experience.

[0199] For example, by inputting a prompt message into the AI ​​model such as, "Assess the user's concerns about the new delivery method and suggest appropriate advice," it is possible to further optimize the user experience.

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

[0201] Step 1:

[0202] The terminal receives information entered by the user regarding the delivery area, equipment used, delivery purpose, and date and time. This input information forms the basis of the data sent to the server.

[0203] Step 2:

[0204] The server retrieves relevant information from the past delivery performance database based on the user's input. It queries the database to find similar delivery records and generates a dataset for use in the next step.

[0205] Step 3:

[0206] The server uses the acquired dataset to optimize delivery routes and application information using a generative AI model written in Python. Leveraging a navigation library, it generates delivery routes that consider geographical data and safety factors, and the system outputs these generated delivery routes.

[0207] Step 4:

[0208] The server uses an emotion analysis engine to analyze the user's emotional state at the time of input. It extracts emotion-related features from the input information and inputs them into an emotion evaluation model to recognize the user's emotional state. The output is the user's emotion as a numerical value or category.

[0209] Step 5:

[0210] The server sends the generated delivery route and application information, as well as the sentiment analysis results, to the terminal. This information is then provided to the user through a notification system, and an interface for confirmation and modification is displayed.

[0211] Step 6:

[0212] The user reviews the provided delivery route and application information and makes any necessary corrections. They then enter their authorization via the terminal to initiate delivery, and this information is sent to the server.

[0213] Step 7:

[0214] After delivery is complete, the device receives feedback from the user and sends it to the server. The server analyzes the feedback and uses it for future improvements. This process aims to improve the experience based on emotions, and the server uses the feedback to update the emotion engine and generative AI model.

[0215] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] This invention is a system for streamlining drone flight preparation, which automatically generates the optimal flight route and application details based on flight information provided by the user. The embodiments thereof are described below.

[0232] The user accesses the system through a terminal and enters necessary information such as the drone's flight area, model, flight purpose, and expected flight date and time. The terminal formats this data and establishes communication to send it to the server.

[0233] The server accesses a database of past flight history based on the received information and retrieves relevant data. This data includes the user's past flight patterns and selection tendencies, forming the basis for analysis by the generative model. The generative model utilizes this collected information to calculate a safe and optimal flight route for the user and generates application information as needed.

[0234] The server then sends the generated flight route and application information to the terminal and notifies the user. The user can review this information on the terminal and make corrections or approvals as needed. This allows the user to efficiently complete pre-flight preparations.

[0235] For example, if a user plans to fly over an urban area, the server cross-references map information of the urban area with historical flight data to provide the optimal route to avoid obstacles. Furthermore, necessary permit information for flying over commercial areas is automatically generated, simplifying the user's procedures.

[0236] After completing a flight, the user provides feedback to the system from their device. This feedback is received by the server and used to improve the accuracy of future flight routes and the application process. This allows the system to continuously learn and prepare users for more efficient and safer drone flights.

[0237] Thus, the present invention reduces the burden on users in preparing for flight and enables improvements in safety and efficiency.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user accesses the system from their terminal and enters the information necessary for drone flight, such as the flight area, aircraft type, flight purpose, and expected flight date and time. This information is appropriately formatted on the terminal and prepared for transmission.

[0241] Step 2:

[0242] The terminal sends the user's input information to the server. At this point, the terminal checks the stability of the data communication and, if necessary, resends the data or sends an acknowledgment.

[0243] Step 3:

[0244] The server accesses a database of past flight history based on the received information and collects relevant data. During this process, map information and safety margins related to the flight area are also obtained.

[0245] Step 4:

[0246] The server analyzes the collected data using a generative model and calculates the optimal flight route. It takes into account obstacles and local regulations to calculate the shortest distance while ensuring safety.

[0247] Step 5:

[0248] The server generates the calculated flight route and necessary application information and sends it to the terminal. The user is notified, and the terminal displays an interface for confirmation.

[0249] Step 6:

[0250] The user reviews the information provided on their device and makes any necessary corrections or approvals. At this point, the user makes final adjustments and completes the flight preparations.

[0251] Step 7:

[0252] After the flight is complete, the user provides feedback through their device. This feedback includes information about the flight's results and experience.

[0253] Step 8:

[0254] The server analyzes the received feedback and uses it to generate future flight routes and application information. The feedback data is then incorporated into the generation model to improve the system's accuracy.

[0255] (Example 1)

[0256] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] When flying drones, there is a need for a system that efficiently handles the formulation of appropriate flight routes and application procedures. However, current systems rely on manual processes for route optimization and the generation of application information, which are time-consuming and labor-intensive, and can sometimes lack accuracy. Furthermore, the system does not effectively utilize past flight history and feedback, which hinders continuous system improvement.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] In this invention, the server includes information gathering means for acquiring relevant information from a past record database based on user input information, information processing means for automatically generating optimized routes and application information using a generation AI model based on the acquired information, and communication means for providing the generated routes and application information to the user and enabling confirmation and modification. This reduces the burden of preparing the drone for flight and enables efficient and safe flight.

[0260] "User input information" refers to data related to the area, equipment, purpose, and date and time of drone flight.

[0261] "Means of exchange between humans and machines" refers to methods of providing an interface for users to input and verify information.

[0262] "Information gathering means" refers to the processes and techniques for obtaining relevant data from historical record databases.

[0263] A "generative AI model" refers to artificial intelligence technology used to automatically generate optimized routes and application information based on available data.

[0264] "Information processing means" refers to a system that uses acquired data to calculate routes and application information.

[0265] "Communication means" refers to a method for providing the generated route and application information to the user and prompting them to confirm and correct it.

[0266] "Evaluation information" refers to feedback data provided by users after completing an action, and is information that can be used to improve the accuracy of future processes.

[0267] A "machine learning algorithm" refers to a data processing technique that uses evaluation information to improve the performance of a system.

[0268] "Route" refers to the trajectory or flight path optimized for drone flight.

[0269] "Application information" refers to data related to permits and document applications required for drone flight.

[0270] This invention is a system for streamlining drone flight preparation and achieving safe and optimal flight. The system consists of three main components: a server, a terminal, and a user.

[0271] The user uses a terminal to input information about the drone flight. This information includes the flight area, equipment, purpose, and date and time. The terminal is equipped with a user interface that collects the information entered by the user, and the input is performed through this interface.

[0272] The terminal formats the entered information appropriately and sends it to the server. Communication is established between the terminal and the server, and the data is converted into a format that the server can process.

[0273] Based on the received information, the server accesses a database containing historical records. Using information gathering tools, the server obtains relevant information, including the user's past flight patterns and selection tendencies. In this process, a generative AI model is utilized to automatically generate the optimal flight route and application information. The generative AI model incorporates machine learning algorithms, performing analysis and optimization tailored to the user's needs.

[0274] The generated information is sent back to the terminal and provided to the user via communication. The user can review the flight route and application information and make corrections as needed.

[0275] As a concrete example, consider a scenario where a user plans a flight over an urban area. The server uses urban map information and historical flight data to provide the optimal route to avoid obstacles. Furthermore, the necessary permit information for flying in commercial areas is automatically generated, allowing the user to proceed with the necessary procedures quickly.

[0276] After completing a flight, the user provides evaluation information from their terminal to the server. The server accepts this feedback and uses it to improve future flights and the application process. This evaluation information also contributes to improving the generated AI model, allowing the system to continuously learn.

[0277] An example of a prompt might be, "Automatically generate the optimal route and application procedures for drone flight in urban areas." Upon receiving this prompt, the system automatically performs the necessary analysis and procedures and provides the user with the best possible recommendations.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The user uses the terminal to input flight-related information. The input information includes the flight area, equipment, purpose, and date and time. The terminal receives the input data and formats it into a form that the program can understand. Here, the input is the user's flight plan, and the output is the formatted data that can be received by the server.

[0281] Step 2:

[0282] The terminal sends the formatted data to the server. The terminal establishes a communication protocol and transfers the data to the server. The input in this process is the formatted user information, and the output is the status indicating the completion of the transmission to the server.

[0283] Step 3:

[0284] The server analyzes the data received from the terminal and accesses the past record database. Here, the server uses SQL queries to extract the user's past flight patterns and selection tendencies. The input is the user data from the terminal, and the output is the obtained past record data.

[0285] Step 4:

[0286] Using the obtained past data as input, the server generates an optimal flight route and application information using a generated AI model. The generated AI model utilizes machine learning algorithms to perform analysis according to the user's needs. The output is the optimized route and application information.

[0287] Step 5:

[0288] The server sends the generated flight route and application information to the terminal. Here, the server uses communication means to convert the data into a form that can be displayed by the terminal. The input is the generated information, and the output is the information indicating the completion of the transmission to the terminal.

[0289] Step 6:

[0290] The terminal displays received data on the user interface and notifies the user. The user can review the information and make corrections as needed. Input is data from the server, and output is a user-viewable information display.

[0291] Step 7:

[0292] After completing the flight, the user provides evaluation information from the terminal to the server. This evaluation information includes feedback based on the actual flight experience. The terminal receives this information and sends it to the server. The input is the user's feedback, and the output is the completion of the transmission to the server.

[0293] Step 8:

[0294] The server uses the evaluation data to improve the generated AI model. It leverages machine learning algorithms to improve the accuracy of future flight routes and application processes. The input for this step is the evaluation data, and the output is the improved model.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] In drone-based delivery services, selecting the optimal delivery route and obtaining necessary permits are crucial for ensuring fast and safe delivery to destinations. Furthermore, inefficient procedures can burden users and reduce the overall operational efficiency of the service. Additionally, it's essential to utilize the information gathered after delivery completion as feedback to improve future delivery processes.

[0298] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0299] In this invention, the server includes a user interface means for receiving user input information and acquiring the delivery area, equipment used, delivery purpose, and date and time; an information gathering means for acquiring relevant information from a past delivery history database based on the input information; and an information processing means for automatically generating optimized delivery routes and permission information using a generative model based on the acquired information. This enables efficient selection of optimal routes and permission acquisition procedures in delivery services.

[0300] "User input information" refers to data provided by the user in the delivery service regarding the delivery area, equipment used, purpose of delivery, and date and time.

[0301] "User interface means" refers to the interface used by a user to send input information to a server.

[0302] "Information gathering means" refers to methods used by a server to retrieve relevant information from a database of past delivery history based on user input.

[0303] A "generative model" is an algorithm that automatically generates the optimal delivery route and permission information based on past data and current information.

[0304] "Information processing means" refers to the process of creating optimal delivery routes and permission information using a generative model based on acquired information.

[0305] A "notification method" is a means of informing the user of the generated delivery route and permission information, allowing them to confirm and correct it.

[0306] "Improvement measures" refer to methods of receiving feedback after delivery is completed and using that feedback to improve the accuracy of the next delivery process.

[0307] This system efficiently generates an optimal delivery route and the necessary permission information in a drone-based delivery service. The embodiments of this system will be described below.

[0308] The server receives the user's input information and provides a dedicated user interface to obtain the delivery area, equipment used, delivery purpose, and date and time. This is usually implemented as a smartphone app for easy access by users.

[0309] The server operates on a cloud service such as AWS Lambda and obtains past delivery history data from Amazon RDS. Through this information collection means, relevant information based on the user's input information is collected.

[0310] As an information processing means, the server utilizes a generative AI model and uses an optimization algorithm to automatically generate a safe and rapid delivery route. At the same time, permission information required for flight in urban areas is obtained through the API of the local government. This information is notified to the user's mobile terminal.

[0311] For example, when a user orders pizza on a Sunday, the server immediately calculates an optimal route based on past data and obtains the necessary permissions. As a result, the pizza is delivered quickly, improving user satisfaction.

[0312] After the delivery is completed, the user provides feedback. This feedback is utilized to improve the subsequent delivery processes.

[0313] Example of a prompt sentence: "Please calculate the optimal route and necessary permission information for smooth food delivery using a drone this weekend. Situation: 5 pm on Sunday, the delivery destination is an apartment in the urban area."

[0314] In this way, the system can improve efficiency and safety in the delivery service.

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The user uses their smartphone to input the delivery area, equipment used, delivery purpose, and date and time through the application's user interface. This data is properly formatted on the device and sent to the server. The input consists of detailed delivery information, and the output is formatted data. The device formats the data and establishes communication with the server.

[0318] Step 2:

[0319] The server uses information gathering tools running on AWS Lambda, based on the user input information received, to retrieve relevant historical delivery history data from Amazon RDS. The input is formatted user information, and the output is historical delivery data including related information. This involves querying the database and retrieving the results.

[0320] Step 3:

[0321] The server supplies acquired historical delivery data to a generating AI model, which uses an algorithm to automatically generate optimized delivery routes and necessary permission information. The input consists of historical delivery data and user request information, while the output is a specific delivery route and permission information. In this step, the AI ​​model performs data analysis and computational processing.

[0322] Step 4:

[0323] The server sends the generated delivery route and permission information to the user's device via a notification system. This includes using local government APIs to collect permission information. The input is the generated delivery route and permission information, and the output is a notification message presented to the user. The device displays this information on its screen, providing the user with an opportunity to review and correct it.

[0324] Step 5:

[0325] After delivery is complete, the user sends feedback information from their device to the server. The server receives this feedback and stores it in a database to use for improving future delivery processes. The input is user feedback information, and the output is reference data for future deliveries. Here, the feedback data is saved.

[0326] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0327] This invention is a system for streamlining drone flight preparation and improving the user experience, and in particular, by combining it with an emotion engine, it provides an even more personalized interface. The embodiments of the invention are described in detail below.

[0328] The user first accesses the system through a terminal and enters the information necessary for flight preparation. This information includes the flight area, aircraft type, flight purpose, and planned flight date and time. This information is sent from the terminal to the server, where relevant information is collected from the database.

[0329] The server references a database of past flight history based on the received information and retrieves relevant data. Furthermore, the emotion engine analyzes the user's responses during input and recognizes the user's emotional state. This emotional information is then used in a generative model to generate the optimal flight route and application information.

[0330] The generated flight route and application information are sent from the server to the terminal and notified to the user. Here, the emotion engine optimizes the user experience by adjusting the tone and responses of the interface according to the user's emotions, and providing additional explanations if the user is feeling anxious.

[0331] For example, if a user is planning their first flight in an urban area, the emotion engine will detect instability in the user's input and provide more detailed guidance. This allows the user to prepare for the flight with confidence.

[0332] After a flight is completed, the user provides feedback through their device. This feedback is processed by the server and used to improve the accuracy of the preparation process for future flights. In particular, emotion-based feedback strengthens the emotion engine model and helps improve the user experience in the future.

[0333] Thus, by incorporating an emotion engine, the present invention enables personalized responses tailored to the user's emotions, resulting in safer and more satisfying drone flight preparation.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The user accesses the system using a terminal and enters the information necessary for drone flight. This includes the flight area, model of drone, purpose of flight, and scheduled flight date and time. The terminal prepares to send the entered information to the emotion engine.

[0337] Step 2:

[0338] The device passes the acquired user information to an emotion engine, which analyzes the user's facial expressions, operation speed, and input content during input. This allows the system to infer the user's emotional state.

[0339] Step 3:

[0340] The emotion engine determines the user's emotional state based on the analysis results and transfers this information, along with other data, to the server.

[0341] Step 4:

[0342] The server references a database of past flight history based on the received information and collects relevant data. Furthermore, it uses a generative model to create safe and efficient flight routes and application information. In this process, it also takes into account the user's emotional state identified by the emotion engine and adjusts the level of detail of the suggestions accordingly.

[0343] Step 5:

[0344] The server sends the generated flight route and application information to the terminal. The user is notified, and the emotion engine adjusts the interface according to the user's emotions. For example, if the user is feeling anxious, the explanation will be more detailed and the response will be reassuring.

[0345] Step 6:

[0346] The user reviews the information presented on the device and makes corrections or approvals as needed. At this point, the user completes final adjustments following the interface's guidance.

[0347] Step 7:

[0348] After the flight is complete, the user provides feedback to the system via their device. This feedback includes information about the flight experience and emotional state.

[0349] Step 8:

[0350] The server receives feedback and incorporates it into the emotion engine and generative model to improve the next flight preparation process. This allows the system to continuously learn and provide a higher quality user experience.

[0351] (Example 2)

[0352] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0353] This invention aims to streamline drone flight preparation and improve safety and user satisfaction. In particular, by taking into account the user's emotional state, it addresses the challenge of quickly responding to individual needs that conventional systems could not adequately address, thereby improving the flight experience.

[0354] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0355] In this invention, the server includes information receiving means for receiving user input information and acquiring the flight area, equipment used, flight purpose, and date and time; information gathering means for acquiring relevant information from a recording medium storing past flight records; and emotion analysis means for analyzing the user's reactions and recognizing their emotional state. This enables dynamic adjustment of the interface according to the user's emotions and the provision of optimized flight paths and application content.

[0356] "Information receiving means" refers to a function that allows the server to receive input information from the user and obtain details related to flight.

[0357] "Information gathering means" refers to a function that allows a server to retrieve relevant information from recording media containing past flight records and use it to prepare for the next flight.

[0358] An "emotion analysis tool" is an algorithm or program that analyzes user input and responses to recognize the user's current emotional state.

[0359] "Information generation means" refers to a function that automatically generates optimized flight paths and application details using a generative model based on collected data and recognized emotional states.

[0360] "Information provision means" refers to a function that presents the generated flight path and application details to the user and adjusts the interface according to the user's feelings.

[0361] "Information utilization means" refers to a function that receives feedback from users after the completion of a flight and uses this feedback to improve future processes.

[0362] This invention is a system designed to assist in drone flight preparation and aims to improve the user experience. Specifically, the user begins by accessing the system via a terminal and entering the necessary information. This information includes the flight area, the type of drone to be used, the purpose of the flight, and the scheduled flight date and time. The terminal then transmits this information to the server.

[0363] The server uses the received information to retrieve relevant data from recording media that store past flight records. This information is essential for optimizing the flight. Furthermore, the server's emotion analysis engine analyzes the user's responses during input to recognize the user's emotional state. Based on this, the server automatically generates the optimal flight route and application details using an AI model.

[0364] This system provides users with flight routes and application details generated via their terminals, and can adapt to the user's emotional state by adjusting the user interface. For example, if a user is planning a flight in an urban area for the first time, the system may sense their anxiety and display more detailed guidance.

[0365] As a concrete example, consider a scenario where a user is flying a drone in an urban area for the first time. In this case, the sentiment analysis engine would detect the user's anxiety in their input, and the system would provide the user with more detailed safety measures and map information. An example of a prompt might be, "This is my first time flying a drone in an urban area. Could you please provide a detailed guide regarding the flight area and safety?"

[0366] By incorporating emotion analysis and generative AI models in this way, more sophisticated individual responses become possible than before, resulting in drone flight preparations that are safer and more satisfying for users.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] Users access the system using a terminal and enter the flight area, equipment to be used, flight purpose, and scheduled flight date and time. The entered information is converted into a digital format by the terminal and sent to the server. The terminal's input interface provides text boxes and drop-down menus to allow users to easily select or enter the necessary information.

[0370] Step 2:

[0371] The server receives flight information transmitted from the terminal. Based on this data, the server accesses the database and searches for past flight history data. It executes SQL queries to extract relevant historical data and associates it with the current flight plan. This allows the server to obtain reference information based on past data.

[0372] Step 3:

[0373] The server activates the sentiment analysis engine based on the user's input. It analyzes the user's input speed and keystroke patterns to infer the user's emotional state. At this stage, the sentiment analysis algorithm is executed to determine whether the user is feeling anxious or stressed. The analysis results are used for subsequent processing.

[0374] Step 4:

[0375] The server combines acquired flight history data with sentiment analysis results and uses a generative AI model to automatically generate the optimal flight path and application information. In this process, the machine learning model considers past data and the current context to calculate the most efficient and safe route. The generated data is stored in a structured format.

[0376] Step 5:

[0377] The server sends the generated flight path and application information to the terminal. The terminal receives this information and adjusts the interface according to the user's emotional state. For example, if anxiety is detected, detailed safety information and operation guides are added. The graphical user interface (GUI) on the terminal is dynamically changed and provided to the user.

[0378] Step 6:

[0379] After completing a flight, users provide feedback through a terminal. The terminal sends this feedback as digital data to a server, which is used to improve the accuracy of preparations for the next flight. The server analyzes the feedback and uses it to further improve the sentiment analysis engine and generative AI models. This continuous feedback loop accelerates the evolution of the system.

[0380] (Application Example 2)

[0381] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0382] In drone-based food delivery services, users may feel anxious about using the service for the first time or about the new delivery method. This anxiety can become a barrier to service adoption. Furthermore, if delivery routes are not optimized, safety and efficiency may be reduced. In addition, there is a challenge in providing a better user experience due to the lack of personalized support that addresses the user's emotions.

[0383] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0384] In this invention, the server includes user connection means for receiving user input information and acquiring delivery area, equipment to be used, delivery purpose, and date and time; data collection means for acquiring relevant information from a past delivery performance database; and data processing means for automatically generating delivery routes and application information. This makes it possible to provide appropriate responses according to the user's emotional state in drone-based food delivery, thereby reducing anxiety and providing the optimal delivery route.

[0385] "User input information" refers to information provided by the user when using the service, including the delivery area, equipment used, delivery purpose, and date and time.

[0386] "User connection means" refers to a mechanism for users to interact with the system through an interface, and includes the function of receiving user input information.

[0387] A "data collection method" is a means that assists in current delivery preparations by extracting relevant information from a database of past delivery performance.

[0388] A "data processing method" is a means that has the function of optimizing delivery routes and application information using a generative model based on acquired data, and automatically generating them anew.

[0389] A "notification system" is a system that provides users with generated delivery routes and application information, and prompts them to confirm or correct the content.

[0390] An "emotion analysis tool" is a tool that analyzes a user's emotional state from their input information and has the function of providing an appropriate response tailored to each individual user.

[0391] A "feedback system" is a system for collecting user feedback after delivery is complete and using it to improve the process for the next time.

[0392] One embodiment of this invention involves a server receiving information entered by a user's terminal to efficiently carry out food delivery by drone. The terminal acquires information entered by the user, such as the delivery area, the equipment to be used, the purpose of delivery, and the time. This information is transmitted to the server, which then uses this information to refer to a database of past delivery performance and collects relevant information.

[0393] The server processes data using programming languages ​​such as Python, and optimizes and generates delivery routes and application information using navigation libraries and generative AI models. This optimization can utilize the computing power of Amazon AWS and Google Cloud. The server also uses an emotion analysis engine to analyze the user's emotional state and adjust the delivery process based on this analysis.

[0394] The generated information is sent from the server to the user's device, where they are prompted to review and correct it through the interface. For example, if a user is using the service for the first time, detailed notifications such as the drone's progress and estimated arrival time can help reduce anxiety. User feedback is returned to the server via the device and used to further improve the delivery experience.

[0395] For example, by inputting a prompt message into the AI ​​model such as, "Assess the user's concerns about the new delivery method and suggest appropriate advice," it is possible to further optimize the user experience.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The terminal receives information entered by the user regarding the delivery area, equipment used, delivery purpose, and date and time. This input information forms the basis of the data sent to the server.

[0399] Step 2:

[0400] The server retrieves relevant information from the past delivery performance database based on the user's input. It queries the database to find similar delivery records and generates a dataset for use in the next step.

[0401] Step 3:

[0402] The server uses the acquired dataset to optimize delivery routes and application information using a generative AI model written in Python. Leveraging a navigation library, it generates delivery routes that consider geographical data and safety factors, and the system outputs these generated delivery routes.

[0403] Step 4:

[0404] The server uses an emotion analysis engine to analyze the user's emotional state at the time of input. It extracts emotion-related features from the input information and inputs them into an emotion evaluation model to recognize the user's emotional state. The output is the user's emotion as a numerical value or category.

[0405] Step 5:

[0406] The server sends the generated delivery route and application information, as well as the sentiment analysis results, to the terminal. This information is then provided to the user through a notification system, and an interface for confirmation and modification is displayed.

[0407] Step 6:

[0408] The user reviews the provided delivery route and application information and makes any necessary corrections. They then enter their authorization via the terminal to initiate delivery, and this information is sent to the server.

[0409] Step 7:

[0410] After delivery is complete, the device receives feedback from the user and sends it to the server. The server analyzes the feedback and uses it for future improvements. This process aims to improve the experience based on emotions, and the server uses the feedback to update the emotion engine and generative AI model.

[0411] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0417] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0419] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0421] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0422] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0423] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0424] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0426] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0427] This invention is a system for streamlining drone flight preparation, which automatically generates the optimal flight route and application details based on flight information provided by the user. The embodiments thereof are described below.

[0428] The user accesses the system through a terminal and enters necessary information such as the drone's flight area, model, flight purpose, and expected flight date and time. The terminal formats this data and establishes communication to send it to the server.

[0429] The server accesses a database of past flight history based on the received information and retrieves relevant data. This data includes the user's past flight patterns and selection tendencies, forming the basis for analysis by the generative model. The generative model utilizes this collected information to calculate a safe and optimal flight route for the user and generates application information as needed.

[0430] The server then sends the generated flight route and application information to the terminal and notifies the user. The user can review this information on the terminal and make corrections or approvals as needed. This allows the user to efficiently complete pre-flight preparations.

[0431] For example, if a user plans to fly over an urban area, the server cross-references map information of the urban area with historical flight data to provide the optimal route to avoid obstacles. Furthermore, necessary permit information for flying over commercial areas is automatically generated, simplifying the user's procedures.

[0432] After completing a flight, the user provides feedback to the system from their device. This feedback is received by the server and used to improve the accuracy of future flight routes and the application process. This allows the system to continuously learn and prepare users for more efficient and safer drone flights.

[0433] Thus, the present invention reduces the burden on users in preparing for flight and enables improvements in safety and efficiency.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The user accesses the system from their terminal and enters the information necessary for drone flight, such as the flight area, aircraft type, flight purpose, and expected flight date and time. This information is appropriately formatted on the terminal and prepared for transmission.

[0437] Step 2:

[0438] The terminal sends the user's input information to the server. At this point, the terminal checks the stability of the data communication and, if necessary, resends the data or sends an acknowledgment.

[0439] Step 3:

[0440] The server accesses a database of past flight history based on the received information and collects relevant data. During this process, map information and safety margins related to the flight area are also obtained.

[0441] Step 4:

[0442] The server analyzes the collected data using a generative model and calculates the optimal flight route. It takes into account obstacles and local regulations to calculate the shortest distance while ensuring safety.

[0443] Step 5:

[0444] The server generates the calculated flight route and necessary application information and sends it to the terminal. The user is notified, and the terminal displays an interface for confirmation.

[0445] Step 6:

[0446] The user reviews the information provided on their device and makes any necessary corrections or approvals. At this point, the user makes final adjustments and completes the flight preparations.

[0447] Step 7:

[0448] After the flight is complete, the user provides feedback through their device. This feedback includes information about the flight's results and experience.

[0449] Step 8:

[0450] The server analyzes the received feedback and uses it to generate future flight routes and application information. The feedback data is then incorporated into the generation model to improve the system's accuracy.

[0451] (Example 1)

[0452] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0453] When flying drones, there is a need for a system that efficiently handles the formulation of appropriate flight routes and application procedures. However, current systems rely on manual processes for route optimization and the generation of application information, which are time-consuming and labor-intensive, and can sometimes lack accuracy. Furthermore, the system does not effectively utilize past flight history and feedback, which hinders continuous system improvement.

[0454] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0455] In this invention, the server includes information gathering means for acquiring relevant information from a past record database based on user input information, information processing means for automatically generating optimized routes and application information using a generation AI model based on the acquired information, and communication means for providing the generated routes and application information to the user and enabling confirmation and modification. This reduces the burden of preparing the drone for flight and enables efficient and safe flight.

[0456] "User input information" refers to data related to the area, equipment, purpose, and date and time of drone flight.

[0457] "Means of exchange between humans and machines" refers to methods of providing an interface for users to input and verify information.

[0458] "Information gathering means" refers to the processes and techniques for obtaining relevant data from historical record databases.

[0459] A "generative AI model" refers to artificial intelligence technology used to automatically generate optimized routes and application information based on available data.

[0460] "Information processing means" refers to a system that uses acquired data to calculate routes and application information.

[0461] "Communication means" refers to a method for providing the generated route and application information to the user and prompting them to confirm and correct it.

[0462] "Evaluation information" refers to feedback data provided by users after completing an action, and is information that can be used to improve the accuracy of future processes.

[0463] A "machine learning algorithm" refers to a data processing technique that uses evaluation information to improve the performance of a system.

[0464] "Route" refers to the trajectory or flight path optimized for drone flight.

[0465] "Application information" refers to data related to permits and document applications required for drone flight.

[0466] This invention is a system for streamlining drone flight preparation and achieving safe and optimal flight. The system consists of three main components: a server, a terminal, and a user.

[0467] The user uses a terminal to input information about the drone flight. This information includes the flight area, equipment, purpose, and date and time. The terminal is equipped with a user interface that collects the information entered by the user, and the input is performed through this interface.

[0468] The terminal formats the entered information appropriately and sends it to the server. Communication is established between the terminal and the server, and the data is converted into a format that the server can process.

[0469] Based on the received information, the server accesses a database containing historical records. Using information gathering tools, the server obtains relevant information, including the user's past flight patterns and selection tendencies. In this process, a generative AI model is utilized to automatically generate the optimal flight route and application information. The generative AI model incorporates machine learning algorithms, performing analysis and optimization tailored to the user's needs.

[0470] The generated information is sent back to the terminal and provided to the user via communication. The user can review the flight route and application information and make corrections as needed.

[0471] As a concrete example, consider a scenario where a user plans a flight over an urban area. The server uses urban map information and historical flight data to provide the optimal route to avoid obstacles. Furthermore, the necessary permit information for flying in commercial areas is automatically generated, allowing the user to proceed with the necessary procedures quickly.

[0472] After completing a flight, the user provides evaluation information from their terminal to the server. The server accepts this feedback and uses it to improve future flights and the application process. This evaluation information also contributes to improving the generated AI model, allowing the system to continuously learn.

[0473] An example of a prompt might be, "Automatically generate the optimal route and application procedures for drone flight in urban areas." Upon receiving this prompt, the system automatically performs the necessary analysis and procedures and provides the user with the best possible recommendations.

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] The user uses a terminal to input flight information. This input includes the flight area, equipment, purpose, and date and time. The terminal receives the input data and formats it into a format the program can understand. The input here is the user's flight plan, and the output is formatted data that the server can receive.

[0477] Step 2:

[0478] The terminal sends formatted data to the server. The terminal establishes a communication protocol and transfers the data to the server. In this process, the input is formatted user information, and the output is a state indicating that the transmission to the server is complete.

[0479] Step 3:

[0480] The server analyzes the data received from the terminal and accesses a database of past records. Here, the server uses SQL queries to extract the user's past flight patterns and selection tendencies. The input is user data from the terminal, and the output is the retrieved past record data.

[0481] Step 4:

[0482] The server uses acquired historical data as input to generate the optimal flight route and application information using a generative AI model. The generative AI model utilizes machine learning algorithms and performs analysis according to user needs. The output is the optimized route and application information.

[0483] Step 5:

[0484] The server sends the generated flight route and application information to the terminal. Here, the server uses communication methods to convert the data into a format that the terminal can display. The input is the generated information, and the output is the confirmation that the transmission to the terminal is complete.

[0485] Step 6:

[0486] The terminal displays received data on the user interface and notifies the user. The user can review the information and make corrections as needed. Input is data from the server, and output is a user-viewable information display.

[0487] Step 7:

[0488] After completing the flight, the user provides evaluation information from the terminal to the server. This evaluation information includes feedback based on the actual flight experience. The terminal receives this information and sends it to the server. The input is the user's feedback, and the output is the completion of the transmission to the server.

[0489] Step 8:

[0490] The server uses the evaluation data to improve the generated AI model. It leverages machine learning algorithms to improve the accuracy of future flight routes and application processes. The input for this step is the evaluation data, and the output is the improved model.

[0491] (Application Example 1)

[0492] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0493] In drone-based delivery services, selecting the optimal delivery route and obtaining necessary permits are crucial for ensuring fast and safe delivery to destinations. Furthermore, inefficient procedures can burden users and reduce the overall operational efficiency of the service. Additionally, it's essential to utilize the information gathered after delivery completion as feedback to improve future delivery processes.

[0494] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0495] In this invention, the server includes a user interface means for receiving user input information and acquiring the delivery area, equipment used, delivery purpose, and date and time; an information gathering means for acquiring relevant information from a past delivery history database based on the input information; and an information processing means for automatically generating optimized delivery routes and permission information using a generative model based on the acquired information. This enables efficient selection of optimal routes and permission acquisition procedures in delivery services.

[0496] "User input information" refers to data provided by the user in the delivery service regarding the delivery area, equipment used, purpose of delivery, and date and time.

[0497] "User interface means" refers to the interface used by a user to send input information to a server.

[0498] "Information gathering means" refers to methods used by a server to retrieve relevant information from a database of past delivery history based on user input.

[0499] A "generative model" is an algorithm that automatically generates the optimal delivery route and permission information based on past data and current information.

[0500] "Information processing means" refers to the process of creating optimal delivery routes and permission information using a generative model based on acquired information.

[0501] A "notification method" is a means of informing the user of the generated delivery route and permission information, allowing them to confirm and correct it.

[0502] "Improvement measures" refer to methods of receiving feedback after delivery is completed and using that feedback to improve the accuracy of the next delivery process.

[0503] This system efficiently generates optimal delivery routes and necessary permit information for drone-based delivery services. The following describes an embodiment of this system.

[0504] The server receives user input and provides a dedicated user interface to retrieve delivery area, equipment used, delivery purpose, and date and time. This is typically implemented as a smartphone app, making it easily accessible to users.

[0505] The server runs on cloud services such as AWS Lambda and retrieves historical delivery data from Amazon RDS. This data collection method gathers relevant information based on user input.

[0506] As an information processing tool, the server utilizes a generative AI model and an optimization algorithm to automatically generate safe and efficient delivery routes. Simultaneously, it obtains necessary permission information for flying in urban areas through local government APIs. This information is then notified to the user's mobile device.

[0507] For example, when a user orders a pizza on a Sunday, the server instantly calculates the optimal route based on past data and obtains the necessary permissions. As a result, the pizza is delivered quickly, and user satisfaction increases.

[0508] After delivery is complete, users provide feedback. This feedback will be used to improve the delivery process for future deliveries.

[0509] Example prompt: "Calculate the optimal route and necessary permits for smooth food delivery using a drone this weekend. Situation: Sunday, 5 PM, delivery destination: an apartment in the city."

[0510] In this way, the system can improve efficiency and safety in delivery services.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The user uses their smartphone to input the delivery area, equipment used, delivery purpose, and date and time through the application's user interface. This data is properly formatted on the device and sent to the server. The input consists of detailed delivery information, and the output is formatted data. The device formats the data and establishes communication with the server.

[0514] Step 2:

[0515] The server uses information gathering tools running on AWS Lambda, based on the user input information received, to retrieve relevant historical delivery history data from Amazon RDS. The input is formatted user information, and the output is historical delivery data including related information. This involves querying the database and retrieving the results.

[0516] Step 3:

[0517] The server supplies acquired historical delivery data to a generating AI model, which uses an algorithm to automatically generate optimized delivery routes and necessary permission information. The input consists of historical delivery data and user request information, while the output is a specific delivery route and permission information. In this step, the AI ​​model performs data analysis and computational processing.

[0518] Step 4:

[0519] The server sends the generated delivery route and permission information to the user's device via a notification system. This includes using local government APIs to collect permission information. The input is the generated delivery route and permission information, and the output is a notification message presented to the user. The device displays this information on its screen, providing the user with an opportunity to review and correct it.

[0520] Step 5:

[0521] After delivery is complete, the user sends feedback information from their device to the server. The server receives this feedback and stores it in a database to use for improving future delivery processes. The input is user feedback information, and the output is reference data for future deliveries. Here, the feedback data is saved.

[0522] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0523] This invention is a system for streamlining drone flight preparation and improving the user experience, and in particular, by combining it with an emotion engine, it provides an even more personalized interface. The embodiments of the invention are described in detail below.

[0524] The user first accesses the system through a terminal and enters the information necessary for flight preparation. This information includes the flight area, aircraft type, flight purpose, and planned flight date and time. This information is sent from the terminal to the server, where relevant information is collected from the database.

[0525] The server references a database of past flight history based on the received information and retrieves relevant data. Furthermore, the emotion engine analyzes the user's responses during input and recognizes the user's emotional state. This emotional information is then used in a generative model to generate the optimal flight route and application information.

[0526] The generated flight route and application information are sent from the server to the terminal and notified to the user. Here, the emotion engine optimizes the user experience by adjusting the tone and responses of the interface according to the user's emotions, and providing additional explanations if the user is feeling anxious.

[0527] For example, if a user is planning their first flight in an urban area, the emotion engine will detect instability in the user's input and provide more detailed guidance. This allows the user to prepare for the flight with confidence.

[0528] After a flight is completed, the user provides feedback through their device. This feedback is processed by the server and used to improve the accuracy of the preparation process for future flights. In particular, emotion-based feedback strengthens the emotion engine model and helps improve the user experience in the future.

[0529] Thus, by incorporating an emotion engine, the present invention enables personalized responses tailored to the user's emotions, resulting in safer and more satisfying drone flight preparation.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] The user accesses the system using a terminal and enters the information necessary for drone flight. This includes the flight area, model of drone, purpose of flight, and scheduled flight date and time. The terminal prepares to send the entered information to the emotion engine.

[0533] Step 2:

[0534] The device passes the acquired user information to an emotion engine, which analyzes the user's facial expressions, operation speed, and input content during input. This allows the system to infer the user's emotional state.

[0535] Step 3:

[0536] The emotion engine determines the user's emotional state based on the analysis results and transfers this information, along with other data, to the server.

[0537] Step 4:

[0538] The server references a database of past flight history based on the received information and collects relevant data. Furthermore, it uses a generative model to create safe and efficient flight routes and application information. In this process, it also takes into account the user's emotional state identified by the emotion engine and adjusts the level of detail of the suggestions accordingly.

[0539] Step 5:

[0540] The server sends the generated flight route and application information to the terminal. The user is notified, and the emotion engine adjusts the interface according to the user's emotions. For example, if the user is feeling anxious, the explanation will be more detailed and the response will be reassuring.

[0541] Step 6:

[0542] The user reviews the information presented on the device and makes corrections or approvals as needed. At this point, the user completes final adjustments following the interface's guidance.

[0543] Step 7:

[0544] After the flight is complete, the user provides feedback to the system via their device. This feedback includes information about the flight experience and emotional state.

[0545] Step 8:

[0546] The server receives feedback and incorporates it into the emotion engine and generative model to improve the next flight preparation process. This allows the system to continuously learn and provide a higher quality user experience.

[0547] (Example 2)

[0548] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] This invention aims to streamline drone flight preparation and improve safety and user satisfaction. In particular, by taking into account the user's emotional state, it addresses the challenge of quickly responding to individual needs that conventional systems could not adequately address, thereby improving the flight experience.

[0550] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0551] In this invention, the server includes information receiving means for receiving user input information and acquiring the flight area, equipment used, flight purpose, and date and time; information gathering means for acquiring relevant information from a recording medium storing past flight records; and emotion analysis means for analyzing the user's reactions and recognizing their emotional state. This enables dynamic adjustment of the interface according to the user's emotions and the provision of optimized flight paths and application content.

[0552] "Information receiving means" refers to a function that allows the server to receive input information from the user and obtain details related to flight.

[0553] "Information gathering means" refers to a function that allows a server to retrieve relevant information from recording media containing past flight records and use it to prepare for the next flight.

[0554] An "emotion analysis tool" is an algorithm or program that analyzes user input and responses to recognize the user's current emotional state.

[0555] "Information generation means" refers to a function that automatically generates optimized flight paths and application details using a generative model based on collected data and recognized emotional states.

[0556] "Information provision means" refers to a function that presents the generated flight path and application details to the user and adjusts the interface according to the user's feelings.

[0557] "Information utilization means" refers to a function that receives feedback from users after the completion of a flight and uses this feedback to improve future processes.

[0558] This invention is a system designed to assist in drone flight preparation and aims to improve the user experience. Specifically, the user begins by accessing the system via a terminal and entering the necessary information. This information includes the flight area, the type of drone to be used, the purpose of the flight, and the scheduled flight date and time. The terminal then transmits this information to the server.

[0559] The server uses the received information to retrieve relevant data from recording media that store past flight records. This information is essential for optimizing the flight. Furthermore, the server's emotion analysis engine analyzes the user's responses during input to recognize the user's emotional state. Based on this, the server automatically generates the optimal flight route and application details using an AI model.

[0560] This system provides users with flight routes and application details generated via their terminals, and can adapt to the user's emotional state by adjusting the user interface. For example, if a user is planning a flight in an urban area for the first time, the system may sense their anxiety and display more detailed guidance.

[0561] As a concrete example, consider a scenario where a user is flying a drone in an urban area for the first time. In this case, the sentiment analysis engine would detect the user's anxiety in their input, and the system would provide the user with more detailed safety measures and map information. An example of a prompt might be, "This is my first time flying a drone in an urban area. Could you please provide a detailed guide regarding the flight area and safety?"

[0562] By incorporating emotion analysis and generative AI models in this way, more sophisticated individual responses become possible than before, resulting in drone flight preparations that are safer and more satisfying for users.

[0563] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0564] Step 1:

[0565] Users access the system using a terminal and enter the flight area, equipment to be used, flight purpose, and scheduled flight date and time. The entered information is converted into a digital format by the terminal and sent to the server. The terminal's input interface provides text boxes and drop-down menus to allow users to easily select or enter the necessary information.

[0566] Step 2:

[0567] The server receives flight information transmitted from the terminal. Based on this data, the server accesses the database and searches for past flight history data. It executes SQL queries to extract relevant historical data and associates it with the current flight plan. This allows the server to obtain reference information based on past data.

[0568] Step 3:

[0569] The server activates the sentiment analysis engine based on the user's input. It analyzes the user's input speed and keystroke patterns to infer the user's emotional state. At this stage, the sentiment analysis algorithm is executed to determine whether the user is feeling anxious or stressed. The analysis results are used for subsequent processing.

[0570] Step 4:

[0571] The server combines acquired flight history data with sentiment analysis results and uses a generative AI model to automatically generate the optimal flight path and application information. In this process, the machine learning model considers past data and the current context to calculate the most efficient and safe route. The generated data is stored in a structured format.

[0572] Step 5:

[0573] The server sends the generated flight path and application information to the terminal. The terminal receives this information and adjusts the interface according to the user's emotional state. For example, if anxiety is detected, detailed safety information and operation guides are added. The graphical user interface (GUI) on the terminal is dynamically changed and provided to the user.

[0574] Step 6:

[0575] After completing a flight, users provide feedback through a terminal. The terminal sends this feedback as digital data to a server, which is used to improve the accuracy of preparations for the next flight. The server analyzes the feedback and uses it to further improve the sentiment analysis engine and generative AI models. This continuous feedback loop accelerates the evolution of the system.

[0576] (Application Example 2)

[0577] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0578] In drone-based food delivery services, users may feel anxious about using the service for the first time or about the new delivery method. This anxiety can become a barrier to service adoption. Furthermore, if delivery routes are not optimized, safety and efficiency may be reduced. In addition, there is a challenge in providing a better user experience due to the lack of personalized support that addresses the user's emotions.

[0579] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0580] In this invention, the server includes user connection means for receiving user input information and acquiring delivery area, equipment to be used, delivery purpose, and date and time; data collection means for acquiring relevant information from a past delivery performance database; and data processing means for automatically generating delivery routes and application information. This makes it possible to provide appropriate responses according to the user's emotional state in drone-based food delivery, thereby reducing anxiety and providing the optimal delivery route.

[0581] "User input information" refers to information provided by the user when using the service, including the delivery area, equipment used, delivery purpose, and date and time.

[0582] "User connection means" refers to a mechanism for users to interact with the system through an interface, and includes the function of receiving user input information.

[0583] A "data collection method" is a means that assists in current delivery preparations by extracting relevant information from a database of past delivery performance.

[0584] A "data processing method" is a means that has the function of optimizing delivery routes and application information using a generative model based on acquired data, and automatically generating them anew.

[0585] A "notification system" is a system that provides users with generated delivery routes and application information, and prompts them to confirm or correct the content.

[0586] An "emotion analysis tool" is a tool that analyzes a user's emotional state from their input information and has the function of providing an appropriate response tailored to each individual user.

[0587] A "feedback system" is a system for collecting user feedback after delivery is complete and using it to improve the process for the next time.

[0588] One embodiment of this invention involves a server receiving information entered by a user's terminal to efficiently carry out food delivery by drone. The terminal acquires information entered by the user, such as the delivery area, the equipment to be used, the purpose of delivery, and the time. This information is transmitted to the server, which then uses this information to refer to a database of past delivery performance and collects relevant information.

[0589] The server processes data using programming languages ​​such as Python, and optimizes and generates delivery routes and application information using navigation libraries and generative AI models. This optimization can utilize the computing power of Amazon AWS and Google Cloud. The server also uses an emotion analysis engine to analyze the user's emotional state and adjust the delivery process based on this analysis.

[0590] The generated information is sent from the server to the user's device, where they are prompted to review and correct it through the interface. For example, if a user is using the service for the first time, detailed notifications such as the drone's progress and estimated arrival time can help reduce anxiety. User feedback is returned to the server via the device and used to further improve the delivery experience.

[0591] For example, by inputting a prompt message into the AI ​​model such as, "Assess the user's concerns about the new delivery method and suggest appropriate advice," it is possible to further optimize the user experience.

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The terminal receives information entered by the user regarding the delivery area, equipment used, delivery purpose, and date and time. This input information forms the basis of the data sent to the server.

[0595] Step 2:

[0596] The server retrieves relevant information from the past delivery performance database based on the user's input. It queries the database to find similar delivery records and generates a dataset for use in the next step.

[0597] Step 3:

[0598] The server uses the acquired dataset to optimize delivery routes and application information using a generative AI model written in Python. Leveraging a navigation library, it generates delivery routes that consider geographical data and safety factors, and the system outputs these generated delivery routes.

[0599] Step 4:

[0600] The server uses an emotion analysis engine to analyze the user's emotional state at the time of input. It extracts emotion-related features from the input information and inputs them into an emotion evaluation model to recognize the user's emotional state. The output is the user's emotion as a numerical value or category.

[0601] Step 5:

[0602] The server sends the generated delivery route and application information, as well as the sentiment analysis results, to the terminal. This information is then provided to the user through a notification system, and an interface for confirmation and modification is displayed.

[0603] Step 6:

[0604] The user reviews the provided delivery route and application information and makes any necessary corrections. They then enter their authorization via the terminal to initiate delivery, and this information is sent to the server.

[0605] Step 7:

[0606] After delivery is complete, the device receives feedback from the user and sends it to the server. The server analyzes the feedback and uses it for future improvements. This process aims to improve the experience based on emotions, and the server uses the feedback to update the emotion engine and generative AI model.

[0607] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0608] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0609] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0610] [Fourth Embodiment]

[0611] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0612] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0613] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0614] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0615] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0616] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0617] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0618] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0619] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0620] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0621] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0622] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0623] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0624] This invention is a system for streamlining drone flight preparation, which automatically generates the optimal flight route and application details based on flight information provided by the user. The embodiments thereof are described below.

[0625] The user accesses the system through a terminal and enters necessary information such as the drone's flight area, model, flight purpose, and expected flight date and time. The terminal formats this data and establishes communication to send it to the server.

[0626] The server accesses a database of past flight history based on the received information and retrieves relevant data. This data includes the user's past flight patterns and selection tendencies, forming the basis for analysis by the generative model. The generative model utilizes this collected information to calculate a safe and optimal flight route for the user and generates application information as needed.

[0627] The server then sends the generated flight route and application information to the terminal and notifies the user. The user can review this information on the terminal and make corrections or approvals as needed. This allows the user to efficiently complete pre-flight preparations.

[0628] For example, if a user plans to fly over an urban area, the server cross-references map information of the urban area with historical flight data to provide the optimal route to avoid obstacles. Furthermore, necessary permit information for flying over commercial areas is automatically generated, simplifying the user's procedures.

[0629] After completing a flight, the user provides feedback to the system from their device. This feedback is received by the server and used to improve the accuracy of future flight routes and the application process. This allows the system to continuously learn and prepare users for more efficient and safer drone flights.

[0630] Thus, the present invention reduces the burden on users in preparing for flight and enables improvements in safety and efficiency.

[0631] The following describes the processing flow.

[0632] Step 1:

[0633] The user accesses the system from their terminal and enters the information necessary for drone flight, such as the flight area, aircraft type, flight purpose, and expected flight date and time. This information is appropriately formatted on the terminal and prepared for transmission.

[0634] Step 2:

[0635] The terminal sends the user's input information to the server. At this point, the terminal checks the stability of the data communication and, if necessary, resends the data or sends an acknowledgment.

[0636] Step 3:

[0637] The server accesses a database of past flight history based on the received information and collects relevant data. During this process, map information and safety margins related to the flight area are also obtained.

[0638] Step 4:

[0639] The server analyzes the collected data using a generative model and calculates the optimal flight route. It takes into account obstacles and local regulations to calculate the shortest distance while ensuring safety.

[0640] Step 5:

[0641] The server generates the calculated flight route and necessary application information and sends it to the terminal. The user is notified, and the terminal displays an interface for confirmation.

[0642] Step 6:

[0643] The user reviews the information provided on their device and makes any necessary corrections or approvals. At this point, the user makes final adjustments and completes the flight preparations.

[0644] Step 7:

[0645] After the flight is complete, the user provides feedback through their device. This feedback includes information about the flight's results and experience.

[0646] Step 8:

[0647] The server analyzes the received feedback and uses it to generate future flight routes and application information. The feedback data is then incorporated into the generation model to improve the system's accuracy.

[0648] (Example 1)

[0649] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0650] When flying drones, there is a need for a system that efficiently handles the formulation of appropriate flight routes and application procedures. However, current systems rely on manual processes for route optimization and the generation of application information, which are time-consuming and labor-intensive, and can sometimes lack accuracy. Furthermore, the system does not effectively utilize past flight history and feedback, which hinders continuous system improvement.

[0651] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0652] In this invention, the server includes information gathering means for acquiring relevant information from a past record database based on user input information, information processing means for automatically generating optimized routes and application information using a generation AI model based on the acquired information, and communication means for providing the generated routes and application information to the user and enabling confirmation and modification. This reduces the burden of preparing the drone for flight and enables efficient and safe flight.

[0653] "User input information" refers to data related to the area, equipment, purpose, and date and time of drone flight.

[0654] "Means of exchange between humans and machines" refers to methods of providing an interface for users to input and verify information.

[0655] "Information gathering means" refers to the processes and techniques for obtaining relevant data from historical record databases.

[0656] A "generative AI model" refers to artificial intelligence technology used to automatically generate optimized routes and application information based on available data.

[0657] "Information processing means" refers to a system that uses acquired data to calculate routes and application information.

[0658] "Communication means" refers to a method for providing the generated route and application information to the user and prompting them to confirm and correct it.

[0659] "Evaluation information" refers to feedback data provided by users after completing an action, and is information that can be used to improve the accuracy of future processes.

[0660] A "machine learning algorithm" refers to a data processing technique that uses evaluation information to improve the performance of a system.

[0661] "Route" refers to the trajectory or flight path optimized for drone flight.

[0662] "Application information" refers to data related to permits and document applications required for drone flight.

[0663] This invention is a system for streamlining drone flight preparation and achieving safe and optimal flight. The system consists of three main components: a server, a terminal, and a user.

[0664] The user uses a terminal to input information about the drone flight. This information includes the flight area, equipment, purpose, and date and time. The terminal is equipped with a user interface that collects the information entered by the user, and the input is performed through this interface.

[0665] The terminal formats the entered information appropriately and sends it to the server. Communication is established between the terminal and the server, and the data is converted into a format that the server can process.

[0666] Based on the received information, the server accesses a database containing historical records. Using information gathering tools, the server obtains relevant information, including the user's past flight patterns and selection tendencies. In this process, a generative AI model is utilized to automatically generate the optimal flight route and application information. The generative AI model incorporates machine learning algorithms, performing analysis and optimization tailored to the user's needs.

[0667] The generated information is sent back to the terminal and provided to the user via communication. The user can review the flight route and application information and make corrections as needed.

[0668] As a concrete example, consider a scenario where a user plans a flight over an urban area. The server uses urban map information and historical flight data to provide the optimal route to avoid obstacles. Furthermore, the necessary permit information for flying in commercial areas is automatically generated, allowing the user to proceed with the necessary procedures quickly.

[0669] After completing a flight, the user provides evaluation information from their terminal to the server. The server accepts this feedback and uses it to improve future flights and the application process. This evaluation information also contributes to improving the generated AI model, allowing the system to continuously learn.

[0670] An example of a prompt might be, "Automatically generate the optimal route and application procedures for drone flight in urban areas." Upon receiving this prompt, the system automatically performs the necessary analysis and procedures and provides the user with the best possible recommendations.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The user uses a terminal to input flight information. This input includes the flight area, equipment, purpose, and date and time. The terminal receives the input data and formats it into a format the program can understand. The input here is the user's flight plan, and the output is formatted data that the server can receive.

[0674] Step 2:

[0675] The terminal sends formatted data to the server. The terminal establishes a communication protocol and transfers the data to the server. In this process, the input is formatted user information, and the output is a state indicating that the transmission to the server is complete.

[0676] Step 3:

[0677] The server analyzes the data received from the terminal and accesses a database of past records. Here, the server uses SQL queries to extract the user's past flight patterns and selection tendencies. The input is user data from the terminal, and the output is the retrieved past record data.

[0678] Step 4:

[0679] The server uses acquired historical data as input to generate the optimal flight route and application information using a generative AI model. The generative AI model utilizes machine learning algorithms and performs analysis according to user needs. The output is the optimized route and application information.

[0680] Step 5:

[0681] The server sends the generated flight route and application information to the terminal. Here, the server uses communication methods to convert the data into a format that the terminal can display. The input is the generated information, and the output is the confirmation that the transmission to the terminal is complete.

[0682] Step 6:

[0683] The terminal displays received data on the user interface and notifies the user. The user can review the information and make corrections as needed. Input is data from the server, and output is a user-viewable information display.

[0684] Step 7:

[0685] After completing the flight, the user provides evaluation information from the terminal to the server. This evaluation information includes feedback based on the actual flight experience. The terminal receives this information and sends it to the server. The input is the user's feedback, and the output is the completion of the transmission to the server.

[0686] Step 8:

[0687] The server uses the evaluation data to improve the generated AI model. It leverages machine learning algorithms to improve the accuracy of future flight routes and application processes. The input for this step is the evaluation data, and the output is the improved model.

[0688] (Application Example 1)

[0689] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] In drone-based delivery services, selecting the optimal delivery route and obtaining necessary permits are crucial for ensuring fast and safe delivery to destinations. Furthermore, inefficient procedures can burden users and reduce the overall operational efficiency of the service. Additionally, it's essential to utilize the information gathered after delivery completion as feedback to improve future delivery processes.

[0691] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0692] In this invention, the server includes a user interface means for receiving user input information and acquiring the delivery area, equipment used, delivery purpose, and date and time; an information gathering means for acquiring relevant information from a past delivery history database based on the input information; and an information processing means for automatically generating optimized delivery routes and permission information using a generative model based on the acquired information. This enables efficient selection of optimal routes and permission acquisition procedures in delivery services.

[0693] "User input information" refers to data provided by the user in the delivery service regarding the delivery area, equipment used, purpose of delivery, and date and time.

[0694] "User interface means" refers to the interface used by a user to send input information to a server.

[0695] "Information gathering means" refers to methods used by a server to retrieve relevant information from a database of past delivery history based on user input.

[0696] A "generative model" is an algorithm that automatically generates the optimal delivery route and permission information based on past data and current information.

[0697] "Information processing means" refers to the process of creating optimal delivery routes and permission information using a generative model based on acquired information.

[0698] A "notification method" is a means of informing the user of the generated delivery route and permission information, allowing them to confirm and correct it.

[0699] "Improvement measures" refer to methods of receiving feedback after delivery is completed and using that feedback to improve the accuracy of the next delivery process.

[0700] This system efficiently generates optimal delivery routes and necessary permit information for drone-based delivery services. The following describes an embodiment of this system.

[0701] The server receives user input and provides a dedicated user interface to retrieve delivery area, equipment used, delivery purpose, and date and time. This is typically implemented as a smartphone app, making it easily accessible to users.

[0702] The server runs on cloud services such as AWS Lambda and retrieves historical delivery data from Amazon RDS. This data collection method gathers relevant information based on user input.

[0703] As an information processing tool, the server utilizes a generative AI model and an optimization algorithm to automatically generate safe and efficient delivery routes. Simultaneously, it obtains necessary permission information for flying in urban areas through local government APIs. This information is then notified to the user's mobile device.

[0704] For example, when a user orders a pizza on a Sunday, the server instantly calculates the optimal route based on past data and obtains the necessary permissions. As a result, the pizza is delivered quickly, and user satisfaction increases.

[0705] After delivery is complete, users provide feedback. This feedback will be used to improve the delivery process for future deliveries.

[0706] Example prompt: "Calculate the optimal route and necessary permits for smooth food delivery using a drone this weekend. Situation: Sunday, 5 PM, delivery destination: an apartment in the city."

[0707] In this way, the system can improve efficiency and safety in delivery services.

[0708] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0709] Step 1:

[0710] The user uses their smartphone to input the delivery area, equipment used, delivery purpose, and date and time through the application's user interface. This data is properly formatted on the device and sent to the server. The input consists of detailed delivery information, and the output is formatted data. The device formats the data and establishes communication with the server.

[0711] Step 2:

[0712] The server uses information gathering tools running on AWS Lambda, based on the user input information received, to retrieve relevant historical delivery history data from Amazon RDS. The input is formatted user information, and the output is historical delivery data including related information. This involves querying the database and retrieving the results.

[0713] Step 3:

[0714] The server supplies acquired historical delivery data to a generating AI model, which uses an algorithm to automatically generate optimized delivery routes and necessary permission information. The input consists of historical delivery data and user request information, while the output is a specific delivery route and permission information. In this step, the AI ​​model performs data analysis and computational processing.

[0715] Step 4:

[0716] The server sends the generated delivery route and permission information to the user's device via a notification system. This includes using local government APIs to collect permission information. The input is the generated delivery route and permission information, and the output is a notification message presented to the user. The device displays this information on its screen, providing the user with an opportunity to review and correct it.

[0717] Step 5:

[0718] After delivery is complete, the user sends feedback information from their device to the server. The server receives this feedback and stores it in a database to use for improving future delivery processes. The input is user feedback information, and the output is reference data for future deliveries. Here, the feedback data is saved.

[0719] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0720] This invention is a system for streamlining drone flight preparation and improving the user experience, and in particular, by combining it with an emotion engine, it provides an even more personalized interface. The embodiments of the invention are described in detail below.

[0721] The user first accesses the system through a terminal and enters the information necessary for flight preparation. This information includes the flight area, aircraft type, flight purpose, and planned flight date and time. This information is sent from the terminal to the server, where relevant information is collected from the database.

[0722] The server references a database of past flight history based on the received information and retrieves relevant data. Furthermore, the emotion engine analyzes the user's responses during input and recognizes the user's emotional state. This emotional information is then used in a generative model to generate the optimal flight route and application information.

[0723] The generated flight route and application information are sent from the server to the terminal and notified to the user. Here, the emotion engine optimizes the user experience by adjusting the tone and responses of the interface according to the user's emotions, and providing additional explanations if the user is feeling anxious.

[0724] For example, if a user is planning their first flight in an urban area, the emotion engine will detect instability in the user's input and provide more detailed guidance. This allows the user to prepare for the flight with confidence.

[0725] After a flight is completed, the user provides feedback through their device. This feedback is processed by the server and used to improve the accuracy of the preparation process for future flights. In particular, emotion-based feedback strengthens the emotion engine model and helps improve the user experience in the future.

[0726] Thus, by incorporating an emotion engine, the present invention enables personalized responses tailored to the user's emotions, resulting in safer and more satisfying drone flight preparation.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] The user accesses the system using a terminal and enters the information necessary for drone flight. This includes the flight area, model of drone, purpose of flight, and scheduled flight date and time. The terminal prepares to send the entered information to the emotion engine.

[0730] Step 2:

[0731] The device passes the acquired user information to an emotion engine, which analyzes the user's facial expressions, operation speed, and input content during input. This allows the system to infer the user's emotional state.

[0732] Step 3:

[0733] The emotion engine determines the user's emotional state based on the analysis results and transfers this information, along with other data, to the server.

[0734] Step 4:

[0735] The server references a database of past flight history based on the received information and collects relevant data. Furthermore, it uses a generative model to create safe and efficient flight routes and application information. In this process, it also takes into account the user's emotional state identified by the emotion engine and adjusts the level of detail of the suggestions accordingly.

[0736] Step 5:

[0737] The server sends the generated flight route and application information to the terminal. The user is notified, and the emotion engine adjusts the interface according to the user's emotions. For example, if the user is feeling anxious, the explanation will be more detailed and the response will be reassuring.

[0738] Step 6:

[0739] The user reviews the information presented on the device and makes corrections or approvals as needed. At this point, the user completes final adjustments following the interface's guidance.

[0740] Step 7:

[0741] After the flight is complete, the user provides feedback to the system via their device. This feedback includes information about the flight experience and emotional state.

[0742] Step 8:

[0743] The server receives feedback and incorporates it into the emotion engine and generative model to improve the next flight preparation process. This allows the system to continuously learn and provide a higher quality user experience.

[0744] (Example 2)

[0745] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] This invention aims to streamline drone flight preparation and improve safety and user satisfaction. In particular, by taking into account the user's emotional state, it addresses the challenge of quickly responding to individual needs that conventional systems could not adequately address, thereby improving the flight experience.

[0747] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0748] In this invention, the server includes information receiving means for receiving user input information and acquiring the flight area, equipment used, flight purpose, and date and time; information gathering means for acquiring relevant information from a recording medium storing past flight records; and emotion analysis means for analyzing the user's reactions and recognizing their emotional state. This enables dynamic adjustment of the interface according to the user's emotions and the provision of optimized flight paths and application content.

[0749] "Information receiving means" refers to a function that allows the server to receive input information from the user and obtain details related to flight.

[0750] "Information gathering means" refers to a function that allows a server to retrieve relevant information from recording media containing past flight records and use it to prepare for the next flight.

[0751] An "emotion analysis tool" is an algorithm or program that analyzes user input and responses to recognize the user's current emotional state.

[0752] "Information generation means" refers to a function that automatically generates optimized flight paths and application details using a generative model based on collected data and recognized emotional states.

[0753] "Information provision means" refers to a function that presents the generated flight path and application details to the user and adjusts the interface according to the user's feelings.

[0754] "Information utilization means" refers to a function that receives feedback from users after the completion of a flight and uses this feedback to improve future processes.

[0755] This invention is a system designed to assist in drone flight preparation and aims to improve the user experience. Specifically, the user begins by accessing the system via a terminal and entering the necessary information. This information includes the flight area, the type of drone to be used, the purpose of the flight, and the scheduled flight date and time. The terminal then transmits this information to the server.

[0756] The server uses the received information to retrieve relevant data from recording media that store past flight records. This information is essential for optimizing the flight. Furthermore, the server's emotion analysis engine analyzes the user's responses during input to recognize the user's emotional state. Based on this, the server automatically generates the optimal flight route and application details using an AI model.

[0757] This system provides users with flight routes and application details generated via their terminals, and can adapt to the user's emotional state by adjusting the user interface. For example, if a user is planning a flight in an urban area for the first time, the system may sense their anxiety and display more detailed guidance.

[0758] As a concrete example, consider a scenario where a user is flying a drone in an urban area for the first time. In this case, the sentiment analysis engine would detect the user's anxiety in their input, and the system would provide the user with more detailed safety measures and map information. An example of a prompt might be, "This is my first time flying a drone in an urban area. Could you please provide a detailed guide regarding the flight area and safety?"

[0759] By incorporating emotion analysis and generative AI models in this way, more sophisticated individual responses become possible than before, resulting in drone flight preparations that are safer and more satisfying for users.

[0760] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0761] Step 1:

[0762] Users access the system using a terminal and enter the flight area, equipment to be used, flight purpose, and scheduled flight date and time. The entered information is converted into a digital format by the terminal and sent to the server. The terminal's input interface provides text boxes and drop-down menus to allow users to easily select or enter the necessary information.

[0763] Step 2:

[0764] The server receives flight information transmitted from the terminal. Based on this data, the server accesses the database and searches for past flight history data. It executes SQL queries to extract relevant historical data and associates it with the current flight plan. This allows the server to obtain reference information based on past data.

[0765] Step 3:

[0766] The server activates the sentiment analysis engine based on the user's input. It analyzes the user's input speed and keystroke patterns to infer the user's emotional state. At this stage, the sentiment analysis algorithm is executed to determine whether the user is feeling anxious or stressed. The analysis results are used for subsequent processing.

[0767] Step 4:

[0768] The server combines acquired flight history data with sentiment analysis results and uses a generative AI model to automatically generate the optimal flight path and application information. In this process, the machine learning model considers past data and the current context to calculate the most efficient and safe route. The generated data is stored in a structured format.

[0769] Step 5:

[0770] The server sends the generated flight path and application information to the terminal. The terminal receives this information and adjusts the interface according to the user's emotional state. For example, if anxiety is detected, detailed safety information and operation guides are added. The graphical user interface (GUI) on the terminal is dynamically changed and provided to the user.

[0771] Step 6:

[0772] After completing a flight, users provide feedback through a terminal. The terminal sends this feedback as digital data to a server, which is used to improve the accuracy of preparations for the next flight. The server analyzes the feedback and uses it to further improve the sentiment analysis engine and generative AI models. This continuous feedback loop accelerates the evolution of the system.

[0773] (Application Example 2)

[0774] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0775] In drone-based food delivery services, users may feel anxious about using the service for the first time or about the new delivery method. This anxiety can become a barrier to service adoption. Furthermore, if delivery routes are not optimized, safety and efficiency may be reduced. In addition, there is a challenge in providing a better user experience due to the lack of personalized support that addresses the user's emotions.

[0776] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0777] In this invention, the server includes user connection means for receiving user input information and acquiring delivery area, equipment to be used, delivery purpose, and date and time; data collection means for acquiring relevant information from a past delivery performance database; and data processing means for automatically generating delivery routes and application information. This makes it possible to provide appropriate responses according to the user's emotional state in drone-based food delivery, thereby reducing anxiety and providing the optimal delivery route.

[0778] "User input information" refers to information provided by the user when using the service, including the delivery area, equipment used, delivery purpose, and date and time.

[0779] "User connection means" refers to a mechanism for users to interact with the system through an interface, and includes the function of receiving user input information.

[0780] A "data collection method" is a means that assists in current delivery preparations by extracting relevant information from a database of past delivery performance.

[0781] A "data processing method" is a means that has the function of optimizing delivery routes and application information using a generative model based on acquired data, and automatically generating them anew.

[0782] A "notification system" is a system that provides users with generated delivery routes and application information, and prompts them to confirm or correct the content.

[0783] An "emotion analysis tool" is a tool that analyzes a user's emotional state from their input information and has the function of providing an appropriate response tailored to each individual user.

[0784] A "feedback system" is a system for collecting user feedback after delivery is complete and using it to improve the process for the next time.

[0785] One embodiment of this invention involves a server receiving information entered by a user's terminal to efficiently carry out food delivery by drone. The terminal acquires information entered by the user, such as the delivery area, the equipment to be used, the purpose of delivery, and the time. This information is transmitted to the server, which then uses this information to refer to a database of past delivery performance and collects relevant information.

[0786] The server processes data using programming languages ​​such as Python, and optimizes and generates delivery routes and application information using navigation libraries and generative AI models. This optimization can utilize the computing power of Amazon AWS and Google Cloud. The server also uses an emotion analysis engine to analyze the user's emotional state and adjust the delivery process based on this analysis.

[0787] The generated information is sent from the server to the user's device, where they are prompted to review and correct it through the interface. For example, if a user is using the service for the first time, detailed notifications such as the drone's progress and estimated arrival time can help reduce anxiety. User feedback is returned to the server via the device and used to further improve the delivery experience.

[0788] For example, by inputting a prompt message into the AI ​​model such as, "Assess the user's concerns about the new delivery method and suggest appropriate advice," it is possible to further optimize the user experience.

[0789] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0790] Step 1:

[0791] The terminal receives information entered by the user regarding the delivery area, equipment used, delivery purpose, and date and time. This input information forms the basis of the data sent to the server.

[0792] Step 2:

[0793] The server retrieves relevant information from the past delivery performance database based on the user's input. It queries the database to find similar delivery records and generates a dataset for use in the next step.

[0794] Step 3:

[0795] The server uses the acquired dataset to optimize delivery routes and application information using a generative AI model written in Python. Leveraging a navigation library, it generates delivery routes that consider geographical data and safety factors, and the system outputs these generated delivery routes.

[0796] Step 4:

[0797] The server uses an emotion analysis engine to analyze the user's emotional state at the time of input. It extracts emotion-related features from the input information and inputs them into an emotion evaluation model to recognize the user's emotional state. The output is the user's emotion as a numerical value or category.

[0798] Step 5:

[0799] The server sends the generated delivery route and application information, as well as the sentiment analysis results, to the terminal. This information is then provided to the user through a notification system, and an interface for confirmation and modification is displayed.

[0800] Step 6:

[0801] The user reviews the provided delivery route and application information and makes any necessary corrections. They then enter their authorization via the terminal to initiate delivery, and this information is sent to the server.

[0802] Step 7:

[0803] After delivery is complete, the device receives feedback from the user and sends it to the server. The server analyzes the feedback and uses it for future improvements. This process aims to improve the experience based on emotions, and the server uses the feedback to update the emotion engine and generative AI model.

[0804] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0805] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0806] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0807] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0808] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0809] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0810] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0811] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0812] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0813] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0814] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0815] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0816] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0817] 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.

[0818] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0819] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0820] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0821] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0822] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0823] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0824] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0825] The following is further disclosed regarding the embodiments described above.

[0826] (Claim 1)

[0827] A user interface means that receives user input information and obtains the flight area, aircraft type, flight purpose, and date and time,

[0828] A data collection means that obtains relevant information from a past flight history database based on the aforementioned input information,

[0829] A data processing means that automatically generates optimized flight routes and application information using the acquired information,

[0830] A notification mechanism that provides the user with the generated flight route and application information, and allows for confirmation and modification.

[0831] A feedback mechanism for receiving feedback after the completion of the aforementioned flight and utilizing it to improve the accuracy of future processes,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, characterized in that the data processing means calculates a safe flight route taking into account map information of the flight area and safety factors.

[0835] (Claim 3)

[0836] The system according to claim 1, characterized in that the notification means provides an interface that prompts the user to confirm and approve the generated information.

[0837] "Example 1"

[0838] (Claim 1)

[0839] A means of human-machine exchange of information, which receives user input information and obtains flight area, equipment, purpose, and date and time.

[0840] Based on the aforementioned input information, an information gathering means obtains relevant information from a past record database,

[0841] Information processing means that automatically generates optimized routes and application information using the acquired information,

[0842] A communication means that provides the generated route and application information to a human, enabling verification and correction,

[0843] An evaluation information means that receives evaluation information after the completion of the aforementioned action and uses it to improve the accuracy of future processes,

[0844] The information processing means includes means for simulating the evaluation information using a machine learning algorithm,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, characterized in that the information processing means calculates a safe route taking into account map information of the flight area and safety factors.

[0848] (Claim 3)

[0849] The system according to claim 1, characterized in that the communication means provides a means for a human and a machine to exchange information, prompting the human to confirm and approve the generated information.

[0850] "Application Example 1"

[0851] (Claim 1)

[0852] A user interface means that receives user input information and obtains the delivery area, equipment used, delivery purpose, and date and time,

[0853] Based on the aforementioned input information, an information gathering means is provided to obtain relevant information from a past delivery history database.

[0854] Information processing means that automatically generates optimized delivery routes and permission information using the acquired information,

[0855] A notification mechanism that provides the user with generated delivery routes and permission information, and allows for confirmation and modification.

[0856] The feedback received after the completion of the aforementioned delivery will be used as an improvement measure to improve the accuracy of future procedures,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, characterized in that the information processing means calculates a safe delivery route taking into account map information of the delivery area and safety factors.

[0860] (Claim 3)

[0861] The system according to claim 1, characterized in that the notification means provides an interface that prompts the user to confirm and approve the generated information.

[0862] "Example 2 of combining an emotion engine"

[0863] (Claim 1)

[0864] Information receiving means that receives user input information and obtains the flight area, equipment used, flight purpose, and date and time,

[0865] Information gathering means that, based on the aforementioned input information, acquires relevant information from a recording medium storing past flight records,

[0866] An emotion analysis method that analyzes user reactions to recognize emotional states,

[0867] An information generation means that automatically generates an optimized flight path and application content using a generative model based on acquired information and recognized emotional states,

[0868] An information provision means that provides the user with the generated flight path and application details, and dynamically adjusts the interface according to the user's emotional state,

[0869] A means of utilizing information to receive feedback after the completion of the flight and use it to improve the accuracy of future processes,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, characterized in that the information generation means optimizes the flight path and application content generated using the emotion analysis results for the user.

[0873] (Claim 3)

[0874] The system according to claim 1, characterized in that the information providing means provides detailed explanations and guides based on the user's emotional state and provides an interface that prompts the user to confirm and correct.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A user connection means that receives user input information and obtains the delivery area, equipment used, delivery purpose, and date and time,

[0878] Based on the aforementioned input information, a data collection means obtains relevant information from a past delivery performance database,

[0879] A data processing means that uses the acquired information to automatically generate optimized delivery routes and application information using a generative model,

[0880] A notification mechanism that provides the user with the generated delivery route and application information, and allows for confirmation and modification.

[0881] A sentiment analysis tool that analyzes the user's emotional state and provides an appropriate response,

[0882] A feedback mechanism to receive feedback after the completion of the aforementioned delivery and to use it to improve the accuracy of future processes,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, characterized in that the data processing means calculates a safe delivery route considering map information and safety factors of the delivery area.

[0886] (Claim 3)

[0887] The system according to claim 1, characterized in that the notification means provides an interface that prompts the user to confirm and approve the generated information. [Explanation of symbols]

[0888] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A user interface means that receives user input information and obtains the flight area, aircraft type, flight purpose, and date and time, A data collection means that obtains relevant information from a past flight history database based on the aforementioned input information, A data processing means that automatically generates optimized flight routes and application information using the acquired information, A notification mechanism that provides the user with the generated flight route and application information, and allows for confirmation and modification. A feedback mechanism for receiving feedback after the completion of the aforementioned flight and utilizing it to improve the accuracy of future processes, A system that includes this.

2. The system according to claim 1, characterized in that the data processing means calculates a safe flight route taking into account map information of the flight area and safety factors.

3. The system according to claim 1, characterized in that the notification means provides an interface that prompts the user to confirm and approve the generated information.

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