System

Abstract

To provide a system.SOLUTION: A system includes: moving means using drone technology; information processing means using generative AI, learning means based on the evolution of the generative AI; means for receiving instructions from a user, generating information using the generative AI model, and providing analysis results to the user; means for learning optimal actions according to the user's behavioral patterns and circumstances and taking action based on that; and problem-solving means for solving the user's problems using the moving means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a need for efficient and practical systems to solve issues such as providing support for the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters. [Means for solving the problem]

[0005] This invention solves the above problems by combining a means of transportation using drone technology, an information processing means using generative AI, and a learning means based on the evolution of generative AI. Specifically, the means for controlling drone flight enables the solution of problems such as supporting the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters. Furthermore, the means for generating and analyzing information using generative AI enables the solution of these problems more efficiently. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] An example of this invention is a system that combines a means of transportation using drone technology, an information processing means using generative AI, and a learning means based on the evolution of generative AI. Specifically, the drone uses GPS, sensors, etc. to fly autonomously, traveling to a destination and transporting supplies. The generative AI generates and analyzes the necessary information based on user instructions and the situation. The learning means based on the evolution of generative AI learns optimal behavior based on the user's behavior patterns and the situation, and acts based on that. By combining these means, it is possible to solve issues such as supporting the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[0029] "Example 2"

[0030] A concrete example is a system that provides support for the daily lives of the elderly. Drones assist the elderly with mobility and deliver necessary supplies. Generative AI understands the health status and lifestyle habits of the elderly and generates the necessary information. The learning methods that result from the evolution of generative AI learn the behavioral patterns of the elderly and provide optimal support based on that. Combining these methods can improve the quality of daily life for the elderly.

[0031] "Example 3"

[0032] Another possible system is to provide effective support to remote and disaster-stricken areas. Drones will transport supplies and collect information to remote and disaster-stricken areas. Generative AI will determine the support needed based on the collected information, and the learning method that evolves through generative AI will learn the optimal support method from past experience. By combining these methods, it will be possible to provide rapid and effective support to remote and disaster-stricken areas.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The drone begins autonomous flight using GPS, sensors, etc.

[0036] Step 2: Transport to the destination and deliver supplies.

[0037] Step 3: The generative AI generates and analyzes the necessary information based on user instructions and the situation.

[0038] Step 4: Generative AI will evolve to learn optimal behavior based on the user's behavioral patterns and circumstances, and then act accordingly.

[0039] "Example 2"

[0040] Step 1: Drones will assist elderly people with mobility assistance and deliver necessary supplies.

[0041] Step 2: The generative AI understands the health status and lifestyle habits of the elderly and generates the necessary information.

[0042] Step 3: The learning means of generative AI evolves to learn the behavioral patterns of the elderly and provide optimal support based on that.

[0043] "Example 3"

[0044] Step 1: Drones will transport supplies and gather information in remote areas and disaster zones.

[0045] Step 2: The generative AI determines the necessary support based on the collected information.

[0046] Step 3: The learning method used by the generative AI evolves to learn the optimal support method from past experience.

[0047] Example 1

[0048] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0049] There are many challenges, such as providing support for the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, protecting the environment, and preventing natural disasters. Conventional technologies have not been able to adequately address these challenges. In particular, there is a need for learning and executing optimal actions according to the user's behavioral patterns and situations.

[0050] The identification processing by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for receiving instructions from a user, generating information using a generative AI model, and providing the user with the analysis results, means for learning optimal behavior based on the user's behavioral patterns and situation and taking action based on the learned behavior, means for transportation using drone technology, information processing means using generative AI, and learning means through the evolution of generative AI. This enables daily life support for the elderly and people with physical limitations, effective support for remote areas and disaster areas, environmental protection, and natural disaster prevention.

[0051] "Drone technology" refers to the use of unmanned aerial vehicles to fly autonomously and perform functions such as transporting goods and conducting surveillance.

[0052] "Generative AI" is a type of artificial intelligence that generates and analyzes necessary information based on user instructions and the situation.

[0053] A "generative AI model" is a specific implementation of generative AI, such as an algorithm that generates text and data using a natural language processing model.

[0054] "Learning means" refers to technology that learns optimal behavior based on the user's behavioral patterns and circumstances, and then acts based on that.

[0055] "Information processing means" refers to technology for generating and analyzing information using generative AI.

[0056] The "means for receiving instructions from the user" refers to an interface or communication means by which the system receives instructions input by the user.

[0057] "Means for providing analysis results to users" refers to technology for displaying or notifying the generated information and analysis results to users.

[0058] "Behavioral patterns" refer to the tendencies of the actions and choices that a user makes on a daily basis.

[0059] "Optimal action" refers to the action that is most suitable for the user's situation and purpose.

[0060] "Transportation" refers to technology that uses drone technology to transport goods and people to their destinations.

[0061] "Problem-solving means" are technologies and methods for solving specific problems, such as supporting the daily lives of the elderly and people with physical limitations, providing support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[0062] This invention is a system that combines drone technology, generative AI, and learning methods enabled by the evolution of generative AI, and its aims are to support the daily lives of the elderly and people with physical limitations, provide effective support to remote areas and disaster-stricken areas, protect the environment, and prevent natural disasters.

[0063] Server Roles

[0064] The server receives instructions from the user, generates information using a generative AI model, and provides the analysis results to the user. Specifically, the server uses a generative AI model (e.g., GPT-4 (registered trademark)) implemented using Python. The server inputs the user's prompt into the generative AI model and analyzes the generated information. The analysis results are also used as data for learning optimal behavior based on the user's behavioral patterns and circumstances.

[0065] Device Role

[0066] The terminal provides an interface for inputting instructions from the user. For example, a smartphone or tablet can be used as the terminal. When the user inputs "Tell me the best route to my next destination" into the terminal, the terminal sends that information to the server. The generated information returned from the server is then displayed to the user.

[0067] User Roles

[0068] The user issues instructions to the system via a terminal. For example, they might input, "I would like to request the delivery of supplies to a disaster area." Based on the user's instructions, the drone will fly autonomously and deliver the supplies to the specified destination. The user can then decide their next action based on the information provided by the generative AI.

[0069] Specific examples

[0070] Example 1: Assisting the elderly in their daily lives

[0071] The elderly person types "Confirm my next hospital appointment" into the device. The device sends the information to the server, which uses generative AI to generate and analyze the appointment information. The analysis results are returned to the device, allowing the elderly person to check the date and time of their next hospital appointment.

[0072] Example 2: Support for disaster-stricken areas

[0073] The user inputs into the device, "I would like to request the delivery of supplies to the disaster area." The device sends this information to the server, which then uses generative AI to generate the optimal route and delivery method. The drone then flies autonomously based on this information and delivers the supplies to the specified destination.

[0074] Prompt Sentence Examples

[0075] 1. "What's the best route to my next destination?"

[0076] 2. "I would like to request the transportation of supplies to disaster areas."

[0077] 3. "Check my next doctor's appointment."

[0078] By inputting these prompts into a generative AI model, users can obtain the information they need.

[0079] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0080] Step 1: User input

[0081] A user inputs instructions into the system through a terminal. For example, the user might input, "Tell me the best route to my next destination." The input instructions are sent to the server through the terminal interface. The input data is in text format and specifically describes the user's request.

[0082] Step 2: Send instructions to the server

[0083] The terminal sends instructions received from the user to the server. Specifically, it sends the user's prompt to the server using an HTTP request. The input is the user's instruction text, and the output is the request sent to the server.

[0084] Step 3: Information generation using generative AI models

[0085] The server generates the necessary information using a generative AI model (e.g., GPT-4) based on the received instructions. Specifically, it runs a Python script and inputs the user's prompt into the generative AI model. The input is the user's instruction text, and the output is the generated information text.

[0086] Step 4: Analyze the generated information

[0087] The server analyzes the information returned by the generative AI model. For example, it analyzes the generated route information and identifies the optimal route. The input is the generated information text, and the output is the analysis result.

[0088] Step 5: Submitting the analysis results

[0089] The server sends the analysis results to the terminal. Specifically, it returns the analysis results to the terminal using an HTTP response. The input is the analysis results, and the output is the response sent to the terminal.

[0090] Step 6: Receiving the analysis results

[0091] The terminal receives the analysis result returned from the server. The input is the response from the server, and the output is the reception of the analysis result.

[0092] Step 7: Viewing the analysis results

[0093] The terminal displays the received analysis results to the user. Specifically, optimal route information is displayed on the terminal screen. The input is the analysis results, and the output is the information displayed to the user.

[0094] Step 8: Decide on next steps

[0095] The user checks the analysis results displayed on the device and decides on the next course of action, for example, heading to the destination by following the displayed route. The input is the analysis results, and the output is the user's decision on the course of action.

[0096] (Application example 1)

[0097] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0098] Conventional food delivery systems have had issues with delivery efficiency and user convenience. In particular, delivery route optimization and learning of user behavior patterns have not been adequately performed, resulting in delivery delays and low user satisfaction. Support for the elderly and people with physical limitations has also been insufficient, and providing prompt support to remote areas and disaster-stricken areas has also been an issue.

[0099] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0100] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a means for analyzing the user's order details and the delivery destination situation and calculating the optimal route, and a means for learning the user's behavioral patterns and preferences to enable smoother future orders. This improves delivery efficiency, enhances user convenience, and enables support for the elderly and people with physical limitations, as well as rapid support in remote areas and disaster-stricken areas.

[0101] "Drone technology" refers to the use of unmanned aerial vehicles to fly autonomously and perform functions such as transporting goods and conducting surveillance.

[0102] "Transportation" refers to a method or device for transporting objects or people from one point to another.

[0103] "Generative AI" is an artificial intelligence technology that generates and analyzes necessary information based on user instructions and the situation.

[0104] "Information processing means" refers to methods and devices for collecting, analyzing, and generating data.

[0105] "Learning methods through the evolution of generative AI" refers to artificial intelligence technology that learns optimal behavior based on the user's behavior patterns and circumstances, and then acts based on that.

[0106] "Means for analyzing the contents of a user's order and the status of the delivery destination and calculating the optimal route" refers to a method or device for analyzing the contents of a user's order and information on the delivery destination and calculating the most efficient delivery route.

[0107] "Means for learning user behavioral patterns and preferences to enable smoother ordering in the future" refers to a method or device for learning a user's past behavior and preferences and optimizing the ordering process in the future.

[0108] "Daily life support for the elderly and people with physical limitations" refers to providing the support necessary for the elderly and people with physical limitations to carry out their daily lives.

[0109] "Effective support to remote and disaster-stricken areas" refers to providing prompt and effective support to remote and disaster-stricken areas.

[0110] "Environmental protection and natural disaster prevention" refers to activities and measures to protect the environment and prevent the occurrence of natural disasters.

[0111] The system for implementing this invention combines drone technology, generative AI, and learning means based on the evolution of generative AI. Specific embodiments of this system are described below.

[0112] System Configuration

[0113] The system consists of the following main components:

[0114] 1. Drones: Equipped with GPS and sensors, they fly autonomously.

[0115] 2. Server: Executes the generative AI and the learning method that evolves the generative AI.

[0116] 3. User device: The user places the order on a device such as a smartphone.

[0117] Program processing

[0118] Hardware

[0119] Drone: GPS, sensors

[0120] Server: High-performance processor, memory

[0121] User device: Smartphone

[0122] software

[0123] Generative AI: Python, TENSORFLOW (registered trademark)

[0124] Learning AI: Python, Scikit-learn

[0125] Data processing and calculation

[0126] 1. Order analysis: When a user places an order on their smartphone, the server uses generative AI to analyze the order and calculate the optimal route.

[0127] 2. Behavioral learning: The server uses generative AI to learn the user's behavioral patterns and preferences, making future orders smoother.

[0128] 3. Autonomous flight: The drones use GPS and sensors to fly autonomously and deliver meals to designated locations.

[0129] Specific examples

[0130] Consider a scenario where a user orders a pizza via smartphone and has it delivered to their home. Once the user confirms the order, the server uses generative AI to analyze the order and calculate the optimal delivery route. Next, the server uses generative AI to learn the user's past order history and behavioral patterns, allowing for smoother future orders. The drone then autonomously flies from the warehouse to the user's home to deliver the pizza.

[0131] Prompt Sentence Examples

[0132] "Build a system where a user can order a pizza on their smartphone and a drone will deliver it to their home. Use generative AI to analyze the order and calculate the optimal route. Also, use the advanced learning methods of generative AI to learn the user's behavioral patterns and preferences, making future orders smoother."

[0133] In this way, the system can solve problems such as providing daily support to the elderly and people with physical limitations, providing effective support to remote and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[0134] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0135] Step 1:

[0136] The user places an order on their smartphone. The user opens the smartphone application, selects the meal they want to order, and enters the delivery address. The input data includes the order details and delivery address information.

[0137] Step 2:

[0138] The server receives the order details and analyzes them using generative AI. The server receives the order details and delivery destination information from the user as input, and calculates the optimal delivery route using a generative AI model. The output is the optimal delivery route information.

[0139] Step 3:

[0140] The server uses generative AI to learn the user's behavioral patterns and preferences. The server receives past order history and user behavior data as input, and uses a generative AI model to learn the user's behavioral patterns and preferences. The output is a learned user profile.

[0141] Step 4:

[0142] The server sends the optimal delivery route to the drone. The server then sends the calculated optimal delivery route information to the drone. The drone then begins autonomous flight based on the received delivery route information. The input is the optimal delivery route information, and the output is flight instructions for the drone.

[0143] Step 5:

[0144] The drone flies autonomously and delivers meals to the designated destination. The drone flies autonomously using GPS and sensors to head to the designated destination. The input is the drone's flight instructions, and the output is a delivery completion notification.

[0145] Step 6:

[0146] The server notifies the user that the delivery is complete. When the drone completes the delivery, the server sends a delivery completion notification to the user's smartphone. The input is the delivery completion notification, and the output is a notification to the user.

[0147] In this way, the system can efficiently carry out the entire process from the user's order to the completion of delivery.

[0148] Example 2

[0149] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0150] Issues such as providing support for the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters have not been adequately resolved with existing technologies. In particular, there is a need for systems that can effectively support elderly people by integrating multiple technologies, such as understanding their health status, learning their behavioral patterns, and transporting supplies using drones.

[0151] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0152] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, a means for collecting health data and behavioral data, a means for calculating drone flight routes and transporting supplies, a means for analyzing health conditions and lifestyle habits using a generative AI model and generating information, and a means for learning behavioral patterns using a machine learning algorithm and providing optimal support. This improves the quality of daily life for the elderly and people with physical limitations and enables rapid and effective support in remote areas and disaster areas.

[0153] "Drone technology" refers to the use of unmanned aerial vehicles to transport goods and assist with transportation.

[0154] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate information.

[0155] "The evolution of generative AI" is the process by which artificial intelligence improves its performance through learning.

[0156] "Health data" refers to data that indicates an individual's health condition, such as heart rate, blood pressure, and number of steps taken.

[0157] "Behavioral data" refers to data that indicates an individual's daily behavior, such as meal times and walking times.

[0158] A "flight route" is the route a drone will take to reach its destination.

[0159] A "generative AI model" is a specific implementation of generative AI, and includes algorithms and programs for data analysis and information generation.

[0160] A "machine learning algorithm" is a computational method for learning patterns based on data and making predictions and classifications.

[0161] "Optimal support" refers to support and services that are provided in a manner that best suits the needs of each individual user.

[0162] This invention is a system for solving problems such as providing daily support for the elderly and physically limited people, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters. This system includes drone technology, generative AI, the evolution of generative AI, collection of health and behavioral data, calculation of drone flight routes, data analysis using generative AI models, and learning behavioral patterns using machine learning algorithms to provide optimal support.

[0163] 1. Program Generation

[0164] The server generates the system's program, which includes drone control, generative AI data processing, and learning of elderly people's behavioral patterns.

[0165] 2. Program processing explanation

[0166] The server performs the following processing using the generated program.

[0167] Drone Control

[0168] The server uses drone control software (e.g., DJI SDK) to assist elderly people with their mobility and deliver necessary supplies. Specifically, the server analyzes GPS data and calculates the drone's flight route. The drone then travels along that route and delivers the supplies to the elderly.

[0169] Data processing using generative AI

[0170] The server uses a generative AI model (e.g., OpenAI® GPT-4) to understand the health status and lifestyle habits of the elderly. Specifically, the server collects the elderly's health data (e.g., heart rate, blood pressure) and inputs it into the generative AI model. The generative AI model analyzes this data and generates necessary information (e.g., health advice).

[0171] Learning behavioral patterns

[0172] The server uses a machine learning algorithm (e.g., TensorFlow) to learn the behavioral patterns of the elderly. Specifically, the server collects data on the elderly's daily behavior (e.g., meal times, walking time) and inputs it into a machine learning model. The model uses this data to learn the elderly's behavioral patterns and provide optimal support.

[0173] 3. Examples and prompts

[0174] As a concrete example, the following scenario can be considered.

[0175] Example 1: Drone delivery of goods

[0176] Users (elderly people) use a smartphone app to order necessary supplies (e.g., medicine, food). The server receives the order information and uses drone control software to deliver the supplies to the elderly person.

[0177] Example 2: Understanding your health status

[0178] Users (elderly people) wear a wearable device to collect health data. The server inputs the collected data into a generative AI model and generates health advice.

[0179] Prompt Sentence Examples

[0180] "How long does a 70-year-old man take for a walk each day?"

[0181] What should elderly people do if they have high blood pressure?

[0182] Combining these measures can improve the quality of everyday life for older people.

[0183] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0184] Step 1:

[0185] Data collection

[0186] Users wear a wearable device to collect health data (e.g., heart rate, blood pressure, number of steps). The device (wearable device) measures this data in real time and transmits it to a server via Bluetooth or Wi-Fi.

[0187] Input: User health data

[0188] Data processing: The terminal formats the measurement data and converts it into a form that can be transmitted.

[0189] Output: Health data sent to the server

[0190] Step 2:

[0191] Drone control and cargo transportation

[0192] Users use a smartphone app to order necessary supplies (e.g., medicine, food). The server receives the order information and uses drone control software to deliver the supplies to the elderly person. The server analyzes GPS data and calculates the drone's flight route. The drone then travels along that route and delivers the supplies to the user.

[0193] Input: User order information, GPS data

[0194] Data processing: The server analyzes the order information and calculates the drone's flight route.

[0195] Output: Drone flight route, cargo transportation

[0196] Step 3:

[0197] Data analysis and information generation using generative AI

[0198] The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze the user's health status and lifestyle habits and generate the necessary information. The server inputs the collected health data into the generative AI model, which then analyzes this data and generates health advice and lifestyle improvement suggestions.

[0199] Input: Health data

[0200] Data processing: The generative AI model analyzes health data and generates the necessary information.

[0201] Output: Health advice, lifestyle improvement suggestions

[0202] Step 4:

[0203] Learning behavioral patterns and providing optimal support

[0204] The server uses a machine learning algorithm (e.g., TensorFlow) to learn the user's behavioral patterns and provide optimal support. The server collects the user's daily behavioral data (e.g., meal times, walking time) and inputs it into a machine learning model. The model uses this data to learn the user's behavioral patterns and provide optimal support.

[0205] Input: Daily behavior data

[0206] Data processing: Machine learning models analyze behavioral data and learn behavioral patterns.

[0207] Output: Optimal assistance (e.g., reminders, health advice)

[0208] Through these steps, the quality of daily life of older people can be improved.

[0209] (Application example 2)

[0210] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0211] In order to support the daily lives of the elderly and people with physical limitations, there is a lack of means to monitor their health status, provide appropriate meal plans, and efficiently deliver those meals. Similar support is also needed in remote areas and disaster areas, but the current system does not adequately address these issues.

[0212] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, a means for monitoring health data, a means for generating meal plans, a means for setting a delivery schedule by drone, and a means for optimizing the delivery schedule based on behavioral patterns. This enables efficient and effective support for the daily lives of elderly people and people with physical limitations, and also enables similar support in remote areas and disaster areas.

[0213] "Drone technology" is a technology that uses unmanned aerial vehicles to move objects, monitor them, collect data, and more.

[0214] "Generative AI" is a type of artificial intelligence, a technology that generates new information and content based on data.

[0215] A "learning method" is the method or process by which generative AI analyzes data and learns patterns and trends.

[0216] "Health data" refers to various physiological data that indicate an individual's health status, such as blood pressure, blood sugar level, and heart rate.

[0217] "Monitoring" is the act of continuously observing and recording specific data or conditions.

[0218] A "meal plan" is a suggested meal plan and schedule based on an individual's health status and preferences.

[0219] A "delivery schedule" is a plan or timetable for delivering goods or services at a specific time and place.

[0220] "Behavioral patterns" are data or information that show tendencies in an individual's behavior and habits.

[0221] "Optimization" is the process of deriving the most effective methods and results for specific purposes and conditions.

[0222] A system for carrying out this invention is configured as follows: A server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of the generative AI, a means for monitoring health data, a means for generating meal plans, a means for setting a delivery schedule by drone, and a means for optimizing the delivery schedule based on behavioral patterns.

[0223] Hardware and Software Configuration

[0224] Hardware:

[0225] Smartphone: A device that allows a user to operate applications.

[0226] Drone: An unmanned aerial vehicle used to move or deliver goods.

[0227] Sensors for health data monitoring: Devices for obtaining health data such as blood pressure and blood sugar levels.

[0228] software:

[0229] Python: A programming language used to implement programs.

[0230] Sensor API for Health Data Monitoring: A software interface for acquiring health data.

[0231] Drone control API: A software interface for controlling drone flight and setting delivery schedules.

[0232] Data processing and calculation

[0233] 1. Health Data Monitoring:

[0234] The server uses a sensor API to obtain health data (blood pressure, blood sugar levels, etc.) of the elderly.

[0235] The acquired data is stored on the server and used for analysis by generative AI.

[0236] 2. Generate meal plans:

[0237] The server uses generative AI to generate an optimal meal plan based on the acquired health data and user profile.

[0238] The generated meal plan is sent to the user's smartphone.

[0239] 3. Schedule your delivery:

[0240] The server uses a drone control API to schedule delivery of the generated meal plan at the specified time.

[0241] Delivery schedules are kept on a server and instructions are sent to the drones.

[0242] 4. Schedule optimization:

[0243] The server learns the behavioral patterns of the elderly and optimizes delivery schedules.

[0244] The optimized schedule is maintained on a server and instructions are sent to the drones.

[0245] Specific examples

[0246] If an elderly user likes Japanese food and has high blood pressure, the server operates as follows.

[0247] 1. A health data monitoring sensor measures the user's blood pressure and transmits the data to a server.

[0248] 2. The server uses generative AI to suggest low-salt Japanese meals based on the user's health data and dietary preferences.

[0249] 3. The server uses the drone control API to schedule delivery of the suggested meal to the user's home in one hour.

[0250] 4. The server learns the user's behavioral patterns and optimizes the delivery schedule.

[0251] Prompt Sentence Examples

[0252] User profile:

[0253] Name: Elderly User

[0254] Age: 75

[0255] Food preferences: Japanese, Western, Chinese

[0256] Health Data:

[0257] Blood pressure: 135

[0258] Blood sugar level: 90

[0259] Generated meal plan:

[0260] Low-salt Japanese food

[0261] Delivery Schedule:

[0262] Delivery in 1 hour

[0263] In this way, a system will be realized that efficiently and effectively supports the daily lives of elderly people and people with physical limitations.

[0264] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0265] Step 1:

[0266] The server uses a sensor API for health data monitoring to obtain the user's health data (blood pressure, blood glucose level, etc.). The input is physiological data from the sensor, and the output is health data stored in the server. Specifically, the sensor is worn on the user's body, periodically collects data, and sends it to the server.

[0267] Step 2:

[0268] The server analyzes the acquired health data and uses generative AI to evaluate the user's health condition. The input is the health data acquired in step 1, and the output is the health condition evaluation result. Specifically, the server runs a data analysis algorithm to detect abnormalities in blood pressure and blood sugar levels.

[0269] Step 3:

[0270] The server uses generative AI to generate an optimal meal plan based on the user profile (food preferences, allergy information, etc.) and the health assessment results. The input is the user profile and the health assessment results, and the output is the generated meal plan. Specifically, the server inputs a prompt statement into the generative AI model and generates the meal plan.

[0271] Step 4:

[0272] The server notifies the user of the generated meal plan on their smartphone. The input is the generated meal plan, and the output is a notification displayed on the user's smartphone. Specifically, the server calls the notification API and sends the contents of the meal plan to the user.

[0273] Step 5:

[0274] The server uses the drone control API to schedule delivery of the generated meal plan at the specified time. The input is the generated meal plan and delivery time, and the output is the drone delivery schedule. Specifically, the server calls the drone control API to set the delivery schedule.

[0275] Step 6:

[0276] The server learns the user's behavioral patterns and optimizes the delivery schedule. The input is the user's behavioral data and delivery schedule, and the output is the optimized delivery schedule. Specifically, the server executes a behavioral pattern analysis algorithm and calculates the optimal delivery time.

[0277] Step 7:

[0278] The server sends the optimized delivery schedule to the drone and executes the meal delivery. The input is the optimized delivery schedule, and the output is the meal delivered to the user's home. Specifically, the server sends instructions to the drone, and the drone delivers the meal to the user's home at the specified time.

[0279] Example 3

[0280] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0281] To provide prompt and effective support in remote areas and disaster-stricken areas, it is necessary to accurately grasp the local situation and quickly determine the appropriate support method. However, with conventional methods, it takes time to collect and analyze information, which often results in delays in support. Similar issues exist in providing support for the daily lives of the elderly and people with physical limitations, as well as in environmental protection and natural disaster prevention.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0283] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, an information gathering means using drones, a means for inputting the gathered data into a generative AI model, a data analysis means using the generative AI model, a means for proposing a support method based on the analysis results, and a means for transporting and delivering support supplies, thereby enabling fast and effective support in remote areas and disaster areas.

[0284] "Drone technology" is a technology that uses unmanned aerial vehicles to travel to specific locations such as remote areas or disaster areas, and transport supplies and collect information.

[0285] "Transportation means" means a means for using a drone to travel to a designated location.

[0286] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[0287] "Information processing means" refers to means for analyzing collected data and generating necessary information.

[0288] "Learning methods through the evolution of generative AI" refers to a method by which generative AI learns based on past data and experience and proposes more effective support methods.

[0289] "Information collection means" refers to the means of collecting local images and environmental data using drones.

[0290] "Means for inputting collected data into a generative AI model" refers to the means for inputting data collected by a drone into a generative AI model and performing analysis.

[0291] "Means for data analysis using generative AI models" refers to means for analyzing collected data using generative AI models to understand the local situation.

[0292] "Means for proposing support methods based on analysis results" refers to means for proposing optimal support methods based on the analysis results of a generative AI model.

[0293] "Means for transporting and implementing relief supplies" refers to the means for using drones to transport the proposed relief supplies and implement relief on site.

[0294] MODE FOR CARRYING OUT THE INVENTION

[0295] The present invention is a system for realizing rapid and effective support for remote areas and disaster areas. A specific embodiment of this system will be described below.

[0296] 1. Program Generation

[0297] The server generates programs for providing support to remote areas and disaster-stricken areas, including drone operation, information gathering, data analysis using generative AI, and support decision-making.

[0298] 2. Program processing explanation

[0299] The server dispatches drones to remote areas or disaster zones to deliver supplies and gather local information. The drones are equipped with cameras and sensors to collect video and environmental data in real time. The collected data is then sent to the server.

[0300] The server inputs the received data into a generative AI model, which determines the optimal method of assistance based on past disaster response data and assistance track record. This generative AI model uses, for example, OpenAI's GPT-4 or Google's BERT.

[0301] The generative AI model analyzes the collected information and suggests necessary relief supplies and methods. For example, if a shortage of food or medicine is identified, it will instruct the delivery of these supplies as a priority. It will also suggest evacuation routes and determine the need for medical support depending on the situation in the affected area.

[0302] 3. Examples of concrete examples and prompts

[0303] As a concrete example, consider the case where a flood occurs in a certain area. A drone takes aerial photographs of the flood damage and collects information on the location of victims and the status of evacuation routes. The collected data is sent to a server and analyzed by a generative AI model.

[0304] Example prompts for generative AI models:

[0305] "Analyze the damage situation in flood-affected areas and propose the necessary relief supplies and methods of support. The collected data will include the location information of victims, the status of evacuation routes, and the shortage of supplies."

[0306] Based on this prompt, the generative AI model proposes the optimal method of assistance, and the server sends the instructions to the drone, which then transports the proposed relief supplies and delivers them to the disaster area, thereby achieving fast and effective assistance.

[0307] This system combines drone technology and generative AI technology to efficiently provide support to remote areas and disaster areas. Furthermore, by using a learning method based on the evolution of the generative AI model, it is possible to learn the optimal support method from past experience and provide more effective support. The flow of the specific processing in Example 3 is explained using FIG. 15.

[0308] Program processing flow

[0309] Step 1: Dispatch drones and gather information

[0310] The server issues instructions to dispatch drones to remote locations or disaster areas. The drones are equipped with cameras and sensors and collect local video and environmental data. Specifically, the drones use GPS to fly along designated routes and capture video of the disaster area with their cameras. They also use temperature and humidity sensors to collect environmental data. The input is the dispatch instruction from the server, and the output is the collected video data and environmental data.

[0311] Step 2: Sending and Receiving Data

[0312] The drone transmits the collected data to a server in real time. The server stores the received data and prepares it for analysis. Specifically, the video data captured by the drone and the environmental data acquired from the sensors are transmitted to the server via wireless communication. The input is the data collected from the drone, and the output is the data stored on the server.

[0313] Step 3: Data analysis using generative AI models

[0314] The server inputs the received data into a generative AI model. The generative AI model analyzes the optimal method of support based on past disaster response data and support track record. Specifically, the generative AI model identifies the extent of flood damage and determines evacuation routes for victims and necessary support supplies. The input is the collected data stored on the server, and the output is the analysis results.

[0315] Step 4: Propose support methods

[0316] The generative AI model proposes assistance methods based on the analysis results. The server checks the proposed assistance methods and generates specific instructions. Specifically, if the generative AI model detects a shortage of food or medicine, the server issues instructions to prioritize the transportation of these supplies. The input is the analysis results of the generative AI model, and the output is specific assistance instructions.

[0317] Step 5: Delivery and implementation of relief supplies

[0318] The server issues instructions to the drone to transport relief supplies. The drone loads the specified supplies and flies toward the disaster area. After arriving at the disaster area, the drone drops off the supplies and provides the necessary support. Specifically, the drone delivers medicines to the disaster area and hands them over to local medical staff. The input is support instructions from the server, and the output is the delivery of supplies to the disaster area and the implementation of support.

[0319] This series of processes enables rapid and effective support to remote areas and disaster-stricken areas.

[0320] (Application example 3)

[0321] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0322] In order to provide prompt and effective support to remote areas and disaster-stricken areas, it is necessary to accurately grasp the local situation and quickly determine the optimal support method. However, with conventional methods, it takes time to gather information and determine the support method, which often results in delays in support. Optimizing disaster support has also been an issue for logistics centers.

[0323] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0324] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a remote control means using a smartphone, a data analysis means using a cloud server, and a means for optimizing disaster relief at a logistics center, thereby enabling fast and effective support to remote areas and disaster-stricken areas.

[0325] "Drone technology" refers to the use of unmanned aerial vehicles to transport supplies and gather information.

[0326] "Generative AI" is an artificial intelligence technology that generates and analyzes information based on collected data.

[0327] "Learning through the evolution of generative AI" is a learning method for artificial intelligence that evolves by learning the optimal support methods based on past data and experience.

[0328] "Remote control using a smartphone" is a technology that allows you to control a drone in a remote location using a smartphone.

[0329] "Data analysis using cloud servers" is a technology that analyzes data collected using servers on the cloud.

[0330] "Optimization of disaster relief at logistics centers" refers to technology that determines the optimal method of transporting relief supplies and providing support in the event of a disaster at a logistics center.

[0331] The system for implementing the present invention uses the following hardware and software.

[0332] Hardware used

[0333] Smartphone (iOS or ANDROID (registered trademark))

[0334] Drone (with camera and GPS)

[0335] Server (cloud-based, hosts generative AI models)

[0336] Software used

[0337] Smartphone app (Swift for iOS, Kotlin for Android)

[0338] Drone control software (DJI SDK, etc.)

[0339] Generative AI models (e.g., GPT-4)

[0340] Cloud services (AWS (registered trademark), Google Cloud)

[0341] System Operation

[0342] Remote control of drones

[0343] Using a smartphone app, users can remotely control the drone and fly it to the disaster area. The drone captures real-time video footage with its camera and transmits it to the smartphone.

[0344] Information collection and transmission

[0345] The video and sensor data collected by the drone is sent from a smartphone to a cloud server, where a generative AI model analyzes the data and determines the necessary assistance.

[0346] Proposing optimal support methods

[0347] The generative AI model learns the optimal support method based on past data and notifies the smartphone app of the optimal support method.

[0348] Specific examples

[0349] For example, if a flood occurs in a certain area, a user can use a smartphone app to operate a drone to collect video footage of the area. If the generative AI model analyzes the video and determines that there is a shortage of drinking water, a notification will appear on the smartphone app stating, "Please make transporting drinking water your top priority."

[0350] Prompt Sentence Examples

[0351] Analyze footage of flooded areas and suggest the most needed relief supplies.

[0352] In this way, users can transport relief supplies quickly and efficiently.

[0353] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0354] Step 1:

[0355] The user launches a smartphone app and remotely controls the drone.

[0356] Input: User operation instructions (touch operation, GPS coordinates)

[0357] Output: Drone flight start, real-time video acquisition

[0358] How it works: The user touches the smartphone screen to set the drone's flight route, and the drone begins flying along the specified route. The drone's camera captures real-time footage and sends it to the smartphone.

[0359] Step 2:

[0360] The video and sensor data collected by the drone is sent from a smartphone to a cloud server.

[0361] Input: Real-time video from drone, sensor data

[0362] Output: Send data to cloud server

[0363] How it works: The smartphone uploads the video and sensor data received from the drone to a cloud server, which then receives and stores the data.

[0364] Step 3:

[0365] A generative AI model runs on a cloud server and analyzes the data to determine the necessary assistance.

[0366] Input: Video data and sensor data stored on a cloud server

[0367] Output: Analysis results (type and amount of relief supplies needed)

[0368] How it works: A generative AI model on a cloud server analyzes the received video and sensor data. For example, it analyzes flood footage and determines that there is a shortage of drinking water.

[0369] Step 4:

[0370] The generative AI model learns the optimal support method based on past data and notifies the smartphone app of the optimal support method.

[0371] Input: Analysis results, past data

[0372] Output: Notification of the best way to help

[0373] How it works: The generative AI model references past assistance data and learns the best way to provide assistance in the current situation. For example, based on past flood assistance data, it determines that drinking water and medical kits are most needed and notifies the smartphone app.

[0374] Step 5:

[0375] Users can check notifications on their smartphone app and prepare the most appropriate relief supplies.

[0376] Input: Notification from smartphone app

[0377] Output: Preparing relief supplies

[0378] Specific operation: The user checks the notification on the smartphone app and prepares the requested relief supplies (e.g., drinking water, medical kits). Once preparations are complete, the user operates the drone again to deliver the relief supplies to the disaster area.

[0379] In this way, users can transport relief supplies quickly and efficiently.

[0380] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0381] "Example 1"

[0382] One embodiment of the present invention is a system that combines drone technology, generative AI, and an emotion engine. This system recognizes a user's emotions and controls the drone's behavior according to that user's emotional state. Specifically, when the user feels joy, the drone exhibits active movements, and when the user feels sad, the drone exhibits quiet movements. The generative AI also adjusts information generation and analysis according to the user's emotional state. For example, when the user feels stressed, the generative AI generates information related to relaxation, and when the user feels joy, the generative AI generates information related to enjoyable activities.

[0383] "Example 2"

[0384] Another embodiment of the present invention is a system in which an emotion engine adjusts the learning methods of a generative AI according to the user's emotional state. Specifically, when a user feels angry, the generative AI learns the cause and, if a similar situation recurs, suggests measures to alleviate the user's anger. Similarly, when a user feels happy, the generative AI learns the cause and suggests ways to recreate a similar situation.

[0385] "Example 3"

[0386] In a further embodiment of the present invention, an emotion engine adjusts the flight control of a drone according to the user's emotional state. Specifically, when the user feels surprised, the drone performs an action such as rapidly increasing altitude to attract the user's attention. When the user feels safe, the drone performs a slow movement so as not to disturb the user's sense of safety.

[0387] The processing flow of each embodiment will be described below.

[0388] "Example 1"

[0389] Step 1: The emotion engine recognizes the user's emotional state.

[0390] Step 2: The emotion engine controls the drone's behavior according to the recognized emotional state.

[0391] Step 3: At the same time, the generative AI adjusts information generation and analysis according to the user's emotional state.

[0392] "Example 2"

[0393] Step 1: The emotion engine recognizes the user's emotional state.

[0394] Step 2: Adjust the learning methods of the generative AI according to the emotional state recognized by the emotion engine.

[0395] Step 3: Based on the results of the generative AI's learning, measures to improve the user's emotional state are proposed.

[0396] "Example 3"

[0397] Step 1: The emotion engine recognizes the user's emotional state.

[0398] Step 2: Adjust the drone's flight control according to the emotional state recognized by the emotion engine.

[0399] Step 3: The drone operates according to the adjusted flight control.

[0400] Example 1

[0401] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0402] Efficient and flexible means of transportation and information processing are necessary to solve issues such as providing daily support for the elderly and physically limited people, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters. However, conventional technologies have had difficulty in comprehensively solving these issues. Furthermore, there have been no systems that can control movement or provide information according to the user's emotional state.

[0403] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0404] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative artificial intelligence, a learning means based on the evolution of generative artificial intelligence, and a drone control means using an emotion recognition engine. This makes it possible to efficiently and flexibly solve issues such as supporting the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters. It also makes it possible to realize operation control and information provision according to the user's emotional state.

[0405] "Drone technology" refers to the use of unmanned aerial vehicles to move around or transport goods autonomously or remotely.

[0406] "Generative AI" is an AI technology that generates and analyzes necessary information according to user instructions and the situation.

[0407] "The evolution of generative artificial intelligence" refers to the evolution of artificial intelligence technology that learns optimal behavior based on the user's behavioral patterns and circumstances, and optimizes behavior based on the results of that learning.

[0408] An "emotion recognition engine" is a technology that recognizes the user's emotions and controls the system's operation according to that emotional state.

[0409] "Transportation" refers to the use of drone technology to travel to a destination or transport goods.

[0410] "Information processing means" refers to means for generating and analyzing information using generative artificial intelligence.

[0411] The "learning means" is a means for the generative artificial intelligence to evolve and learn optimal behavior according to the user's behavior patterns and situations.

[0412] The "drone control means" is a means for controlling the operation of the drone according to the user's emotional state using an emotion recognition engine.

[0413] MODE FOR CARRYING OUT THE INVENTION

[0414] This invention is a system that combines drone technology, generative artificial intelligence, the evolution of generative artificial intelligence, and an emotion recognition engine, and solves issues such as supporting the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[0415] Transportation using drone technology

[0416] The server uses GPS and various sensors to control the drone's autonomous flight. Specifically, the drone determines its current location based on GPS data and calculates the optimal route to its destination. The sensors are used to detect obstacles and take evasive action. For example, when a drone is to deliver a package, the server sends the coordinates of the delivery destination to the drone as GPS data, and the drone then flies autonomously toward those coordinates.

[0417] Information processing methods using generative artificial intelligence

[0418] The device uses generative AI to generate and analyze the necessary information according to the user's instructions and the situation. Generative AI uses OpenAI's GPT-4, for example. When a user instructs the device to "tell me the weather tomorrow," the device inputs the prompt "tell me the weather tomorrow" into the generative AI and provides the generated weather information to the user.

[0419] Learning methods through the evolution of generative artificial intelligence

[0420] The server uses generative artificial intelligence to learn optimal behavior based on the user's behavioral patterns and circumstances. For example, if a user has a habit of jogging at 7am every morning, the server will learn that pattern and provide jogging information and weather forecasts every morning at 7am.

[0421] Drone control using emotion recognition engine

[0422] The device recognizes the user's emotions and controls the drone's behavior according to that emotional state. The emotion recognition engine uses, for example, Affectiva's emotion recognition software. When the user feels happy, the device instructs the drone to move more actively, and when the user feels sad, it instructs the drone to move more calmly. For example, when the user smiles, the device recognizes that emotion through the emotion recognition engine and sends the drone the command to "move more actively."

[0423] Specific examples

[0424] Example 1: Assisting the elderly with daily living

[0425] If the user is elderly and needs to take their medicine at a specific time each day, the server will use a drone to deliver the medicine at that time. The drone will autonomously fly to the elderly person's home using GPS and land safely using sensors.

[0426] Example 2: Support for disaster-stricken areas

[0427] When delivering supplies to a disaster-hit area, the server loads the supplies onto a drone and has it fly autonomously to the disaster area using GPS data. The sensors detect obstacles and take evasive action.

[0428] Prompt Sentence Examples

[0429] 1. "What's the weather going to be like tomorrow?"

[0430] 2. "Recommend some relaxing music."

[0431] 3. "What's the best route for my next jog?"

[0432] These prompt sentences are input into a generative AI model to provide the user with the information they need.

[0433] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0434] Transportation using drone technology

[0435] Step 1: Set your destination

[0436] The user specifies the drone's destination, for example, by entering "from home to the park" using a smartphone app.

[0437] Input: Destination coordinates

[0438] Output: Destination coordinate data

[0439] Step 2: Route calculation

[0440] The server calculates the optimal route based on the destination specified by the user, using GPS data and map information to generate a route that avoids obstacles and no-fly zones.

[0441] Input: Destination coordinate data, GPS data, map information

[0442] Output: Optimal route data

[0443] Step 3: Start autonomous flight

[0444] The server then sends the calculated route information to the drone, which then uses GPS and sensors to begin autonomous flight. For example, if the drone flies from your home to a park, it will use its sensors to avoid trees and buildings along the way.

[0445] Input: Optimal route data

[0446] Output: Drone begins flying

[0447] Step 4: Reaching the destination

[0448] The drone will follow the specified route to reach its destination. After it arrives, the server will send a notification to the user. For example, a notification will appear on the smartphone saying, "The drone has arrived at the park."

[0449] Input: Drone flight data

[0450] Output: Destination arrival notification

[0451] Information processing methods using generative artificial intelligence

[0452] Step 1: User input

[0453] Users input instructions to generative AI, for example, "Tell me what the weather will be like tomorrow" into a smartphone app.

[0454] Input: User instructions

[0455] Output: prompt statement

[0456] Step 2: Generate a prompt statement

[0457] The terminal generates a prompt sentence based on the user's instruction, for example, "Tell me what the weather will be like tomorrow."

[0458] Input: User instructions

[0459] Output: prompt statement

[0460] Step 3: Information generation and analysis

[0461] The device inputs a prompt sentence into a generative artificial intelligence model (e.g., GPT-4) to generate the necessary information. The generated information is analyzed and provided to the user. For example, the information generated is "Tomorrow will be sunny and the temperature will be 25 degrees."

[0462] Input: prompt statement

[0463] Output: The information generated

[0464] Step 4: Inform users

[0465] The device provides the generated information to the user. For example, a message such as "Tomorrow will be sunny and the temperature will be 25 degrees" will be displayed on the smartphone screen.

[0466] Input: Generated information

[0467] Output: Displaying information to the user

[0468] Learning methods through the evolution of generative artificial intelligence

[0469] Step 1: Collect user behavior patterns

[0470] The server collects the user's behavioral patterns, for example, data that the user goes jogging at 7:00 every morning.

[0471] Input: User behavior data

[0472] Output: Behavioral pattern data

[0473] Step 2: Analyze behavioral patterns

[0474] The server analyzes the collected data and learns the user's behavioral patterns, for example, that the user goes jogging at 7am every morning.

[0475] Input: Behavioral pattern data

[0476] Output: Training results

[0477] Step 3: Recommend the best course of action

[0478] The server then suggests optimal actions based on the learned behavioral patterns. For example, every morning at 7:00, it sends a notification saying, "It's time to go jogging. Today's weather is sunny."

[0479] Input: Training results

[0480] Output: Action suggestion notification

[0481] Step 4: Gather user feedback

[0482] The server collects feedback from users and further improves its learning. For example, if a user gives feedback such as "I'm not going jogging today," the server will use that information to adjust its next suggestion.

[0483] Input: User feedback

[0484] Output: Adjusted training results

[0485] Drone control using emotion recognition engine

[0486] Step 1: Recognizing user emotions

[0487] The terminal uses an emotion recognition engine to recognize the user's emotion. For example, if the user smiles, the emotion recognition engine recognizes "joy."

[0488] Input: User's facial expression data

[0489] Output: Emotion recognition result

[0490] Step 2: Drone movement control

[0491] The device controls the drone's behavior based on the recognized emotion. For example, if the user is feeling happy, the device will send a command to the drone to "move more actively."

[0492] Input: Emotion recognition results

[0493] Output: Drone control instructions

[0494] Step 3: Drone movement

[0495] The drone performs actions according to instructions from the terminal, such as performing acrobatic maneuvers in the air.

[0496] Input: Drone control instructions

[0497] Output: Drone movement

[0498] Step 4: User feedback

[0499] The device collects user reactions to the drone's actions to improve the accuracy of its emotion recognition engine. For example, if the user smiles more, the device will use that information to adjust the drone's next action.

[0500] Input: User response data

[0501] Output: Adjusted emotion recognition results

[0502] (Application example 1)

[0503] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0504] Conventional food delivery systems have difficulty providing services that respond to the user's emotional state, resulting in a lack of appropriate support, particularly for the elderly and those with physical limitations. Providing prompt and effective support to remote areas and disaster-stricken areas is also a challenge. Furthermore, responses to environmental protection and natural disaster prevention are insufficient. To solve these issues, a new system that takes the user's emotional state into account is needed.

[0505] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0506] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, a means for controlling the operation of the drone according to the emotional state of the user, and a means for adjusting information generation and analysis according to the emotional state of the user. This enables optimal food delivery according to the emotional state of the user, thereby realizing support for the daily lives of the elderly and people with physical limitations, effective support for remote areas and disaster-stricken areas, environmental protection, and natural disaster prevention.

[0507] "Drone technology" refers to the use of unmanned aerial vehicles to fly autonomously and perform functions such as transporting goods and conducting surveillance.

[0508] "Transportation" refers to a method or device for transporting objects or people from one place to another.

[0509] "Generative AI" is a system that uses artificial intelligence technology to generate new information and content from data.

[0510] "Information processing means" refers to methods and devices for collecting, analyzing, transforming, storing, and transmitting data.

[0511] "Means of learning through the evolution of generative AI" refers to methods and devices that allow generative AI to learn and evolve optimal behavior based on the user's behavioral patterns and circumstances.

[0512] "Means for controlling drone operation according to the user's emotional state" refers to a method or device for analyzing a user's emotions and adjusting the drone's operation based on the results.

[0513] "Means for adjusting information generation and analysis according to the user's emotional state" refers to a method or device for analyzing a user's emotions and adjusting the information generated by the generative AI and the analysis method based on the results.

[0514] "Assisted daily living for the elderly and physically limited" refers to methods and devices for providing the elderly and physically limited people with the assistance they need to carry out their daily lives.

[0515] "Effective support to remote and disaster-stricken areas" refers to methods and equipment for providing prompt and appropriate support to remote and disaster-stricken areas.

[0516] "Environmental protection and natural disaster prevention" refers to methods and devices for protecting the environment and preventing the occurrence of natural disasters.

[0517] As an embodiment of the present invention, an emotion-responsive food delivery drone system will be described as an example.

[0518] System Program

[0519] The system combines drone technology, generative AI, and an emotion engine, and specifically uses the following hardware and software:

[0520] Hardware

[0521] Drone: An unmanned aerial vehicle equipped with GPS and sensors and capable of autonomous flight.

[0522] Smartphone: Used as a user interface and equipped with a camera and microphone for emotion recognition.

[0523] Server: Data processing and generative AI execution environment.

[0524] software

[0525] GPS module: Software for obtaining drone location information.

[0526] Drone control software: Software for controlling the autonomous flight of drones.

[0527] Emotion recognition software: Software for analyzing user emotions.

[0528] Generative AI: Artificial intelligence that generates information based on the user's emotional state.

[0529] Processing flow

[0530] 1. Emotion recognition: When a user places an order through their smartphone, the smartphone's camera and microphone are used to analyze the user's emotions. Emotion recognition software processes this data and identifies the user's emotional state.

[0531] 2. Menu generation: Based on the analyzed emotions, the generative AI generates an appropriate menu. For example, if the user is feeling stressed, it will suggest a menu with a relaxation effect.

[0532] 3. Drone control: Using GPS information, the drone autonomously flies to its destination. The drone control software calculates the flight path and guides the drone to its destination.

[0533] 4. Food Delivery: The drone will arrive at the destination and deliver the food. The drone's behavior will also be adjusted depending on the user's emotional state. For example, if the user is happy, the drone will be more active.

[0534] Specific examples

[0535] If the user is stressed, the generative AI will suggest relaxing herbal tea or snacks, which the drone will deliver, and if the user is happy, the generative AI will suggest menu items related to enjoyable activities, which the drone will deliver.

[0536] Prompt Sentence Examples

[0537] If the user's emotional state is "stressed," generate a menu with a relaxation effect.

[0538] In this way, the emotion-responsive food delivery drone system can provide optimal service according to the user's emotions.

[0539] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0540] Step 1:

[0541] A user places a food delivery order via their smartphone. The smartphone's camera and microphone are used to collect the user's emotional data (such as facial expressions and tone of voice). The input is the user's emotional data, and the output is raw data sent to emotion recognition software.

[0542] Step 2:

[0543] The server uses emotion recognition software to analyze the user's emotional data. Specifically, it analyzes the collected facial expressions and tone of voice to identify the user's emotional state (e.g., joy, sadness, stress, etc.). The input is the raw data collected in step 1, and the output is the analyzed emotional state.

[0544] Step 3:

[0545] The server uses generative AI to generate an appropriate menu based on the analyzed emotional state. For example, if the user is feeling stressed, it will suggest a menu with a relaxation effect. The input is the emotional state identified in step 2, and the output is the generated menu.

[0546] Step 4:

[0547] The server sends the generated menu to the user's smartphone and asks for confirmation. The user checks the menu and confirms the order. The input is the generated menu, and the output is the user's confirmation result.

[0548] Step 5:

[0549] The server uses drone control software to set the destination (user's address) for the drone. Using GPS information, the drone autonomously flies to the destination. The input is the user's address information, and the output is the drone's flight path.

[0550] Step 6:

[0551] The drone arrives at the destination and delivers the meal. The drone's behavior is adjusted according to the user's emotional state. For example, if the user is happy, the drone will be more active. The input is the drone's flight path and the user's emotional state, and the output is the meal has been delivered.

[0552] Step 7:

[0553] The server sends a delivery completion notification to the user's smartphone. The user confirms the delivery and provides feedback if necessary. The input is delivery completion information, and the output is user feedback.

[0554] Example 2

[0555] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0556] There is a need to efficiently support the daily lives of the elderly and people with physical limitations. There are also challenges, such as providing prompt and effective support to remote areas and disaster-stricken areas, as well as environmental protection and natural disaster prevention. To solve these challenges, a system that combines the latest technologies is needed.

[0557] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0558] In this invention, the server includes a means for collecting elderly behavioral data, a means for analyzing and learning from the collected behavioral data, a means for providing mobility assistance and transporting goods by drone, a means for collecting and analyzing user emotion data, a means for adjusting the generative AI's learning means based on the analysis results, and a means for processing user requests. This not only enables efficient support for the daily lives of elderly people and those with physical limitations, but also enables rapid and effective support in remote areas and disaster-stricken areas. It also contributes to environmental protection and natural disaster prevention.

[0559] "Means for collecting elderly people's behavioral data" refers to devices or systems that use sensors or wearable devices to collect daily behavioral data such as walking distance, heart rate, and meal times of elderly people.

[0560] "Means for analyzing and learning from collected behavioral data" refers to software or algorithms that analyze collected behavioral data and learn behavioral patterns and health conditions.

[0561] "Means for providing mobility assistance and transporting supplies using drones" is a system that uses drones to assist the mobility of elderly people and transport necessary supplies.

[0562] The "means for collecting and analyzing user emotional data" refers to devices or software that collect and analyze the user's voice and facial expression data to understand their emotional state.

[0563] The "means for adjusting the learning means of generative AI based on the analysis results" is a system for adjusting the learning algorithms and parameters of generative AI based on the analysis results of emotional data.

[0564] The "means for processing requests from users" is a system for receiving assistance requests sent by users via dedicated terminals and providing appropriate responses.

[0565] The present invention is a system for efficiently supporting the daily lives of elderly people and people with physical limitations. Specific embodiments of this system will be described below.

[0566] Collecting behavioral data on elderly people

[0567] The server collects data on the elderly's behavior from dedicated sensors and wearable devices. Specifically, devices such as pedometers and heart rate monitors are used to obtain daily behavioral data such as walking distance, heart rate, and meal times. This data is sent to the server in real time and stored in a database. MySQL (registered trademark) or MongoDB is used as the database.

[0568] Behavioral data analysis and learning

[0569] The server analyzes the collected behavioral data using Python and TensorFlow. For example, it analyzes walking distance and heart rate fluctuations to detect abnormal patterns and health risks early on. The generative AI uses the analysis results to learn the behavioral patterns of the elderly and builds a model to provide optimal support. This makes it possible to constantly monitor the health of the elderly and provide the necessary support.

[0570] Drone-based mobility support and supply transport

[0571] The server issues instructions to the drone based on the analysis results of the generative AI. For example, it can instruct the drone to accompany an elderly person when they go out. The drone uses GPS and a camera to send the elderly person's location information to the server in real time, allowing the server to monitor the elderly person's safety. The drone can also transport necessary supplies. Specifically, the drone automatically starts up when the elderly person leaves their home and flies along the specified route.

[0572] Collecting and analyzing user emotion data

[0573] The device collects the user's voice and facial expression data. For example, it uses the smartphone's camera and microphone to understand the user's emotional state. The collected data is sent to a server in real time, where the server analyzes the emotional data using OpenCV and NLP technology. The analysis results are stored in a database and provided to the generative AI.

[0574] Adjusting learning methods

[0575] Generative AI adjusts its learning methods based on the results of analyzing emotional data. For example, it can identify the cause of a user's anger and suggest countermeasures the next time a similar situation occurs. This allows it to provide optimal support according to the user's emotional state.

[0576] Processing user requests

[0577] Users (elderly people) can request assistance from the server through a dedicated device. For example, they can request grocery delivery. The device is an Android-based tablet, and sends the request through a dedicated application. The server receives the request and sends instructions to drones or other devices to provide the necessary assistance. Specifically, when a user selects "grocery delivery" in the application, the server issues instructions to a drone to transport the specified items.

[0578] Examples of specific examples and prompts

[0579] As a concrete example, the following prompt sentence is input to the generative AI model:

[0580] Prompt: "Design a system to record how far an elderly person walks each day and notify them if their walking distance decreases."

[0581]

[0582] Prompt: "Design a system that, when a user feels anger, identifies the cause of the anger and suggests solutions the next time a similar situation occurs."

[0583] Based on these prompts, the generative AI model can suggest specific support methods and countermeasures.

[0584] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0585] Step 1:

[0586] The server collects behavioral data of elderly people from dedicated sensors and wearable devices. Specifically, it uses devices such as pedometers and heart rate monitors to obtain daily behavioral data such as walking distance, heart rate, and meal times. This data is sent to the server in real time and stored in a database. The input is data from the sensors and wearable devices, and the output is behavioral data stored in the database.

[0587] Step 2:

[0588] The server analyzes the collected behavioral data using Python and TensorFlow. For example, it analyzes walking distance and heart rate fluctuations to detect abnormal patterns and health risks early on. The input is the behavioral data stored in the database, and the output is the analysis results. Specifically, the server processes the data in batches every night and trains the generative AI.

[0589] Step 3:

[0590] The generative AI learns the behavioral patterns of the elderly based on the analysis results and builds a model to provide optimal support. The input is the analysis results from the server, and the output is the learned model. In concrete terms, the generative AI learns new data and updates the model parameters.

[0591] Step 4:

[0592] The server issues instructions to the drone based on the generative AI's analysis results. For example, it instructs the drone to accompany an elderly person when they go out. The input is instructions from the generative AI, and the output is instructions to the drone. Specifically, the drone automatically starts up when the elderly person leaves their home and flies along the specified route.

[0593] Step 5:

[0594] The drone uses GPS and a camera to send the location information of the elderly person to a server in real time, allowing the server to monitor the elderly person's safety. The input is data from the drone's sensors, and the output is the transmission of location information to the server. Specifically, the drone flies in accordance with the elderly person's movements, constantly updating its location information.

[0595] Step 6:

[0596] The device collects the user's voice and facial expression data. For example, it uses the smartphone's camera and microphone to understand the user's emotional state. The input is the user's voice and facial expression data, and the output is data transmission to the server. Specifically, the device automatically starts collecting data when the user launches the application.

[0597] Step 7:

[0598] The server analyzes the emotion data using OpenCV and NLP technology. The analysis results are stored in a database and provided to the generative AI. The input is emotion data from the device, and the output is the analysis results. Specifically, the server converts the voice data into text and inputs it into the emotion analysis model.

[0599] Step 8:

[0600] Generative AI adjusts its learning methods based on the results of analyzing emotional data. For example, it can identify the reason why a user felt anger and suggest countermeasures the next time a similar situation occurs. The input is the analysis results from the server, and the output is an adjusted learning model. Specifically, generative AI learns new data and updates the model parameters.

[0601] Step 9:

[0602] Users (elderly people) can request assistance from the server through a dedicated terminal. For example, they can request grocery delivery. The input is the request from the user, and the output is the request sent to the server. Specifically, when the user selects "grocery delivery" in the application, the server issues instructions to the drone to transport the specified items.

[0603] Step 10:

[0604] The server receives requests from users and sends instructions to drones and other devices to provide the necessary assistance. The input is the user's request, and the output is instructions to the drone or other device. Specifically, the server analyzes the request and determines the optimal method of assistance.

[0605] (Application example 2)

[0606] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0607] In order to support the daily lives of the elderly and people with physical limitations, it is necessary to address a wide range of needs, such as transportation, transporting supplies, monitoring health conditions, and analyzing emotional states. Furthermore, there are also challenges, such as providing effective support to remote areas and disaster-stricken areas, protecting the environment, and preventing natural disasters. Systems that can efficiently and effectively solve these challenges are needed.

[0608] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0609] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means by evolving the generative AI, and an emotional state analysis means using an emotion engine. This enables support for the daily lives of the elderly and people with physical limitations, effective support for remote areas and disaster-stricken areas, environmental protection, and natural disaster prevention.

[0610] "Drone technology" is the technology of using unmanned aerial vehicles to move objects, monitor them, and collect data.

[0611] A "transportation means" is a method or device for transporting objects or people from one place to another.

[0612] "Generative AI" is a system that uses artificial intelligence to generate and analyze data.

[0613] "Information processing means" refers to a method or device for analyzing collected data and generating necessary information.

[0614] "Learning means through the evolution of generative AI" refers to methods and devices that allow generative AI to learn based on past data and experience, and perform more advanced information generation and analysis.

[0615] An "emotion engine" is a system that analyzes the user's emotional state and responds appropriately based on the results.

[0616] The "emotional state analysis means" is a method or device for analyzing the user's emotions and understanding their state.

[0617] "Daily life support for the elderly and physically disabled" refers to methods and systems for providing the elderly and physically disabled with the support they need to carry out their daily lives.

[0618] "Effective support to remote and disaster-stricken areas" refers to methods and systems for providing prompt and appropriate support to remote areas and areas affected by disasters.

[0619] "Environmental protection and natural disaster prevention" refers to methods and systems for protecting the environment and preventing the occurrence of natural disasters.

[0620] A system for implementing this invention includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, and an emotional state analysis means using an emotion engine. This enables support for the daily lives of the elderly and people with physical limitations, effective support for remote areas and disaster-stricken areas, environmental protection, and natural disaster prevention.

[0621] System configuration

[0622] Hardware

[0623] Drone: An unmanned aerial vehicle used to transport supplies and provide mobility support.

[0624] Smartphones: Devices that collect health and emotional data from users.

[0625] Sensor: A device that measures health data such as blood sugar levels or blood pressure.

[0626] software

[0627] Generative AI: An artificial intelligence system that generates and analyzes data.

[0628] Emotion engine: A system that analyzes the user's emotional state and responds appropriately.

[0629] Health data management system: A system that manages users' health data and suggests appropriate meals and services.

[0630] Data processing and calculation

[0631] Health data collection

[0632] Devices (smartphones and sensors) collect health data such as the user's blood sugar level and blood pressure. This data is analyzed by generative AI and used to understand the user's health condition.

[0633] Collecting Emotional Data

[0634] The device (smartphone or smart glasses) collects emotion data from the user's facial expressions and voice. The emotion engine analyzes this data and understands the user's emotional state.

[0635] Data analysis

[0636] The server uses generative AI to analyze the collected health and emotional data, and then suggests appropriate meals and services based on the user's health and emotional state.

[0637] Specific examples

[0638] Health data example

[0639] An elderly person with a blood sugar level of 130 and a blood pressure of 135

[0640] Emotion data example

[0641] Users feel "happy"

[0642] Prompt Sentence Examples

[0643] Suggest appropriate meals and services based on the user's health data (blood sugar: 130, blood pressure: 135) and emotional data (happy).

[0644] In this way, the system can assist the elderly and people with physical limitations in their daily lives and provide effective assistance to remote areas and disaster zones.

[0645] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0646] Step 1:

[0647] The device (smartphone or sensor) collects the user's health data (blood glucose level, blood pressure, etc.). The input is data indicating the user's health condition, and the output is the collected health data. Specifically, the sensor measures the user's blood glucose level and blood pressure and sends the data to the smartphone.

[0648] Step 2:

[0649] The device (smartphone or smart glasses) collects emotion data from the user's facial expressions and voice. The input is the user's facial expression and voice data, and the output is analyzed emotion data. Specifically, the smartphone or smart glasses captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[0650] Step 3:

[0651] The server receives the collected health data and emotion data and analyzes the data using generative AI. The input is health data and emotion data, and the output is the analysis results. Specifically, the server analyzes the health data using a generative AI model to evaluate the user's health condition. It also analyzes the emotion data using an emotion engine to evaluate the user's emotional state.

[0652] Step 4:

[0653] Based on the analysis results, the server proposes appropriate meals and services according to the user's health and emotional state. The input is the analysis results, and the output is the proposed meals and services. Specifically, the server uses a generative AI model to generate a meal menu suited to the user's health condition, and an emotion engine to propose services according to the user's emotional state.

[0654] Step 5:

[0655] The device notifies the user of meals and services suggested by the server. The input is the suggestions from the server, and the output is the notification to the user. Specifically, the smartphone displays meal menus and service suggestions to the user, and arranges for meal delivery by drone if necessary.

[0656] Step 6:

[0657] The drone delivers meals to the user's home based on instructions from the server. The input is the delivery instruction, and the output is a delivery completion notification. Specifically, the drone loads the specified meal, flies to the user's home, and delivers it. After the delivery is complete, it sends a notification to the server.

[0658] In this way, the system can assist the elderly and people with physical limitations in their daily lives and provide effective assistance to remote areas and disaster zones.

[0659] Example 3

[0660] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0661] Providing rapid and effective support to remote areas and disaster areas has been difficult with conventional technology. Furthermore, conventional systems have not been able to adequately respond to situations that require flexible responses according to the user's emotional state. Furthermore, there is a need to provide consistent solutions to a wide range of issues, such as supporting the daily lives of the elderly and people with physical limitations, as well as environmental protection and natural disaster prevention.

[0662] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0663] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a means for analyzing user emotions using an emotion engine, and a means for controlling the drone's flight in accordance with the user's emotional state. This enables rapid and effective support in remote areas and disaster-stricken areas. Furthermore, flexible responses based on the user's emotional state enable consistent solutions to a wide range of issues, including support for the daily lives of the elderly and people with physical limitations, environmental protection, and natural disaster prevention.

[0664] "Drone technology" is the technology of using unmanned aerial vehicles to transport supplies and collect information.

[0665] "Generative AI" is an artificial intelligence technology that generates and analyzes information based on collected data.

[0666] "Learning through the evolution of generative AI" is a learning method for artificial intelligence that evolves by learning the optimal support methods based on past data and experience.

[0667] An "emotion engine" is a technology that analyzes a user's emotional state from facial expressions, voice, etc.

[0668] "User emotion analysis" is the process of analyzing a user's emotional state using an emotion engine.

[0669] "Drone flight control" is the technology that controls the flight path and movement of a drone.

[0670] "Transportation" refers to the use of drone technology to transport goods and information.

[0671] "Information processing means" refers to the means of analyzing collected data using generative AI and generating the necessary information.

[0672] "Problem-solving measures" are means for solving problems such as providing support for the daily lives of the elderly and people with physical limitations, providing support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[0673] MODE FOR CARRYING OUT THE INVENTION

[0674] This invention is a system that combines drone technology and generative AI to provide effective support to remote areas and disaster areas. Specific embodiments for implementing this system are described below.

[0675] Hardware and software used

[0676] Drones: Commercial drones (e.g., DJI Phantom 4)

[0677] Generative AI: General artificial intelligence models (e.g., OpenAI GPT-4)

[0678] Emotion engine: Emotion analysis software (e.g. Affectiva SDK)

[0679] Communications infrastructure: high-speed communications networks (e.g., 5G networks)

[0680] System Overview

[0681] This system uses drones to collect information from remote areas and disaster-stricken areas, analyzes that information using generative AI, and then uses an emotion engine to analyze the user's emotional state and reflect this in the drone's flight control, enabling flexible responses based on the user's emotions.

[0682] Program processing

[0683] 1. Information gathering

[0684] Drones fly to designated remote locations or disaster areas and collect local information using cameras and sensors.

[0685] The collected data is sent to a server in real time.

[0686] 2. Data Analysis

[0687] The server inputs the received data into a generative AI system and analyzes the local situation.

[0688] For example, image data from disaster areas can be analyzed to determine which areas need supplies.

[0689] 3. Deciding on support methods

[0690] Generative AI learns the optimal support method based on past data and determines the support needed.

[0691] For example, identify areas where food or medicine is in short supply and select appropriate supplies.

[0692] 4. Transporting supplies

[0693] The server issues instructions to the drone to transport the selected supplies.

[0694] The drones will transport supplies to designated locations and deliver them to the site.

[0695] 5. Flight control using an emotion engine

[0696] The emotion engine analyzes the user's emotional state.

[0697] For example, if the user is startled, the drone will rapidly increase in altitude.

[0698] If the user feels comfortable, the drone will move slowly.

[0699] Examples and prompts

[0700] Example 1: Transporting supplies to disaster areas

[0701] Prompt: "Analyze image data from disaster areas to determine which areas need food."

[0702] How it works: Drones take images of disaster areas, which are then received and analyzed by a server. Based on the analysis, generative AI identifies areas in need of food, and the server issues instructions to the drones to deliver the food to those areas. The drones then load the food and deliver it to the designated areas.

[0703] Example 2: Drone flight control based on user emotions

[0704] Prompt: "If the user is startled, perform a rapid altitude increase motion on the drone."

[0705] Action: The emotion engine analyzes the user's facial expressions and voice and determines that the user is surprised. The drone receives this information and takes action by rapidly increasing its altitude.

[0706] This system enables quick and effective support to be provided to remote areas and disaster areas, and realizes flexible responses according to the emotional state of the user. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0707] Step 1:

[0708] Information gathering

[0709] The drones will be flown to designated remote locations or disaster areas.

[0710] Input: Drone cameras and sensors collect on-site image and environmental data.

[0711] Data processing: The drone compresses the collected data in real time and transmits it to a server via communication infrastructure.

[0712] Output: Local image and environmental data sent to the server.

[0713] Specific operation: The drone photographs the damage to buildings and the gathering of people in the disaster area.

[0714] Step 2:

[0715] Data reception and analysis

[0716] The server receives the data sent from the drone in real time.

[0717] Input: Image and environmental data transmitted from the drone.

[0718] Data calculation: The server inputs the received data into the generative AI and analyzes the local situation.

[0719] Output: The analysis results in information about which areas need the most support.

[0720] Specific operation: The server analyzes the degree of damage in the affected area from image data and identifies areas that need assistance.

[0721] Step 3:

[0722] Deciding how to support

[0723] The server inputs the analysis results into the generative AI.

[0724] Input: Information about the area where you need assistance as a result of your analysis.

[0725] Data calculation: Generative AI learns the optimal support method based on past data and determines the support needed.

[0726] Output: Information on what type and how much of the supplies to transport as a means of support.

[0727] How it works: Generative AI identifies areas with shortages of food or medicine and selects appropriate supplies.

[0728] Step 4:

[0729] Drone instructions

[0730] Based on the results of the generative AI's judgment, the server issues specific transport instructions to the drone.

[0731] Input: Instructions for transporting supplies as a method of support.

[0732] Data processing: The server converts the instructions into a format that the drone can understand.

[0733] Output: Delivery instructions to drone.

[0734] Specific operation: The server commands the drone to deliver food to a specific location.

[0735] Step 5:

[0736] Material transportation

[0737] The drone follows instructions from the server, loads the specified supplies, and transports them to the specified location.

[0738] Input: Transport instructions from the server.

[0739] Data processing: The drone calculates the flight path and loads supplies based on instructions.

[0740] Output: Delivery of supplies to a specified location.

[0741] Specific operation: Drones deliver food to evacuation centers in disaster areas.

[0742] Step 6:

[0743] Flight control by emotion engine

[0744] The emotion engine analyzes the user's emotional state.

[0745] Input: User facial and voice data.

[0746] Data calculation: The emotion engine analyzes the user's emotional state and reflects the results in the drone's flight control.

[0747] Output: Drone flight control instructions according to the user's emotional state.

[0748] Specific behavior: If the user feels surprised, the drone will rapidly increase in altitude. If the user feels safe, the drone will move slowly.

[0749] (Application example 3)

[0750] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0751] Conventional drone technology and generative AI systems have had issues with being unable to provide fast and effective support to remote areas and disaster-stricken areas. They also lack the ability to respond flexibly to the user's emotional state and lack the accuracy required for anomaly detection. This has led to problems that reduce the efficiency and safety of support activities.

[0752] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a transportation means using drone technology, an information processing means using generative AI, a learning means based on the evolution of the generative AI, a flight control means using an emotion engine to respond to the user's emotional state, and anomaly detection means. This enables fast and effective support to remote areas and disaster areas, and enables flexible responses according to the user's emotional state and highly accurate anomaly detection.

[0753] "Drone technology" refers to the use of unmanned aerial vehicles for transportation, transporting supplies, and gathering information.

[0754] "Generative AI" is an artificial intelligence technology that generates and analyzes information based on collected data.

[0755] "The evolution of generative AI" refers to the evolutionary process of artificial intelligence that learns from past experiences and data and derives the most optimal support methods.

[0756] An "emotion engine" is a technology that detects the user's emotional state and adjusts the system's behavior accordingly.

[0757] "Flight control means" refers to means for controlling the flight pattern, altitude, and speed of a drone.

[0758] "Anomaly detection means" is a means for analyzing collected data and video to detect anomalies.

[0759] A system for implementing this invention includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a flight control means that responds to the user's emotional state using an emotion engine, and an anomaly detection means.

[0760] The server uses image analysis libraries such as OpenCV to analyze the video data acquired from the drone. This allows it to detect specific colors and shapes and identify abnormalities. The generative AI also uses emotion recognition engines such as DeepFace to detect the user's emotional state in real time.

[0761] The device uses a drone control library, such as the DJI SDK, to control the drone's flight, allowing it to adjust its flight pattern, altitude, and speed based on the user's emotional state.

[0762] For example, if a drone detects a red object (such as a sign of a fire) while patrolling, the server will determine the abnormality and issue an alert. Also, if the user is surprised, the terminal will rapidly increase the drone's altitude to attract the user's attention.

[0763] An example prompt is, "Create a program that will issue an alert if the drone detects an abnormality while patrolling. Also, add a function that will adjust the drone's flight pattern according to the user's emotional state."

[0764] In this way, it becomes possible to provide fast and effective support to remote areas and disaster areas, and to realize flexible responses according to the user's emotional state and highly accurate anomaly detection.

[0765] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0766] Step 1:

[0767] The server receives video data from the drone. The input is real-time video data sent from the drone, and the output is the video frames to be analyzed. Specifically, the server captures the video streamed from the drone's camera and saves it frame by frame.

[0768] Step 2:

[0769] The server analyzes the acquired video data using OpenCV. The input is the video frame acquired in step 1, and the output is the result of the anomaly detection. Specifically, the server converts the video frame into the HSV color space and performs masking to detect specific colors (e.g., red). If the specific color is detected above a certain level as a result of the masking process, it is determined to be an anomaly.

[0770] Step 3:

[0771] The server detects the user's emotional state using an emotion recognition engine such as DeepFace. The input is the user's facial image captured by the drone, and the output is the user's emotional state (e.g., surprise, relief). Specifically, the server uses a face detection algorithm to identify the user's face and classifies the emotion using the emotion recognition engine.

[0772] Step 4:

[0773] The server generates flight control instructions for the drone based on the anomaly detection results and the user's emotional state. The input is the output of Step 2 and Step 3, and the output is a control instruction for the drone. Specifically, the server issues an alert if an anomaly is detected, and generates an instruction to rapidly increase the drone's altitude if the user feels surprised.

[0774] Step 5:

[0775] The device receives control instructions from the server and controls the drone using the DJI SDK. The input is the control instruction sent from the server, and the output is a change in the drone's flight pattern. Specifically, the device adjusts the drone's altitude and speed according to the instructions from the server.

[0776] Step 6:

[0777] The user checks the drone's status and the results of anomaly detection through the device. The input is a notification from the device, and the output is the user's recognition. Specifically, the user checks the drone's video and anomaly detection alerts on the device screen, and gives additional instructions as necessary.

[0778] The above processing steps enable rapid and effective support to be provided to remote areas and disaster areas.

[0779] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0780] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generative AI models. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0781] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0782] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0783] [Second embodiment]

[0784] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0785] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0786] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0787] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0788] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0789] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0790] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0791] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0792] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0793] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0794] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0795] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0796] "Example 1"

[0797] An example of this invention is a system that combines a means of transportation using drone technology, an information processing means using generative AI, and a learning means based on the evolution of generative AI. Specifically, the drone uses GPS, sensors, etc. to fly autonomously, traveling to a destination and transporting supplies. The generative AI generates and analyzes the necessary information based on user instructions and the situation. The learning means based on the evolution of generative AI learns optimal behavior based on the user's behavior patterns and the situation, and acts based on that. By combining these means, it is possible to solve issues such as supporting the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[0798] "Example 2"

[0799] A concrete example is a system that provides support for the daily lives of the elderly. Drones assist the elderly with mobility and deliver necessary supplies. Generative AI understands the health status and lifestyle habits of the elderly and generates the necessary information. The learning methods that result from the evolution of generative AI learn the behavioral patterns of the elderly and provide optimal support based on that. Combining these methods can improve the quality of daily life for the elderly.

[0800] "Example 3"

[0801] Another possible system is to provide effective support to remote and disaster-stricken areas. Drones will transport supplies and collect information to remote and disaster-stricken areas. Generative AI will determine the support needed based on the collected information, and the learning method that evolves through generative AI will learn the optimal support method from past experience. By combining these methods, it will be possible to provide rapid and effective support to remote and disaster-stricken areas.

[0802] The processing flow of each embodiment will be described below.

[0803] "Example 1"

[0804] Step 1: The drone begins autonomous flight using GPS, sensors, etc.

[0805] Step 2: Transport to the destination and deliver supplies.

[0806] Step 3: The generative AI generates and analyzes the necessary information based on user instructions and the situation.

[0807] Step 4: Generative AI will evolve to learn optimal behavior based on the user's behavioral patterns and circumstances, and then act accordingly.

[0808] "Example 2"

[0809] Step 1: Drones will assist elderly people with mobility assistance and deliver necessary supplies.

[0810] Step 2: The generative AI understands the health status and lifestyle habits of the elderly and generates the necessary information.

[0811] Step 3: The learning means of generative AI evolves to learn the behavioral patterns of the elderly and provide optimal support based on that.

[0812] "Example 3"

[0813] Step 1: Drones will transport supplies and gather information in remote areas and disaster zones.

[0814] Step 2: The generative AI determines the necessary support based on the collected information.

[0815] Step 3: The learning method used by the generative AI evolves to learn the optimal support method from past experience.

[0816] Example 1

[0817] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0818] There are many challenges, such as providing support for the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, protecting the environment, and preventing natural disasters. Conventional technologies have not been able to adequately address these challenges. In particular, there is a need for learning and executing optimal actions according to the user's behavioral patterns and situations.

[0819] The identification processing by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for receiving instructions from a user, generating information using a generative AI model, and providing the user with the analysis results, means for learning optimal behavior based on the user's behavioral patterns and situation and taking action based on the learned behavior, means for transportation using drone technology, information processing means using generative AI, and learning means through the evolution of generative AI. This enables daily life support for the elderly and people with physical limitations, effective support for remote areas and disaster areas, environmental protection, and natural disaster prevention.

[0820] "Drone technology" refers to the use of unmanned aerial vehicles to fly autonomously and perform functions such as transporting goods and conducting surveillance.

[0821] "Generative AI" is a type of artificial intelligence that generates and analyzes necessary information based on user instructions and the situation.

[0822] A "generative AI model" is a specific implementation of generative AI, such as an algorithm that generates text and data using a natural language processing model.

[0823] "Learning means" refers to technology that learns optimal behavior based on the user's behavioral patterns and circumstances, and then acts based on that.

[0824] "Information processing means" refers to technology for generating and analyzing information using generative AI.

[0825] The "means for receiving instructions from the user" refers to an interface or communication means by which the system receives instructions input by the user.

[0826] "Means for providing analysis results to users" refers to technology for displaying or notifying the generated information and analysis results to users.

[0827] "Behavioral patterns" refer to the tendencies of the actions and choices that a user makes on a daily basis.

[0828] "Optimal action" refers to the action that is most suitable for the user's situation and purpose.

[0829] "Transportation" refers to technology that uses drone technology to transport goods and people to their destinations.

[0830] "Problem-solving means" are technologies and methods for solving specific problems, such as supporting the daily lives of the elderly and people with physical limitations, providing support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[0831] This invention is a system that combines drone technology, generative AI, and learning methods enabled by the evolution of generative AI, and its aims are to support the daily lives of the elderly and people with physical limitations, provide effective support to remote areas and disaster-stricken areas, protect the environment, and prevent natural disasters.

[0832] Server Roles

[0833] The server receives instructions from the user, generates information using a generative AI model, and provides the analysis results to the user. Specifically, the server uses a generative AI model (e.g., GPT-4) implemented using Python. The server inputs the user's prompt into the generative AI model and analyzes the generated information. The analysis results are also used as data to learn optimal actions based on the user's behavioral patterns and circumstances.

[0834] Device Role

[0835] The terminal provides an interface for inputting instructions from the user. For example, a smartphone or tablet can be used as the terminal. When the user inputs "Tell me the best route to my next destination" into the terminal, the terminal sends that information to the server. The generated information returned from the server is then displayed to the user.

[0836] User Roles

[0837] The user issues instructions to the system via a terminal. For example, they might input, "I would like to request the delivery of supplies to a disaster area." Based on the user's instructions, the drone will fly autonomously and deliver the supplies to the specified destination. The user can then decide their next action based on the information provided by the generative AI.

[0838] Specific examples

[0839] Example 1: Assisting the elderly in their daily lives

[0840] The elderly person types "Confirm my next hospital appointment" into the device. The device sends the information to the server, which uses generative AI to generate and analyze the appointment information. The analysis results are returned to the device, allowing the elderly person to check the date and time of their next hospital appointment.

[0841] Example 2: Support for disaster-stricken areas

[0842] The user inputs into the device, "I would like to request the delivery of supplies to the disaster area." The device sends this information to the server, which then uses generative AI to generate the optimal route and delivery method. The drone then flies autonomously based on this information and delivers the supplies to the specified destination.

[0843] Prompt Sentence Examples

[0844] 1. "What's the best route to my next destination?"

[0845] 2. "I would like to request the transportation of supplies to disaster areas."

[0846] 3. "Check my next doctor's appointment."

[0847] By inputting these prompts into a generative AI model, users can obtain the information they need.

[0848] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0849] Step 1: User input

[0850] A user inputs instructions into the system through a terminal. For example, the user might input, "Tell me the best route to my next destination." The input instructions are sent to the server through the terminal interface. The input data is in text format and specifically describes the user's request.

[0851] Step 2: Send instructions to the server

[0852] The terminal sends instructions received from the user to the server. Specifically, it sends the user's prompt to the server using an HTTP request. The input is the user's instruction text, and the output is the request sent to the server.

[0853] Step 3: Information generation using generative AI models

[0854] The server generates the necessary information using a generative AI model (e.g., GPT-4) based on the received instructions. Specifically, it runs a Python script and inputs the user's prompt into the generative AI model. The input is the user's instruction text, and the output is the generated information text.

[0855] Step 4: Analyze the generated information

[0856] The server analyzes the information returned by the generative AI model. For example, it analyzes the generated route information and identifies the optimal route. The input is the generated information text, and the output is the analysis result.

[0857] Step 5: Submitting the analysis results

[0858] The server sends the analysis results to the terminal. Specifically, it returns the analysis results to the terminal using an HTTP response. The input is the analysis results, and the output is the response sent to the terminal.

[0859] Step 6: Receiving the analysis results

[0860] The terminal receives the analysis result returned from the server. The input is the response from the server, and the output is the reception of the analysis result.

[0861] Step 7: Viewing the analysis results

[0862] The terminal displays the received analysis results to the user. Specifically, optimal route information is displayed on the terminal screen. The input is the analysis results, and the output is the information displayed to the user.

[0863] Step 8: Decide on next steps

[0864] The user checks the analysis results displayed on the device and decides on the next course of action, for example, heading to the destination by following the displayed route. The input is the analysis results, and the output is the user's decision on the course of action.

[0865] (Application example 1)

[0866] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0867] Conventional food delivery systems have had issues with delivery efficiency and user convenience. In particular, delivery route optimization and learning of user behavior patterns have not been adequately performed, resulting in delivery delays and low user satisfaction. Support for the elderly and people with physical limitations has also been insufficient, and providing prompt support to remote areas and disaster-stricken areas has also been an issue.

[0868] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0869] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a means for analyzing the user's order details and the delivery destination situation and calculating the optimal route, and a means for learning the user's behavioral patterns and preferences to enable smoother future orders. This improves delivery efficiency, enhances user convenience, and enables support for the elderly and people with physical limitations, as well as rapid support in remote areas and disaster-stricken areas.

[0870] "Drone technology" refers to the use of unmanned aerial vehicles to fly autonomously and perform functions such as transporting goods and conducting surveillance.

[0871] "Transportation" refers to a method or device for transporting objects or people from one point to another.

[0872] "Generative AI" is an artificial intelligence technology that generates and analyzes necessary information based on user instructions and the situation.

[0873] "Information processing means" refers to methods and devices for collecting, analyzing, and generating data.

[0874] "Learning methods through the evolution of generative AI" refers to artificial intelligence technology that learns optimal behavior based on the user's behavior patterns and circumstances, and then acts based on that.

[0875] "Means for analyzing the contents of a user's order and the status of the delivery destination and calculating the optimal route" refers to a method or device for analyzing the contents of a user's order and information on the delivery destination and calculating the most efficient delivery route.

[0876] "Means for learning user behavioral patterns and preferences to enable smoother ordering in the future" refers to a method or device for learning a user's past behavior and preferences and optimizing the ordering process in the future.

[0877] "Daily life support for the elderly and people with physical limitations" refers to providing the support necessary for the elderly and people with physical limitations to carry out their daily lives.

[0878] "Effective support to remote and disaster-stricken areas" refers to providing prompt and effective support to remote and disaster-stricken areas.

[0879] "Environmental protection and natural disaster prevention" refers to activities and measures to protect the environment and prevent the occurrence of natural disasters.

[0880] The system for implementing this invention combines drone technology, generative AI, and learning means based on the evolution of generative AI. Specific embodiments of this system are described below.

[0881] System Configuration

[0882] The system consists of the following main components:

[0883] 1. Drones: Equipped with GPS and sensors, they fly autonomously.

[0884] 2. Server: Executes the generative AI and the learning method that evolves the generative AI.

[0885] 3. User device: The user places the order on a device such as a smartphone.

[0886] Program processing

[0887] Hardware

[0888] Drone: GPS, sensors

[0889] Server: High-performance processor, memory

[0890] User device: Smartphone

[0891] software

[0892] Generative AI: Python, TensorFlow

[0893] Learning AI: Python, Scikit-learn

[0894] Data processing and calculation

[0895] 1. Order analysis: When a user places an order on their smartphone, the server uses generative AI to analyze the order and calculate the optimal route.

[0896] 2. Behavioral learning: The server uses generative AI to learn the user's behavioral patterns and preferences, making future orders smoother.

[0897] 3. Autonomous flight: The drones use GPS and sensors to fly autonomously and deliver meals to designated locations.

[0898] Specific examples

[0899] Consider a scenario where a user orders a pizza via smartphone and has it delivered to their home. Once the user confirms the order, the server uses generative AI to analyze the order and calculate the optimal delivery route. Next, the server uses generative AI to learn the user's past order history and behavioral patterns, allowing for smoother future orders. The drone then autonomously flies from the warehouse to the user's home to deliver the pizza.

[0900] Prompt Sentence Examples

[0901] "Build a system where a user can order a pizza on their smartphone and a drone will deliver it to their home. Use generative AI to analyze the order and calculate the optimal route. Also, use the advanced learning methods of generative AI to learn the user's behavioral patterns and preferences, making future orders smoother."

[0902] In this way, the system can solve problems such as providing daily support to the elderly and people with physical limitations, providing effective support to remote and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[0903] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0904] Step 1:

[0905] The user places an order on their smartphone. The user opens the smartphone application, selects the meal they want to order, and enters the delivery address. The input data includes the order details and delivery address information.

[0906] Step 2:

[0907] The server receives the order details and analyzes them using generative AI. The server receives the order details and delivery destination information from the user as input, and calculates the optimal delivery route using a generative AI model. The output is the optimal delivery route information.

[0908] Step 3:

[0909] The server uses generative AI to learn the user's behavioral patterns and preferences. The server receives past order history and user behavior data as input, and uses a generative AI model to learn the user's behavioral patterns and preferences. The output is a learned user profile.

[0910] Step 4:

[0911] The server sends the optimal delivery route to the drone. The server then sends the calculated optimal delivery route information to the drone. The drone then begins autonomous flight based on the received delivery route information. The input is the optimal delivery route information, and the output is flight instructions for the drone.

[0912] Step 5:

[0913] The drone flies autonomously and delivers meals to the designated destination. The drone flies autonomously using GPS and sensors to head to the designated destination. The input is the drone's flight instructions, and the output is a delivery completion notification.

[0914] Step 6:

[0915] The server notifies the user that the delivery is complete. When the drone completes the delivery, the server sends a delivery completion notification to the user's smartphone. The input is the delivery completion notification, and the output is a notification to the user.

[0916] In this way, the system can efficiently carry out the entire process from the user's order to the completion of delivery.

[0917] Example 2

[0918] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0919] Issues such as providing support for the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters have not been adequately resolved with existing technologies. In particular, there is a need for systems that can effectively support elderly people by integrating multiple technologies, such as understanding their health status, learning their behavioral patterns, and transporting supplies using drones.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0921] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, a means for collecting health data and behavioral data, a means for calculating drone flight routes and transporting supplies, a means for analyzing health conditions and lifestyle habits using a generative AI model and generating information, and a means for learning behavioral patterns using a machine learning algorithm and providing optimal support. This improves the quality of daily life for the elderly and people with physical limitations and enables rapid and effective support in remote areas and disaster areas.

[0922] "Drone technology" refers to the use of unmanned aerial vehicles to transport goods and assist with transportation.

[0923] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate information.

[0924] "The evolution of generative AI" is the process by which artificial intelligence improves its performance through learning.

[0925] "Health data" refers to data that indicates an individual's health condition, such as heart rate, blood pressure, and number of steps taken.

[0926] "Behavioral data" refers to data that indicates an individual's daily behavior, such as meal times and walking times.

[0927] A "flight route" is the route a drone will take to reach its destination.

[0928] A "generative AI model" is a specific implementation of generative AI, and includes algorithms and programs for data analysis and information generation.

[0929] A "machine learning algorithm" is a computational method for learning patterns based on data and making predictions and classifications.

[0930] "Optimal support" refers to support and services that are provided in a manner that best suits the needs of each individual user.

[0931] This invention is a system for solving problems such as providing daily support for the elderly and physically limited people, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters. This system includes drone technology, generative AI, the evolution of generative AI, collection of health and behavioral data, calculation of drone flight routes, data analysis using generative AI models, and learning behavioral patterns using machine learning algorithms to provide optimal support.

[0932] 1. Program Generation

[0933] The server generates the system's program, which includes drone control, generative AI data processing, and learning of elderly people's behavioral patterns.

[0934] 2. Program processing explanation

[0935] The server performs the following processing using the generated program.

[0936] Drone Control

[0937] The server uses drone control software (e.g., DJI SDK) to assist elderly people with their mobility and deliver necessary supplies. Specifically, the server analyzes GPS data and calculates the drone's flight route. The drone then travels along that route and delivers the supplies to the elderly.

[0938] Data processing using generative AI

[0939] The server uses a generative AI model (e.g., OpenAI GPT-4) to understand the health status and lifestyle habits of the elderly. Specifically, the server collects the elderly's health data (e.g., heart rate, blood pressure) and inputs it into the generative AI model. The generative AI model analyzes this data and generates the necessary information (e.g., health advice).

[0940] Learning behavioral patterns

[0941] The server uses a machine learning algorithm (e.g., TensorFlow) to learn the behavioral patterns of the elderly. Specifically, the server collects data on the elderly's daily behavior (e.g., meal times, walking time) and inputs it into a machine learning model. The model uses this data to learn the elderly's behavioral patterns and provide optimal support.

[0942] 3. Examples and prompts

[0943] As a concrete example, the following scenario can be considered.

[0944] Example 1: Drone delivery of goods

[0945] Users (elderly people) use a smartphone app to order necessary supplies (e.g., medicine, food). The server receives the order information and uses drone control software to deliver the supplies to the elderly person.

[0946] Example 2: Understanding your health status

[0947] Users (elderly people) wear a wearable device to collect health data. The server inputs the collected data into a generative AI model and generates health advice.

[0948] Prompt Sentence Examples

[0949] "How long does a 70-year-old man take for a walk each day?"

[0950] What should elderly people do if they have high blood pressure?

[0951] Combining these measures can improve the quality of everyday life for older people.

[0952] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0953] Step 1:

[0954] Data collection

[0955] Users wear a wearable device to collect health data (e.g., heart rate, blood pressure, number of steps). The device (wearable device) measures this data in real time and transmits it to a server via Bluetooth or Wi-Fi.

[0956] Input: User health data

[0957] Data processing: The terminal formats the measurement data and converts it into a form that can be transmitted.

[0958] Output: Health data sent to the server

[0959] Step 2:

[0960] Drone control and cargo transportation

[0961] Users use a smartphone app to order necessary supplies (e.g., medicine, food). The server receives the order information and uses drone control software to deliver the supplies to the elderly person. The server analyzes GPS data and calculates the drone's flight route. The drone then travels along that route and delivers the supplies to the user.

[0962] Input: User order information, GPS data

[0963] Data processing: The server analyzes the order information and calculates the drone's flight route.

[0964] Output: Drone flight route, cargo transportation

[0965] Step 3:

[0966] Data analysis and information generation using generative AI

[0967] The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze the user's health status and lifestyle habits and generate the necessary information. The server inputs the collected health data into the generative AI model, which then analyzes this data and generates health advice and lifestyle improvement suggestions.

[0968] Input: Health data

[0969] Data processing: The generative AI model analyzes health data and generates the necessary information.

[0970] Output: Health advice, lifestyle improvement suggestions

[0971] Step 4:

[0972] Learning behavioral patterns and providing optimal support

[0973] The server uses a machine learning algorithm (e.g., TensorFlow) to learn the user's behavioral patterns and provide optimal support. The server collects the user's daily behavioral data (e.g., meal times, walking time) and inputs it into a machine learning model. The model uses this data to learn the user's behavioral patterns and provide optimal support.

[0974] Input: Daily behavior data

[0975] Data processing: Machine learning models analyze behavioral data and learn behavioral patterns.

[0976] Output: Optimal assistance (e.g., reminders, health advice)

[0977] Through these steps, the quality of daily life of older people can be improved.

[0978] (Application example 2)

[0979] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0980] In order to support the daily lives of the elderly and people with physical limitations, there is a lack of means to monitor their health status, provide appropriate meal plans, and efficiently deliver those meals. Similar support is also needed in remote areas and disaster areas, but the current system does not adequately address these issues.

[0981] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, a means for monitoring health data, a means for generating meal plans, a means for setting a delivery schedule by drone, and a means for optimizing the delivery schedule based on behavioral patterns. This enables efficient and effective support for the daily lives of elderly people and people with physical limitations, and also enables similar support in remote areas and disaster areas.

[0982] "Drone technology" is a technology that uses unmanned aerial vehicles to move objects, monitor them, collect data, and more.

[0983] "Generative AI" is a type of artificial intelligence, a technology that generates new information and content based on data.

[0984] A "learning method" is the method or process by which generative AI analyzes data and learns patterns and trends.

[0985] "Health data" refers to various physiological data that indicate an individual's health status, such as blood pressure, blood sugar level, and heart rate.

[0986] "Monitoring" is the act of continuously observing and recording specific data or conditions.

[0987] A "meal plan" is a suggested meal plan and schedule based on an individual's health status and preferences.

[0988] A "delivery schedule" is a plan or timetable for delivering goods or services at a specific time and place.

[0989] "Behavioral patterns" are data or information that show tendencies in an individual's behavior and habits.

[0990] "Optimization" is the process of deriving the most effective methods and results for specific purposes and conditions.

[0991] A system for carrying out this invention is configured as follows: A server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of the generative AI, a means for monitoring health data, a means for generating meal plans, a means for setting a delivery schedule by drone, and a means for optimizing the delivery schedule based on behavioral patterns.

[0992] Hardware and Software Configuration

[0993] Hardware:

[0994] Smartphone: A device that allows a user to operate applications.

[0995] Drone: An unmanned aerial vehicle used to move or deliver goods.

[0996] Sensors for health data monitoring: Devices for obtaining health data such as blood pressure and blood sugar levels.

[0997] software:

[0998] Python: A programming language used to implement programs.

[0999] Sensor API for Health Data Monitoring: A software interface for acquiring health data.

[1000] Drone control API: A software interface for controlling drone flight and setting delivery schedules.

[1001] Data processing and calculation

[1002] 1. Health Data Monitoring:

[1003] The server uses a sensor API to obtain health data (blood pressure, blood sugar levels, etc.) of the elderly.

[1004] The acquired data is stored on the server and used for analysis by generative AI.

[1005] 2. Generate meal plans:

[1006] The server uses generative AI to generate an optimal meal plan based on the acquired health data and user profile.

[1007] The generated meal plan is sent to the user's smartphone.

[1008] 3. Schedule your delivery:

[1009] The server uses a drone control API to schedule delivery of the generated meal plan at the specified time.

[1010] Delivery schedules are kept on a server and instructions are sent to the drones.

[1011] 4. Schedule optimization:

[1012] The server learns the behavioral patterns of the elderly and optimizes delivery schedules.

[1013] The optimized schedule is maintained on a server and instructions are sent to the drones.

[1014] Specific examples

[1015] If an elderly user likes Japanese food and has high blood pressure, the server operates as follows.

[1016] 1. A health data monitoring sensor measures the user's blood pressure and transmits the data to a server.

[1017] 2. The server uses generative AI to suggest low-salt Japanese meals based on the user's health data and dietary preferences.

[1018] 3. The server uses the drone control API to schedule delivery of the suggested meal to the user's home in one hour.

[1019] 4. The server learns the user's behavioral patterns and optimizes the delivery schedule.

[1020] Prompt Sentence Examples

[1021] User profile:

[1022] Name: Elderly User

[1023] Age: 75

[1024] Food preferences: Japanese, Western, Chinese

[1025] Health Data:

[1026] Blood pressure: 135

[1027] Blood sugar level: 90

[1028] Generated meal plan:

[1029] Low-salt Japanese food

[1030] Delivery Schedule:

[1031] Delivery in 1 hour

[1032] In this way, a system will be realized that efficiently and effectively supports the daily lives of elderly people and people with physical limitations.

[1033] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1034] Step 1:

[1035] The server uses a sensor API for health data monitoring to obtain the user's health data (blood pressure, blood glucose level, etc.). The input is physiological data from the sensor, and the output is health data stored in the server. Specifically, the sensor is worn on the user's body, periodically collects data, and sends it to the server.

[1036] Step 2:

[1037] The server analyzes the acquired health data and uses generative AI to evaluate the user's health condition. The input is the health data acquired in step 1, and the output is the health condition evaluation result. Specifically, the server runs a data analysis algorithm to detect abnormalities in blood pressure and blood sugar levels.

[1038] Step 3:

[1039] The server uses generative AI to generate an optimal meal plan based on the user profile (food preferences, allergy information, etc.) and the health assessment results. The input is the user profile and the health assessment results, and the output is the generated meal plan. Specifically, the server inputs a prompt statement into the generative AI model and generates the meal plan.

[1040] Step 4:

[1041] The server notifies the user of the generated meal plan on their smartphone. The input is the generated meal plan, and the output is a notification displayed on the user's smartphone. Specifically, the server calls the notification API and sends the contents of the meal plan to the user.

[1042] Step 5:

[1043] The server uses the drone control API to schedule delivery of the generated meal plan at the specified time. The input is the generated meal plan and delivery time, and the output is the drone delivery schedule. Specifically, the server calls the drone control API to set the delivery schedule.

[1044] Step 6:

[1045] The server learns the user's behavioral patterns and optimizes the delivery schedule. The input is the user's behavioral data and delivery schedule, and the output is the optimized delivery schedule. Specifically, the server executes a behavioral pattern analysis algorithm and calculates the optimal delivery time.

[1046] Step 7:

[1047] The server sends the optimized delivery schedule to the drone and executes the meal delivery. The input is the optimized delivery schedule, and the output is the meal delivered to the user's home. Specifically, the server sends instructions to the drone, and the drone delivers the meal to the user's home at the specified time.

[1048] Example 3

[1049] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1050] To provide prompt and effective support in remote areas and disaster-stricken areas, it is necessary to accurately grasp the local situation and quickly determine the appropriate support method. However, with conventional methods, it takes time to collect and analyze information, which often results in delays in support. Similar issues exist in providing support for the daily lives of the elderly and people with physical limitations, as well as in environmental protection and natural disaster prevention.

[1051] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1052] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, an information gathering means using drones, a means for inputting the gathered data into a generative AI model, a data analysis means using the generative AI model, a means for proposing a support method based on the analysis results, and a means for transporting and delivering support supplies, thereby enabling fast and effective support in remote areas and disaster areas.

[1053] "Drone technology" is a technology that uses unmanned aerial vehicles to travel to specific locations such as remote areas or disaster areas, and transport supplies and collect information.

[1054] "Transportation means" means a means for using a drone to travel to a designated location.

[1055] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[1056] "Information processing means" refers to means for analyzing collected data and generating necessary information.

[1057] "Learning methods through the evolution of generative AI" refers to a method by which generative AI learns based on past data and experience and proposes more effective support methods.

[1058] "Information collection means" refers to the means of collecting local images and environmental data using drones.

[1059] "Means for inputting collected data into a generative AI model" refers to the means for inputting data collected by a drone into a generative AI model and performing analysis.

[1060] "Means for data analysis using generative AI models" refers to means for analyzing collected data using generative AI models to understand the local situation.

[1061] "Means for proposing support methods based on analysis results" refers to means for proposing optimal support methods based on the analysis results of a generative AI model.

[1062] "Means for transporting and implementing relief supplies" refers to the means for using drones to transport the proposed relief supplies and implement relief on site.

[1063] MODE FOR CARRYING OUT THE INVENTION

[1064] The present invention is a system for realizing rapid and effective support for remote areas and disaster areas. A specific embodiment of this system will be described below.

[1065] 1. Program Generation

[1066] The server generates programs for providing support to remote areas and disaster-stricken areas, including drone operation, information gathering, data analysis using generative AI, and support decision-making.

[1067] 2. Program processing explanation

[1068] The server dispatches drones to remote areas or disaster zones to deliver supplies and gather local information. The drones are equipped with cameras and sensors to collect video and environmental data in real time. The collected data is then sent to the server.

[1069] The server inputs the received data into a generative AI model, which determines the optimal method of assistance based on past disaster response data and assistance track record. This generative AI model uses, for example, OpenAI's GPT-4 or Google's BERT.

[1070] The generative AI model analyzes the collected information and suggests necessary relief supplies and methods. For example, if a shortage of food or medicine is identified, it will instruct the delivery of these supplies as a priority. It will also suggest evacuation routes and determine the need for medical support depending on the situation in the affected area.

[1071] 3. Examples of concrete examples and prompts

[1072] As a concrete example, consider the case where a flood occurs in a certain area. A drone takes aerial photographs of the flood damage and collects information on the location of victims and the status of evacuation routes. The collected data is sent to a server and analyzed by a generative AI model.

[1073] Example prompts for generative AI models:

[1074] "Analyze the damage situation in flood-affected areas and propose the necessary relief supplies and methods of support. The collected data will include the location information of victims, the status of evacuation routes, and the shortage of supplies."

[1075] Based on this prompt, the generative AI model proposes the optimal method of assistance, and the server sends the instructions to the drone, which then transports the proposed relief supplies and delivers them to the disaster area, thereby achieving fast and effective assistance.

[1076] This system combines drone technology and generative AI technology to efficiently provide support to remote areas and disaster areas. Furthermore, by using a learning method based on the evolution of the generative AI model, it is possible to learn the optimal support method from past experience and provide more effective support. The flow of the specific processing in Example 3 is explained using FIG. 15.

[1077] Program processing flow

[1078] Step 1: Dispatch drones and gather information

[1079] The server issues instructions to dispatch drones to remote locations or disaster areas. The drones are equipped with cameras and sensors and collect local video and environmental data. Specifically, the drones use GPS to fly along designated routes and capture video of the disaster area with their cameras. They also use temperature and humidity sensors to collect environmental data. The input is the dispatch instruction from the server, and the output is the collected video data and environmental data.

[1080] Step 2: Sending and Receiving Data

[1081] The drone transmits the collected data to a server in real time. The server stores the received data and prepares it for analysis. Specifically, the video data captured by the drone and the environmental data acquired from the sensors are transmitted to the server via wireless communication. The input is the data collected from the drone, and the output is the data stored on the server.

[1082] Step 3: Data analysis using generative AI models

[1083] The server inputs the received data into a generative AI model. The generative AI model analyzes the optimal method of support based on past disaster response data and support track record. Specifically, the generative AI model identifies the extent of flood damage and determines evacuation routes for victims and necessary support supplies. The input is the collected data stored on the server, and the output is the analysis results.

[1084] Step 4: Propose support methods

[1085] The generative AI model proposes assistance methods based on the analysis results. The server checks the proposed assistance methods and generates specific instructions. Specifically, if the generative AI model detects a shortage of food or medicine, the server issues instructions to prioritize the transportation of these supplies. The input is the analysis results of the generative AI model, and the output is specific assistance instructions.

[1086] Step 5: Delivery and implementation of relief supplies

[1087] The server issues instructions to the drone to transport relief supplies. The drone loads the specified supplies and flies toward the disaster area. After arriving at the disaster area, the drone drops off the supplies and provides the necessary support. Specifically, the drone delivers medicines to the disaster area and hands them over to local medical staff. The input is support instructions from the server, and the output is the delivery of supplies to the disaster area and the implementation of support.

[1088] This series of processes enables rapid and effective support to remote areas and disaster-stricken areas.

[1089] (Application example 3)

[1090] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1091] In order to provide prompt and effective support to remote areas and disaster-stricken areas, it is necessary to accurately grasp the local situation and quickly determine the optimal support method. However, with conventional methods, it takes time to gather information and determine the support method, which often results in delays in support. Optimizing disaster support has also been an issue for logistics centers.

[1092] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1093] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a remote control means using a smartphone, a data analysis means using a cloud server, and a means for optimizing disaster relief at a logistics center, thereby enabling fast and effective support to remote areas and disaster-stricken areas.

[1094] "Drone technology" refers to the use of unmanned aerial vehicles to transport supplies and gather information.

[1095] "Generative AI" is an artificial intelligence technology that generates and analyzes information based on collected data.

[1096] "Learning through the evolution of generative AI" is a learning method for artificial intelligence that evolves by learning the optimal support methods based on past data and experience.

[1097] "Remote control using a smartphone" is a technology that allows you to control a drone in a remote location using a smartphone.

[1098] "Data analysis using cloud servers" is a technology that analyzes data collected using servers on the cloud.

[1099] "Optimization of disaster relief at logistics centers" refers to technology that determines the optimal method of transporting relief supplies and providing support in the event of a disaster at a logistics center.

[1100] The system for implementing the present invention uses the following hardware and software.

[1101] Hardware used

[1102] Smartphone (iOS or Android)

[1103] Drone (with camera and GPS)

[1104] Server (cloud-based, hosts generative AI models)

[1105] Software used

[1106] Smartphone app (Swift for iOS, Kotlin for Android)

[1107] Drone control software (DJI SDK, etc.)

[1108] Generative AI models (e.g., GPT-4)

[1109] Cloud services (AWS, Google Cloud)

[1110] System Operation

[1111] Remote control of drones

[1112] Using a smartphone app, users can remotely control the drone and fly it to the disaster area. The drone captures real-time video footage with its camera and transmits it to the smartphone.

[1113] Information collection and transmission

[1114] The video and sensor data collected by the drone is sent from a smartphone to a cloud server, where a generative AI model analyzes the data and determines the necessary assistance.

[1115] Proposing optimal support methods

[1116] The generative AI model learns the optimal support method based on past data and notifies the smartphone app of the optimal support method.

[1117] Specific examples

[1118] For example, if a flood occurs in a certain area, a user can use a smartphone app to operate a drone to collect video footage of the area. If the generative AI model analyzes the video and determines that there is a shortage of drinking water, a notification will appear on the smartphone app stating, "Please make transporting drinking water your top priority."

[1119] Prompt Sentence Examples

[1120] Analyze footage of flooded areas and suggest the most needed relief supplies.

[1121] In this way, users can transport relief supplies quickly and efficiently.

[1122] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1123] Step 1:

[1124] The user launches a smartphone app and remotely controls the drone.

[1125] Input: User operation instructions (touch operation, GPS coordinates)

[1126] Output: Drone flight start, real-time video acquisition

[1127] How it works: The user touches the smartphone screen to set the drone's flight route, and the drone begins flying along the specified route. The drone's camera captures real-time footage and sends it to the smartphone.

[1128] Step 2:

[1129] The video and sensor data collected by the drone is sent from a smartphone to a cloud server.

[1130] Input: Real-time video from drone, sensor data

[1131] Output: Send data to cloud server

[1132] How it works: The smartphone uploads the video and sensor data received from the drone to a cloud server, which then receives and stores the data.

[1133] Step 3:

[1134] A generative AI model runs on a cloud server and analyzes the data to determine the necessary assistance.

[1135] Input: Video data and sensor data stored on a cloud server

[1136] Output: Analysis results (type and amount of relief supplies needed)

[1137] How it works: A generative AI model on a cloud server analyzes the received video and sensor data. For example, it analyzes flood footage and determines that there is a shortage of drinking water.

[1138] Step 4:

[1139] The generative AI model learns the optimal support method based on past data and notifies the smartphone app of the optimal support method.

[1140] Input: Analysis results, past data

[1141] Output: Notification of the best way to help

[1142] How it works: The generative AI model references past assistance data and learns the best way to provide assistance in the current situation. For example, based on past flood assistance data, it determines that drinking water and medical kits are most needed and notifies the smartphone app.

[1143] Step 5:

[1144] Users can check notifications on their smartphone app and prepare the most appropriate relief supplies.

[1145] Input: Notification from smartphone app

[1146] Output: Preparing relief supplies

[1147] Specific operation: The user checks the notification on the smartphone app and prepares the requested relief supplies (e.g., drinking water, medical kits). Once preparations are complete, the user operates the drone again to deliver the relief supplies to the disaster area.

[1148] In this way, users can transport relief supplies quickly and efficiently.

[1149] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1150] "Example 1"

[1151] One embodiment of the present invention is a system that combines drone technology, generative AI, and an emotion engine. This system recognizes a user's emotions and controls the drone's behavior according to that user's emotional state. Specifically, when the user feels joy, the drone exhibits active movements, and when the user feels sad, the drone exhibits quiet movements. The generative AI also adjusts information generation and analysis according to the user's emotional state. For example, when the user feels stressed, the generative AI generates information related to relaxation, and when the user feels joy, the generative AI generates information related to enjoyable activities.

[1152] "Example 2"

[1153] Another embodiment of the present invention is a system in which an emotion engine adjusts the learning methods of a generative AI according to the user's emotional state. Specifically, when a user feels angry, the generative AI learns the cause and, if a similar situation recurs, suggests measures to alleviate the user's anger. Similarly, when a user feels happy, the generative AI learns the cause and suggests ways to recreate a similar situation.

[1154] "Example 3"

[1155] In a further embodiment of the present invention, an emotion engine adjusts the flight control of a drone according to the user's emotional state. Specifically, when the user feels surprised, the drone performs an action such as rapidly increasing altitude to attract the user's attention. When the user feels safe, the drone performs a slow movement so as not to disturb the user's sense of safety.

[1156] The processing flow of each embodiment will be described below.

[1157] "Example 1"

[1158] Step 1: The emotion engine recognizes the user's emotional state.

[1159] Step 2: The emotion engine controls the drone's behavior according to the recognized emotional state.

[1160] Step 3: At the same time, the generative AI adjusts information generation and analysis according to the user's emotional state.

[1161] "Example 2"

[1162] Step 1: The emotion engine recognizes the user's emotional state.

[1163] Step 2: Adjust the learning methods of the generative AI according to the emotional state recognized by the emotion engine.

[1164] Step 3: Based on the results of the generative AI's learning, measures to improve the user's emotional state are proposed.

[1165] "Example 3"

[1166] Step 1: The emotion engine recognizes the user's emotional state.

[1167] Step 2: Adjust the drone's flight control according to the emotional state recognized by the emotion engine.

[1168] Step 3: The drone operates according to the adjusted flight control.

[1169] Example 1

[1170] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1171] Efficient and flexible means of transportation and information processing are necessary to solve issues such as providing daily support for the elderly and physically limited people, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters. However, conventional technologies have had difficulty in comprehensively solving these issues. Furthermore, there have been no systems that can control movement or provide information according to the user's emotional state.

[1172] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1173] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative artificial intelligence, a learning means based on the evolution of generative artificial intelligence, and a drone control means using an emotion recognition engine. This makes it possible to efficiently and flexibly solve issues such as supporting the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters. It also makes it possible to realize operation control and information provision according to the user's emotional state.

[1174] "Drone technology" refers to the use of unmanned aerial vehicles to move around or transport goods autonomously or remotely.

[1175] "Generative AI" is an AI technology that generates and analyzes necessary information according to user instructions and the situation.

[1176] "The evolution of generative artificial intelligence" refers to the evolution of artificial intelligence technology that learns optimal behavior based on the user's behavioral patterns and circumstances, and optimizes behavior based on the results of that learning.

[1177] An "emotion recognition engine" is a technology that recognizes the user's emotions and controls the system's operation according to that emotional state.

[1178] "Transportation" refers to the use of drone technology to travel to a destination or transport goods.

[1179] "Information processing means" refers to means for generating and analyzing information using generative artificial intelligence.

[1180] The "learning means" is a means for the generative artificial intelligence to evolve and learn optimal behavior according to the user's behavior patterns and situations.

[1181] The "drone control means" is a means for controlling the operation of the drone according to the user's emotional state using an emotion recognition engine.

[1182] MODE FOR CARRYING OUT THE INVENTION

[1183] This invention is a system that combines drone technology, generative artificial intelligence, the evolution of generative artificial intelligence, and an emotion recognition engine, and solves issues such as supporting the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[1184] Transportation using drone technology

[1185] The server uses GPS and various sensors to control the drone's autonomous flight. Specifically, the drone determines its current location based on GPS data and calculates the optimal route to its destination. The sensors are used to detect obstacles and take evasive action. For example, when a drone is to deliver a package, the server sends the coordinates of the delivery destination to the drone as GPS data, and the drone then flies autonomously toward those coordinates.

[1186] Information processing methods using generative artificial intelligence

[1187] The device uses generative AI to generate and analyze the necessary information according to the user's instructions and the situation. Generative AI uses OpenAI's GPT-4, for example. When a user instructs the device to "tell me the weather tomorrow," the device inputs the prompt "tell me the weather tomorrow" into the generative AI and provides the generated weather information to the user.

[1188] Learning methods through the evolution of generative artificial intelligence

[1189] The server uses generative artificial intelligence to learn optimal behavior based on the user's behavioral patterns and circumstances. For example, if a user has a habit of jogging at 7am every morning, the server will learn that pattern and provide jogging information and weather forecasts every morning at 7am.

[1190] Drone control using emotion recognition engine

[1191] The device recognizes the user's emotions and controls the drone's behavior according to that emotional state. The emotion recognition engine uses, for example, Affectiva's emotion recognition software. When the user feels happy, the device instructs the drone to move more actively, and when the user feels sad, it instructs the drone to move more calmly. For example, when the user smiles, the device recognizes that emotion through the emotion recognition engine and sends the drone the command to "move more actively."

[1192] Specific examples

[1193] Example 1: Assisting the elderly with daily living

[1194] If the user is elderly and needs to take their medicine at a specific time each day, the server will use a drone to deliver the medicine at that time. The drone will autonomously fly to the elderly person's home using GPS and land safely using sensors.

[1195] Example 2: Support for disaster-stricken areas

[1196] When delivering supplies to a disaster-hit area, the server loads the supplies onto a drone and has it fly autonomously to the disaster area using GPS data. The sensors detect obstacles and take evasive action.

[1197] Prompt Sentence Examples

[1198] 1. "What's the weather going to be like tomorrow?"

[1199] 2. "Recommend some relaxing music."

[1200] 3. "What's the best route for my next jog?"

[1201] These prompt sentences are input into a generative AI model to provide the user with the information they need.

[1202] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1203] Transportation using drone technology

[1204] Step 1: Set your destination

[1205] The user specifies the drone's destination, for example, by entering "from home to the park" using a smartphone app.

[1206] Input: Destination coordinates

[1207] Output: Destination coordinate data

[1208] Step 2: Route calculation

[1209] The server calculates the optimal route based on the destination specified by the user, using GPS data and map information to generate a route that avoids obstacles and no-fly zones.

[1210] Input: Destination coordinate data, GPS data, map information

[1211] Output: Optimal route data

[1212] Step 3: Start autonomous flight

[1213] The server then sends the calculated route information to the drone, which then uses GPS and sensors to begin autonomous flight. For example, if the drone flies from your home to a park, it will use its sensors to avoid trees and buildings along the way.

[1214] Input: Optimal route data

[1215] Output: Drone begins flying

[1216] Step 4: Reaching the destination

[1217] The drone will follow the specified route to reach its destination. After it arrives, the server will send a notification to the user. For example, a notification will appear on the smartphone saying, "The drone has arrived at the park."

[1218] Input: Drone flight data

[1219] Output: Destination arrival notification

[1220] Information processing methods using generative artificial intelligence

[1221] Step 1: User input

[1222] Users input instructions to generative AI, for example, "Tell me what the weather will be like tomorrow" into a smartphone app.

[1223] Input: User instructions

[1224] Output: prompt statement

[1225] Step 2: Generate a prompt statement

[1226] The terminal generates a prompt sentence based on the user's instruction, for example, "Tell me what the weather will be like tomorrow."

[1227] Input: User instructions

[1228] Output: prompt statement

[1229] Step 3: Information generation and analysis

[1230] The device inputs a prompt sentence into a generative artificial intelligence model (e.g., GPT-4) to generate the necessary information. The generated information is analyzed and provided to the user. For example, the information generated is "Tomorrow will be sunny and the temperature will be 25 degrees."

[1231] Input: prompt statement

[1232] Output: The information generated

[1233] Step 4: Inform users

[1234] The device provides the generated information to the user. For example, a message such as "Tomorrow will be sunny and the temperature will be 25 degrees" will be displayed on the smartphone screen.

[1235] Input: Generated information

[1236] Output: Displaying information to the user

[1237] Learning methods through the evolution of generative artificial intelligence

[1238] Step 1: Collect user behavior patterns

[1239] The server collects the user's behavioral patterns, for example, data that the user goes jogging at 7:00 every morning.

[1240] Input: User behavior data

[1241] Output: Behavioral pattern data

[1242] Step 2: Analyze behavioral patterns

[1243] The server analyzes the collected data and learns the user's behavioral patterns, for example, that the user goes jogging at 7am every morning.

[1244] Input: Behavioral pattern data

[1245] Output: Training results

[1246] Step 3: Recommend the best course of action

[1247] The server then suggests optimal actions based on the learned behavioral patterns. For example, every morning at 7:00, it sends a notification saying, "It's time to go jogging. Today's weather is sunny."

[1248] Input: Training results

[1249] Output: Action suggestion notification

[1250] Step 4: Gather user feedback

[1251] The server collects feedback from users and further improves its learning. For example, if a user gives feedback such as "I'm not going jogging today," the server will use that information to adjust its next suggestion.

[1252] Input: User feedback

[1253] Output: Adjusted training results

[1254] Drone control using emotion recognition engine

[1255] Step 1: Recognizing user emotions

[1256] The terminal uses an emotion recognition engine to recognize the user's emotion. For example, if the user smiles, the emotion recognition engine recognizes "joy."

[1257] Input: User's facial expression data

[1258] Output: Emotion recognition result

[1259] Step 2: Drone movement control

[1260] The device controls the drone's behavior based on the recognized emotion. For example, if the user is feeling happy, the device will send a command to the drone to "move more actively."

[1261] Input: Emotion recognition results

[1262] Output: Drone control instructions

[1263] Step 3: Drone movement

[1264] The drone performs actions according to instructions from the terminal, such as performing acrobatic maneuvers in the air.

[1265] Input: Drone control instructions

[1266] Output: Drone movement

[1267] Step 4: User feedback

[1268] The device collects user reactions to the drone's actions to improve the accuracy of its emotion recognition engine. For example, if the user smiles more, the device will use that information to adjust the drone's next action.

[1269] Input: User response data

[1270] Output: Adjusted emotion recognition results

[1271] (Application example 1)

[1272] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1273] Conventional food delivery systems have difficulty providing services that respond to the user's emotional state, resulting in a lack of appropriate support, particularly for the elderly and those with physical limitations. Providing prompt and effective support to remote areas and disaster-stricken areas is also a challenge. Furthermore, responses to environmental protection and natural disaster prevention are insufficient. To solve these issues, a new system that takes the user's emotional state into account is needed.

[1274] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1275] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, a means for controlling the operation of the drone according to the emotional state of the user, and a means for adjusting information generation and analysis according to the emotional state of the user. This enables optimal food delivery according to the emotional state of the user, thereby realizing support for the daily lives of the elderly and people with physical limitations, effective support for remote areas and disaster-stricken areas, environmental protection, and natural disaster prevention.

[1276] "Drone technology" refers to the use of unmanned aerial vehicles to fly autonomously and perform functions such as transporting goods and conducting surveillance.

[1277] "Transportation" refers to a method or device for transporting objects or people from one place to another.

[1278] "Generative AI" is a system that uses artificial intelligence technology to generate new information and content from data.

[1279] "Information processing means" refers to methods and devices for collecting, analyzing, transforming, storing, and transmitting data.

[1280] "Means of learning through the evolution of generative AI" refers to methods and devices that allow generative AI to learn and evolve optimal behavior based on the user's behavioral patterns and circumstances.

[1281] "Means for controlling drone operation according to the user's emotional state" refers to a method or device for analyzing a user's emotions and adjusting the drone's operation based on the results.

[1282] "Means for adjusting information generation and analysis according to the user's emotional state" refers to a method or device for analyzing a user's emotions and adjusting the information generated by the generative AI and the analysis method based on the results.

[1283] "Assisted daily living for the elderly and physically limited" refers to methods and devices for providing the elderly and physically limited people with the assistance they need to carry out their daily lives.

[1284] "Effective support to remote and disaster-stricken areas" refers to methods and equipment for providing prompt and appropriate support to remote and disaster-stricken areas.

[1285] "Environmental protection and natural disaster prevention" refers to methods and devices for protecting the environment and preventing the occurrence of natural disasters.

[1286] As an embodiment of the present invention, an emotion-responsive food delivery drone system will be described as an example.

[1287] System Program

[1288] The system combines drone technology, generative AI, and an emotion engine, and specifically uses the following hardware and software:

[1289] Hardware

[1290] Drone: An unmanned aerial vehicle equipped with GPS and sensors and capable of autonomous flight.

[1291] Smartphone: Used as a user interface and equipped with a camera and microphone for emotion recognition.

[1292] Server: Data processing and generative AI execution environment.

[1293] software

[1294] GPS module: Software for obtaining drone location information.

[1295] Drone control software: Software for controlling the autonomous flight of drones.

[1296] Emotion recognition software: Software for analyzing user emotions.

[1297] Generative AI: Artificial intelligence that generates information based on the user's emotional state.

[1298] Processing flow

[1299] 1. Emotion recognition: When a user places an order through their smartphone, the smartphone's camera and microphone are used to analyze the user's emotions. Emotion recognition software processes this data and identifies the user's emotional state.

[1300] 2. Menu generation: Based on the analyzed emotions, the generative AI generates an appropriate menu. For example, if the user is feeling stressed, it will suggest a menu with a relaxation effect.

[1301] 3. Drone control: Using GPS information, the drone autonomously flies to its destination. The drone control software calculates the flight path and guides the drone to its destination.

[1302] 4. Food Delivery: The drone will arrive at the destination and deliver the food. The drone's behavior will also be adjusted depending on the user's emotional state. For example, if the user is happy, the drone will be more active.

[1303] Specific examples

[1304] If the user is stressed, the generative AI will suggest relaxing herbal tea or snacks, which the drone will deliver, and if the user is happy, the generative AI will suggest menu items related to enjoyable activities, which the drone will deliver.

[1305] Prompt Sentence Examples

[1306] If the user's emotional state is "stressed," generate a menu with a relaxation effect.

[1307] In this way, the emotion-responsive food delivery drone system can provide optimal service according to the user's emotions.

[1308] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1309] Step 1:

[1310] A user places a food delivery order via their smartphone. The smartphone's camera and microphone are used to collect the user's emotional data (such as facial expressions and tone of voice). The input is the user's emotional data, and the output is raw data sent to emotion recognition software.

[1311] Step 2:

[1312] The server uses emotion recognition software to analyze the user's emotional data. Specifically, it analyzes the collected facial expressions and tone of voice to identify the user's emotional state (e.g., joy, sadness, stress, etc.). The input is the raw data collected in step 1, and the output is the analyzed emotional state.

[1313] Step 3:

[1314] The server uses generative AI to generate an appropriate menu based on the analyzed emotional state. For example, if the user is feeling stressed, it will suggest a menu with a relaxation effect. The input is the emotional state identified in step 2, and the output is the generated menu.

[1315] Step 4:

[1316] The server sends the generated menu to the user's smartphone and asks for confirmation. The user checks the menu and confirms the order. The input is the generated menu, and the output is the user's confirmation result.

[1317] Step 5:

[1318] The server uses drone control software to set the destination (user's address) for the drone. Using GPS information, the drone autonomously flies to the destination. The input is the user's address information, and the output is the drone's flight path.

[1319] Step 6:

[1320] The drone arrives at the destination and delivers the meal. The drone's behavior is adjusted according to the user's emotional state. For example, if the user is happy, the drone will be more active. The input is the drone's flight path and the user's emotional state, and the output is the meal has been delivered.

[1321] Step 7:

[1322] The server sends a delivery completion notification to the user's smartphone. The user confirms the delivery and provides feedback if necessary. The input is delivery completion information, and the output is user feedback.

[1323] Example 2

[1324] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1325] There is a need to efficiently support the daily lives of the elderly and people with physical limitations. There are also challenges, such as providing prompt and effective support to remote areas and disaster-stricken areas, as well as environmental protection and natural disaster prevention. To solve these challenges, a system that combines the latest technologies is needed.

[1326] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1327] In this invention, the server includes a means for collecting elderly behavioral data, a means for analyzing and learning from the collected behavioral data, a means for providing mobility assistance and transporting goods by drone, a means for collecting and analyzing user emotion data, a means for adjusting the generative AI's learning means based on the analysis results, and a means for processing user requests. This not only enables efficient support for the daily lives of elderly people and those with physical limitations, but also enables rapid and effective support in remote areas and disaster-stricken areas. It also contributes to environmental protection and natural disaster prevention.

[1328] "Means for collecting elderly people's behavioral data" refers to devices or systems that use sensors or wearable devices to collect daily behavioral data such as walking distance, heart rate, and meal times of elderly people.

[1329] "Means for analyzing and learning from collected behavioral data" refers to software or algorithms that analyze collected behavioral data and learn behavioral patterns and health conditions.

[1330] "Means for providing mobility assistance and transporting supplies using drones" is a system that uses drones to assist the mobility of elderly people and transport necessary supplies.

[1331] The "means for collecting and analyzing user emotional data" refers to devices or software that collect and analyze the user's voice and facial expression data to understand their emotional state.

[1332] The "means for adjusting the learning means of generative AI based on the analysis results" is a system for adjusting the learning algorithms and parameters of generative AI based on the analysis results of emotional data.

[1333] The "means for processing requests from users" is a system for receiving assistance requests sent by users via dedicated terminals and providing appropriate responses.

[1334] The present invention is a system for efficiently supporting the daily lives of elderly people and people with physical limitations. Specific embodiments of this system will be described below.

[1335] Collecting behavioral data on elderly people

[1336] The server collects data on the elderly's behavior from dedicated sensors and wearable devices. Specifically, devices such as pedometers and heart rate monitors are used to obtain daily behavioral data such as walking distance, heart rate, and meal times. This data is sent to the server in real time and stored in a database. MySQL or MongoDB is used as the database.

[1337] Behavioral data analysis and learning

[1338] The server analyzes the collected behavioral data using Python and TensorFlow. For example, it analyzes walking distance and heart rate fluctuations to detect abnormal patterns and health risks early on. The generative AI uses the analysis results to learn the behavioral patterns of the elderly and builds a model to provide optimal support. This makes it possible to constantly monitor the health of the elderly and provide the necessary support.

[1339] Drone-based mobility support and supply transport

[1340] The server issues instructions to the drone based on the analysis results of the generative AI. For example, it can instruct the drone to accompany an elderly person when they go out. The drone uses GPS and a camera to send the elderly person's location information to the server in real time, allowing the server to monitor the elderly person's safety. The drone can also transport necessary supplies. Specifically, the drone automatically starts up when the elderly person leaves their home and flies along the specified route.

[1341] Collecting and analyzing user emotion data

[1342] The device collects the user's voice and facial expression data. For example, it uses the smartphone's camera and microphone to understand the user's emotional state. The collected data is sent to a server in real time, where the server analyzes the emotional data using OpenCV and NLP technology. The analysis results are stored in a database and provided to the generative AI.

[1343] Adjusting learning methods

[1344] Generative AI adjusts its learning methods based on the results of analyzing emotional data. For example, it can identify the cause of a user's anger and suggest countermeasures the next time a similar situation occurs. This allows it to provide optimal support according to the user's emotional state.

[1345] Processing user requests

[1346] Users (elderly people) can request assistance from the server through a dedicated device. For example, they can request grocery delivery. The device is an Android-based tablet, and sends the request through a dedicated application. The server receives the request and sends instructions to drones or other devices to provide the necessary assistance. Specifically, when a user selects "grocery delivery" in the application, the server issues instructions to a drone to transport the specified items.

[1347] Examples of specific examples and prompts

[1348] As a concrete example, the following prompt sentence is input to the generative AI model:

[1349] Prompt: "Design a system to record how far an elderly person walks each day and notify them if their walking distance decreases."

[1350]

[1351] Prompt: "Design a system that, when a user feels anger, identifies the cause of the anger and suggests solutions the next time a similar situation occurs."

[1352] Based on these prompts, the generative AI model can suggest specific support methods and countermeasures.

[1353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1354] Step 1:

[1355] The server collects behavioral data of elderly people from dedicated sensors and wearable devices. Specifically, it uses devices such as pedometers and heart rate monitors to obtain daily behavioral data such as walking distance, heart rate, and meal times. This data is sent to the server in real time and stored in a database. The input is data from the sensors and wearable devices, and the output is behavioral data stored in the database.

[1356] Step 2:

[1357] The server analyzes the collected behavioral data using Python and TensorFlow. For example, it analyzes walking distance and heart rate fluctuations to detect abnormal patterns and health risks early on. The input is the behavioral data stored in the database, and the output is the analysis results. Specifically, the server processes the data in batches every night and trains the generative AI.

[1358] Step 3:

[1359] The generative AI learns the behavioral patterns of the elderly based on the analysis results and builds a model to provide optimal support. The input is the analysis results from the server, and the output is the learned model. In concrete terms, the generative AI learns new data and updates the model parameters.

[1360] Step 4:

[1361] The server issues instructions to the drone based on the generative AI's analysis results. For example, it instructs the drone to accompany an elderly person when they go out. The input is instructions from the generative AI, and the output is instructions to the drone. Specifically, the drone automatically starts up when the elderly person leaves their home and flies along the specified route.

[1362] Step 5:

[1363] The drone uses GPS and a camera to send the location information of the elderly person to a server in real time, allowing the server to monitor the elderly person's safety. The input is data from the drone's sensors, and the output is the transmission of location information to the server. Specifically, the drone flies in accordance with the elderly person's movements, constantly updating its location information.

[1364] Step 6:

[1365] The device collects the user's voice and facial expression data. For example, it uses the smartphone's camera and microphone to understand the user's emotional state. The input is the user's voice and facial expression data, and the output is data transmission to the server. Specifically, the device automatically starts collecting data when the user launches the application.

[1366] Step 7:

[1367] The server analyzes the emotion data using OpenCV and NLP technology. The analysis results are stored in a database and provided to the generative AI. The input is emotion data from the device, and the output is the analysis results. Specifically, the server converts the voice data into text and inputs it into the emotion analysis model.

[1368] Step 8:

[1369] Generative AI adjusts its learning methods based on the results of analyzing emotional data. For example, it can identify the reason why a user felt anger and suggest countermeasures the next time a similar situation occurs. The input is the analysis results from the server, and the output is an adjusted learning model. Specifically, generative AI learns new data and updates the model parameters.

[1370] Step 9:

[1371] Users (elderly people) can request assistance from the server through a dedicated terminal. For example, they can request grocery delivery. The input is the request from the user, and the output is the request sent to the server. Specifically, when the user selects "grocery delivery" in the application, the server issues instructions to the drone to transport the specified items.

[1372] Step 10:

[1373] The server receives requests from users and sends instructions to drones and other devices to provide the necessary assistance. The input is the user's request, and the output is instructions to the drone or other device. Specifically, the server analyzes the request and determines the optimal method of assistance.

[1374] (Application example 2)

[1375] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1376] In order to support the daily lives of the elderly and people with physical limitations, it is necessary to address a wide range of needs, such as transportation, transporting supplies, monitoring health conditions, and analyzing emotional states. Furthermore, there are also challenges, such as providing effective support to remote areas and disaster-stricken areas, protecting the environment, and preventing natural disasters. Systems that can efficiently and effectively solve these challenges are needed.

[1377] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1378] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means by evolving the generative AI, and an emotional state analysis means using an emotion engine. This enables support for the daily lives of the elderly and people with physical limitations, effective support for remote areas and disaster-stricken areas, environmental protection, and natural disaster prevention.

[1379] "Drone technology" is the technology of using unmanned aerial vehicles to move objects, monitor them, and collect data.

[1380] A "transportation means" is a method or device for transporting objects or people from one place to another.

[1381] "Generative AI" is a system that uses artificial intelligence to generate and analyze data.

[1382] "Information processing means" refers to a method or device for analyzing collected data and generating necessary information.

[1383] "Learning means through the evolution of generative AI" refers to methods and devices that allow generative AI to learn based on past data and experience, and perform more advanced information generation and analysis.

[1384] An "emotion engine" is a system that analyzes the user's emotional state and responds appropriately based on the results.

[1385] The "emotional state analysis means" is a method or device for analyzing the user's emotions and understanding their state.

[1386] "Daily life support for the elderly and physically disabled" refers to methods and systems for providing the elderly and physically disabled with the support they need to carry out their daily lives.

[1387] "Effective support to remote and disaster-stricken areas" refers to methods and systems for providing prompt and appropriate support to remote areas and areas affected by disasters.

[1388] "Environmental protection and natural disaster prevention" refers to methods and systems for protecting the environment and preventing the occurrence of natural disasters.

[1389] A system for implementing this invention includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, and an emotional state analysis means using an emotion engine. This enables support for the daily lives of the elderly and people with physical limitations, effective support for remote areas and disaster-stricken areas, environmental protection, and natural disaster prevention.

[1390] System configuration

[1391] Hardware

[1392] Drone: An unmanned aerial vehicle used to transport supplies and provide mobility support.

[1393] Smartphones: Devices that collect health and emotional data from users.

[1394] Sensor: A device that measures health data such as blood sugar levels or blood pressure.

[1395] software

[1396] Generative AI: An artificial intelligence system that generates and analyzes data.

[1397] Emotion engine: A system that analyzes the user's emotional state and responds appropriately.

[1398] Health data management system: A system that manages users' health data and suggests appropriate meals and services.

[1399] Data processing and calculation

[1400] Health data collection

[1401] Devices (smartphones and sensors) collect health data such as the user's blood sugar level and blood pressure. This data is analyzed by generative AI and used to understand the user's health condition.

[1402] Collecting Emotional Data

[1403] The device (smartphone or smart glasses) collects emotion data from the user's facial expressions and voice. The emotion engine analyzes this data and understands the user's emotional state.

[1404] Data analysis

[1405] The server uses generative AI to analyze the collected health and emotional data, and then suggests appropriate meals and services based on the user's health and emotional state.

[1406] Specific examples

[1407] Health data example

[1408] An elderly person with a blood sugar level of 130 and a blood pressure of 135

[1409] Emotion data example

[1410] Users feel "happy"

[1411] Prompt Sentence Examples

[1412] Suggest appropriate meals and services based on the user's health data (blood sugar: 130, blood pressure: 135) and emotional data (happy).

[1413] In this way, the system can assist the elderly and people with physical limitations in their daily lives and provide effective assistance to remote areas and disaster zones.

[1414] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1415] Step 1:

[1416] The device (smartphone or sensor) collects the user's health data (blood glucose level, blood pressure, etc.). The input is data indicating the user's health condition, and the output is the collected health data. Specifically, the sensor measures the user's blood glucose level and blood pressure and sends the data to the smartphone.

[1417] Step 2:

[1418] The device (smartphone or smart glasses) collects emotion data from the user's facial expressions and voice. The input is the user's facial expression and voice data, and the output is analyzed emotion data. Specifically, the smartphone or smart glasses captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[1419] Step 3:

[1420] The server receives the collected health data and emotion data and analyzes the data using generative AI. The input is health data and emotion data, and the output is the analysis results. Specifically, the server analyzes the health data using a generative AI model to evaluate the user's health condition. It also analyzes the emotion data using an emotion engine to evaluate the user's emotional state.

[1421] Step 4:

[1422] Based on the analysis results, the server proposes appropriate meals and services according to the user's health and emotional state. The input is the analysis results, and the output is the proposed meals and services. Specifically, the server uses a generative AI model to generate a meal menu suited to the user's health condition, and an emotion engine to propose services according to the user's emotional state.

[1423] Step 5:

[1424] The device notifies the user of meals and services suggested by the server. The input is the suggestions from the server, and the output is the notification to the user. Specifically, the smartphone displays meal menus and service suggestions to the user, and arranges for meal delivery by drone if necessary.

[1425] Step 6:

[1426] The drone delivers meals to the user's home based on instructions from the server. The input is the delivery instruction, and the output is a delivery completion notification. Specifically, the drone loads the specified meal, flies to the user's home, and delivers it. After the delivery is complete, it sends a notification to the server.

[1427] In this way, the system can assist the elderly and people with physical limitations in their daily lives and provide effective assistance to remote areas and disaster zones.

[1428] Example 3

[1429] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1430] Providing rapid and effective support to remote areas and disaster areas has been difficult with conventional technology. Furthermore, conventional systems have not been able to adequately respond to situations that require flexible responses according to the user's emotional state. Furthermore, there is a need to provide consistent solutions to a wide range of issues, such as supporting the daily lives of the elderly and people with physical limitations, as well as environmental protection and natural disaster prevention.

[1431] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1432] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a means for analyzing user emotions using an emotion engine, and a means for controlling the drone's flight in accordance with the user's emotional state. This enables rapid and effective support in remote areas and disaster-stricken areas. Furthermore, flexible responses based on the user's emotional state enable consistent solutions to a wide range of issues, including support for the daily lives of the elderly and people with physical limitations, environmental protection, and natural disaster prevention.

[1433] "Drone technology" is the technology of using unmanned aerial vehicles to transport supplies and collect information.

[1434] "Generative AI" is an artificial intelligence technology that generates and analyzes information based on collected data.

[1435] "Learning through the evolution of generative AI" is a learning method for artificial intelligence that evolves by learning the optimal support methods based on past data and experience.

[1436] An "emotion engine" is a technology that analyzes a user's emotional state from facial expressions, voice, etc.

[1437] "User emotion analysis" is the process of analyzing a user's emotional state using an emotion engine.

[1438] "Drone flight control" is the technology that controls the flight path and movement of a drone.

[1439] "Transportation" refers to the use of drone technology to transport goods and information.

[1440] "Information processing means" refers to the means of analyzing collected data using generative AI and generating the necessary information.

[1441] "Problem-solving measures" are means for solving problems such as providing support for the daily lives of the elderly and people with physical limitations, providing support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[1442] MODE FOR CARRYING OUT THE INVENTION

[1443] This invention is a system that combines drone technology and generative AI to provide effective support to remote areas and disaster areas. Specific embodiments for implementing this system are described below.

[1444] Hardware and software used

[1445] Drones: Commercial drones (e.g., DJI Phantom 4)

[1446] Generative AI: General artificial intelligence models (e.g., OpenAI GPT-4)

[1447] Emotion engine: Emotion analysis software (e.g. Affectiva SDK)

[1448] Communications infrastructure: high-speed communications networks (e.g., 5G networks)

[1449] System Overview

[1450] This system uses drones to collect information from remote areas and disaster-stricken areas, analyzes that information using generative AI, and then uses an emotion engine to analyze the user's emotional state and reflect this in the drone's flight control, enabling flexible responses based on the user's emotions.

[1451] Program processing

[1452] 1. Information gathering

[1453] Drones fly to designated remote locations or disaster areas and collect local information using cameras and sensors.

[1454] The collected data is sent to a server in real time.

[1455] 2. Data Analysis

[1456] The server inputs the received data into a generative AI system and analyzes the local situation.

[1457] For example, image data from disaster areas can be analyzed to determine which areas need supplies.

[1458] 3. Deciding on support methods

[1459] Generative AI learns the optimal support method based on past data and determines the support needed.

[1460] For example, identify areas where food or medicine is in short supply and select appropriate supplies.

[1461] 4. Transporting supplies

[1462] The server issues instructions to the drone to transport the selected supplies.

[1463] The drones will transport supplies to designated locations and deliver them to the site.

[1464] 5. Flight control using an emotion engine

[1465] The emotion engine analyzes the user's emotional state.

[1466] For example, if the user is startled, the drone will rapidly increase in altitude.

[1467] If the user feels comfortable, the drone will move slowly.

[1468] Examples and prompts

[1469] Example 1: Transporting supplies to disaster areas

[1470] Prompt: "Analyze image data from disaster areas to determine which areas need food."

[1471] How it works: Drones take images of disaster areas, which are then received and analyzed by a server. Based on the analysis, generative AI identifies areas in need of food, and the server issues instructions to the drones to deliver the food to those areas. The drones then load the food and deliver it to the designated areas.

[1472] Example 2: Drone flight control based on user emotions

[1473] Prompt: "If the user is startled, perform a rapid altitude increase motion on the drone."

[1474] Action: The emotion engine analyzes the user's facial expressions and voice and determines that the user is surprised. The drone receives this information and takes action by rapidly increasing its altitude.

[1475] This system enables quick and effective support to be provided to remote areas and disaster areas, and realizes flexible responses according to the emotional state of the user. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1476] Step 1:

[1477] Information gathering

[1478] The drones will be flown to designated remote locations or disaster areas.

[1479] Input: Drone cameras and sensors collect on-site image and environmental data.

[1480] Data processing: The drone compresses the collected data in real time and transmits it to a server via communication infrastructure.

[1481] Output: Local image and environmental data sent to the server.

[1482] Specific operation: The drone photographs the damage to buildings and the gathering of people in the disaster area.

[1483] Step 2:

[1484] Data reception and analysis

[1485] The server receives the data sent from the drone in real time.

[1486] Input: Image and environmental data transmitted from the drone.

[1487] Data calculation: The server inputs the received data into the generative AI and analyzes the local situation.

[1488] Output: The analysis results in information about which areas need the most support.

[1489] Specific operation: The server analyzes the degree of damage in the affected area from image data and identifies areas that need assistance.

[1490] Step 3:

[1491] Deciding how to support

[1492] The server inputs the analysis results into the generative AI.

[1493] Input: Information about the area where you need assistance as a result of your analysis.

[1494] Data calculation: Generative AI learns the optimal support method based on past data and determines the support needed.

[1495] Output: Information on what type and how much of the supplies to transport as a means of support.

[1496] How it works: Generative AI identifies areas with shortages of food or medicine and selects appropriate supplies.

[1497] Step 4:

[1498] Drone instructions

[1499] Based on the results of the generative AI's judgment, the server issues specific transport instructions to the drone.

[1500] Input: Instructions for transporting supplies as a method of support.

[1501] Data processing: The server converts the instructions into a format that the drone can understand.

[1502] Output: Delivery instructions to drone.

[1503] Specific operation: The server commands the drone to deliver food to a specific location.

[1504] Step 5:

[1505] Material transportation

[1506] The drone follows instructions from the server, loads the specified supplies, and transports them to the specified location.

[1507] Input: Transport instructions from the server.

[1508] Data processing: The drone calculates the flight path and loads supplies based on instructions.

[1509] Output: Delivery of supplies to a specified location.

[1510] Specific operation: Drones deliver food to evacuation centers in disaster areas.

[1511] Step 6:

[1512] Flight control by emotion engine

[1513] The emotion engine analyzes the user's emotional state.

[1514] Input: User facial and voice data.

[1515] Data calculation: The emotion engine analyzes the user's emotional state and reflects the results in the drone's flight control.

[1516] Output: Drone flight control instructions according to the user's emotional state.

[1517] Specific behavior: If the user feels surprised, the drone will rapidly increase in altitude. If the user feels safe, the drone will move slowly.

[1518] (Application example 3)

[1519] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1520] Conventional drone technology and generative AI systems have had issues with being unable to provide fast and effective support to remote areas and disaster-stricken areas. They also lack the ability to respond flexibly to the user's emotional state and lack the accuracy required for anomaly detection. This has led to problems that reduce the efficiency and safety of support activities.

[1521] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a transportation means using drone technology, an information processing means using generative AI, a learning means based on the evolution of the generative AI, a flight control means using an emotion engine to respond to the user's emotional state, and anomaly detection means. This enables fast and effective support to remote areas and disaster areas, and enables flexible responses according to the user's emotional state and highly accurate anomaly detection.

[1522] "Drone technology" refers to the use of unmanned aerial vehicles for transportation, transporting supplies, and gathering information.

[1523] "Generative AI" is an artificial intelligence technology that generates and analyzes information based on collected data.

[1524] "The evolution of generative AI" refers to the evolutionary process of artificial intelligence that learns from past experiences and data and derives the most optimal support methods.

[1525] An "emotion engine" is a technology that detects the user's emotional state and adjusts the system's behavior accordingly.

[1526] "Flight control means" refers to means for controlling the flight pattern, altitude, and speed of a drone.

[1527] "Anomaly detection means" is a means for analyzing collected data and video to detect anomalies.

[1528] A system for implementing this invention includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a flight control means that responds to the user's emotional state using an emotion engine, and an anomaly detection means.

[1529] The server uses image analysis libraries such as OpenCV to analyze the video data acquired from the drone. This allows it to detect specific colors and shapes and identify abnormalities. The generative AI also uses emotion recognition engines such as DeepFace to detect the user's emotional state in real time.

[1530] The device uses a drone control library, such as the DJI SDK, to control the drone's flight, allowing it to adjust its flight pattern, altitude, and speed based on the user's emotional state.

[1531] For example, if a drone detects a red object (such as a sign of a fire) while patrolling, the server will determine the abnormality and issue an alert. Also, if the user is surprised, the terminal will rapidly increase the drone's altitude to attract the user's attention.

[1532] An example prompt is, "Create a program that will issue an alert if the drone detects an abnormality while patrolling. Also, add a function that will adjust the drone's flight pattern according to the user's emotional state."

[1533] In this way, it becomes possible to provide fast and effective support to remote areas and disaster areas, and to realize flexible responses according to the user's emotional state and highly accurate anomaly detection.

[1534] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1535] Step 1:

[1536] The server receives video data from the drone. The input is real-time video data sent from the drone, and the output is the video frames to be analyzed. Specifically, the server captures the video streamed from the drone's camera and saves it frame by frame.

[1537] Step 2:

[1538] The server analyzes the acquired video data using OpenCV. The input is the video frame acquired in step 1, and the output is the result of the anomaly detection. Specifically, the server converts the video frame into the HSV color space and performs masking to detect specific colors (e.g., red). If the specific color is detected above a certain level as a result of the masking process, it is determined to be an anomaly.

[1539] Step 3:

[1540] The server detects the user's emotional state using an emotion recognition engine such as DeepFace. The input is the user's facial image captured by the drone, and the output is the user's emotional state (e.g., surprise, relief). Specifically, the server uses a face detection algorithm to identify the user's face and classifies the emotion using the emotion recognition engine.

[1541] Step 4:

[1542] The server generates flight control instructions for the drone based on the anomaly detection results and the user's emotional state. The input is the output of Step 2 and Step 3, and the output is a control instruction for the drone. Specifically, the server issues an alert if an anomaly is detected, and generates an instruction to rapidly increase the drone's altitude if the user feels surprised.

[1543] Step 5:

[1544] The device receives control instructions from the server and controls the drone using the DJI SDK. The input is the control instruction sent from the server, and the output is a change in the drone's flight pattern. Specifically, the device adjusts the drone's altitude and speed according to the instructions from the server.

[1545] Step 6:

[1546] The user checks the drone's status and the results of anomaly detection through the device. The input is a notification from the device, and the output is the user's recognition. Specifically, the user checks the drone's video and anomaly detection alerts on the device screen, and gives additional instructions as necessary.

[1547] The above processing steps enable rapid and effective support to be provided to remote areas and disaster areas.

[1548] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1549] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generative AI models. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1550] Another example of generative AI is Gemini (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1551] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1552] [Third embodiment]

[1553] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1554] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1555] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1556] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1557] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1558] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1559] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1560] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1561] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1562] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1563] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1564] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1565] "Example 1"

[1566] An example of this invention is a system that combines a means of transportation using drone technology, an information processing means using generative AI, and a learning means based on the evolution of generative AI. Specifically, the drone uses GPS, sensors, etc. to fly autonomously, traveling to a destination and transporting supplies. The generative AI generates and analyzes the necessary information based on user instructions and the situation. The learning means based on the evolution of generative AI learns optimal behavior based on the user's behavior patterns and the situation, and acts based on that. By combining these means, it is possible to solve issues such as supporting the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[1567] "Example 2"

[1568] A concrete example is a system that provides support for the daily lives of the elderly. Drones assist the elderly with mobility and deliver necessary supplies. Generative AI understands the health status and lifestyle habits of the elderly and generates the necessary information. The learning methods that result from the evolution of generative AI learn the behavioral patterns of the elderly and provide optimal support based on that. Combining these methods can improve the quality of daily life for the elderly.

[1569] "Example 3"

[1570] Another possible system is to provide effective support to remote and disaster-stricken areas. Drones will transport supplies and collect information to remote and disaster-stricken areas. Generative AI will determine the support needed based on the collected information, and the learning method that evolves through generative AI will learn the optimal support method from past experience. By combining these methods, it will be possible to provide rapid and effective support to remote and disaster-stricken areas.

[1571] The processing flow of each embodiment will be described below.

[1572] "Example 1"

[1573] Step 1: The drone begins autonomous flight using GPS, sensors, etc.

[1574] Step 2: Transport to the destination and deliver supplies.

[1575] Step 3: The generative AI generates and analyzes the necessary information based on user instructions and the situation.

[1576] Step 4: Generative AI will evolve to learn optimal behavior based on the user's behavioral patterns and circumstances, and then act accordingly.

[1577] "Example 2"

[1578] Step 1: Drones will assist elderly people with mobility assistance and deliver necessary supplies.

[1579] Step 2: The generative AI understands the health status and lifestyle habits of the elderly and generates the necessary information.

[1580] Step 3: The learning means of generative AI evolves to learn the behavioral patterns of the elderly and provide optimal support based on that.

[1581] "Example 3"

[1582] Step 1: Drones will transport supplies and gather information in remote areas and disaster zones.

[1583] Step 2: The generative AI determines the necessary support based on the collected information.

[1584] Step 3: The learning method used by the generative AI evolves to learn the optimal support method from past experience.

[1585] Example 1

[1586] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1587] There are many challenges, such as providing support for the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, protecting the environment, and preventing natural disasters. Conventional technologies have not been able to adequately address these challenges. In particular, there is a need for learning and executing optimal actions according to the user's behavioral patterns and situations.

[1588] The identification processing by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for receiving instructions from a user, generating information using a generative AI model, and providing the user with the analysis results, means for learning optimal behavior based on the user's behavioral patterns and situation and taking action based on the learned behavior, means for transportation using drone technology, information processing means using generative AI, and learning means through the evolution of generative AI. This enables daily life support for the elderly and people with physical limitations, effective support for remote areas and disaster areas, environmental protection, and natural disaster prevention.

[1589] "Drone technology" refers to the use of unmanned aerial vehicles to fly autonomously and perform functions such as transporting goods and conducting surveillance.

[1590] "Generative AI" is a type of artificial intelligence that generates and analyzes necessary information based on user instructions and the situation.

[1591] A "generative AI model" is a specific implementation of generative AI, such as an algorithm that generates text and data using a natural language processing model.

[1592] "Learning means" refers to technology that learns optimal behavior based on the user's behavioral patterns and circumstances, and then acts based on that.

[1593] "Information processing means" refers to technology for generating and analyzing information using generative AI.

[1594] The "means for receiving instructions from the user" refers to an interface or communication means by which the system receives instructions input by the user.

[1595] "Means for providing analysis results to users" refers to technology for displaying or notifying the generated information and analysis results to users.

[1596] "Behavioral patterns" refer to the tendencies of the actions and choices that a user makes on a daily basis.

[1597] "Optimal action" refers to the action that is most suitable for the user's situation and purpose.

[1598] "Transportation" refers to technology that uses drone technology to transport goods and people to their destinations.

[1599] "Problem-solving means" are technologies and methods for solving specific problems, such as supporting the daily lives of the elderly and people with physical limitations, providing support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[1600] This invention is a system that combines drone technology, generative AI, and learning methods enabled by the evolution of generative AI, and its aims are to support the daily lives of the elderly and people with physical limitations, provide effective support to remote areas and disaster-stricken areas, protect the environment, and prevent natural disasters.

[1601] Server Roles

[1602] The server receives instructions from the user, generates information using a generative AI model, and provides the analysis results to the user. Specifically, the server uses a generative AI model (e.g., GPT-4) implemented using Python. The server inputs the user's prompt into the generative AI model and analyzes the generated information. The analysis results are also used as data to learn optimal actions based on the user's behavioral patterns and circumstances.

[1603] Device Role

[1604] The terminal provides an interface for inputting instructions from the user. For example, a smartphone or tablet can be used as the terminal. When the user inputs "Tell me the best route to my next destination" into the terminal, the terminal sends that information to the server. The generated information returned from the server is then displayed to the user.

[1605] User Roles

[1606] The user issues instructions to the system via a terminal. For example, they might input, "I would like to request the delivery of supplies to a disaster area." Based on the user's instructions, the drone will fly autonomously and deliver the supplies to the specified destination. The user can then decide their next action based on the information provided by the generative AI.

[1607] Specific examples

[1608] Example 1: Assisting the elderly in their daily lives

[1609] The elderly person types "Confirm my next hospital appointment" into the device. The device sends the information to the server, which uses generative AI to generate and analyze the appointment information. The analysis results are returned to the device, allowing the elderly person to check the date and time of their next hospital appointment.

[1610] Example 2: Support for disaster-stricken areas

[1611] The user inputs into the device, "I would like to request the delivery of supplies to the disaster area." The device sends this information to the server, which then uses generative AI to generate the optimal route and delivery method. The drone then flies autonomously based on this information and delivers the supplies to the specified destination.

[1612] Prompt Sentence Examples

[1613] 1. "What's the best route to my next destination?"

[1614] 2. "I would like to request the transportation of supplies to disaster areas."

[1615] 3. "Check my next doctor's appointment."

[1616] By inputting these prompts into a generative AI model, users can obtain the information they need.

[1617] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1618] Step 1: User input

[1619] A user inputs instructions into the system through a terminal. For example, the user might input, "Tell me the best route to my next destination." The input instructions are sent to the server through the terminal interface. The input data is in text format and specifically describes the user's request.

[1620] Step 2: Send instructions to the server

[1621] The terminal sends instructions received from the user to the server. Specifically, it sends the user's prompt to the server using an HTTP request. The input is the user's instruction text, and the output is the request sent to the server.

[1622] Step 3: Information generation using generative AI models

[1623] The server generates the necessary information using a generative AI model (e.g., GPT-4) based on the received instructions. Specifically, it runs a Python script and inputs the user's prompt into the generative AI model. The input is the user's instruction text, and the output is the generated information text.

[1624] Step 4: Analyze the generated information

[1625] The server analyzes the information returned by the generative AI model. For example, it analyzes the generated route information and identifies the optimal route. The input is the generated information text, and the output is the analysis result.

[1626] Step 5: Submitting the analysis results

[1627] The server sends the analysis results to the terminal. Specifically, it returns the analysis results to the terminal using an HTTP response. The input is the analysis results, and the output is the response sent to the terminal.

[1628] Step 6: Receiving the analysis results

[1629] The terminal receives the analysis result returned from the server. The input is the response from the server, and the output is the reception of the analysis result.

[1630] Step 7: Viewing the analysis results

[1631] The terminal displays the received analysis results to the user. Specifically, optimal route information is displayed on the terminal screen. The input is the analysis results, and the output is the information displayed to the user.

[1632] Step 8: Decide on next steps

[1633] The user checks the analysis results displayed on the device and decides on the next course of action, for example, heading to the destination by following the displayed route. The input is the analysis results, and the output is the user's decision on the course of action.

[1634] (Application example 1)

[1635] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1636] Conventional food delivery systems have had issues with delivery efficiency and user convenience. In particular, delivery route optimization and learning of user behavior patterns have not been adequately performed, resulting in delivery delays and low user satisfaction. Support for the elderly and people with physical limitations has also been insufficient, and providing prompt support to remote areas and disaster-stricken areas has also been an issue.

[1637] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1638] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a means for analyzing the user's order details and the delivery destination situation and calculating the optimal route, and a means for learning the user's behavioral patterns and preferences to enable smoother future orders. This improves delivery efficiency, enhances user convenience, and enables support for the elderly and people with physical limitations, as well as rapid support in remote areas and disaster-stricken areas.

[1639] "Drone technology" refers to the use of unmanned aerial vehicles to fly autonomously and perform functions such as transporting goods and conducting surveillance.

[1640] "Transportation" refers to a method or device for transporting objects or people from one point to another.

[1641] "Generative AI" is an artificial intelligence technology that generates and analyzes necessary information based on user instructions and the situation.

[1642] "Information processing means" refers to methods and devices for collecting, analyzing, and generating data.

[1643] "Learning methods through the evolution of generative AI" refers to artificial intelligence technology that learns optimal behavior based on the user's behavior patterns and circumstances, and then acts based on that.

[1644] "Means for analyzing the contents of a user's order and the status of the delivery destination and calculating the optimal route" refers to a method or device for analyzing the contents of a user's order and information on the delivery destination and calculating the most efficient delivery route.

[1645] "Means for learning user behavioral patterns and preferences to enable smoother ordering in the future" refers to a method or device for learning a user's past behavior and preferences and optimizing the ordering process in the future.

[1646] "Daily life support for the elderly and people with physical limitations" refers to providing the support necessary for the elderly and people with physical limitations to carry out their daily lives.

[1647] "Effective support to remote and disaster-stricken areas" refers to providing prompt and effective support to remote and disaster-stricken areas.

[1648] "Environmental protection and natural disaster prevention" refers to activities and measures to protect the environment and prevent the occurrence of natural disasters.

[1649] The system for implementing this invention combines drone technology, generative AI, and learning means based on the evolution of generative AI. Specific embodiments of this system are described below.

[1650] System Configuration

[1651] The system consists of the following main components:

[1652] 1. Drones: Equipped with GPS and sensors, they fly autonomously.

[1653] 2. Server: Executes the generative AI and the learning method that evolves the generative AI.

[1654] 3. User device: The user places the order on a device such as a smartphone.

[1655] Program processing

[1656] Hardware

[1657] Drone: GPS, sensors

[1658] Server: High-performance processor, memory

[1659] User device: Smartphone

[1660] software

[1661] Generative AI: Python, TensorFlow

[1662] Learning AI: Python, Scikit-learn

[1663] Data processing and calculation

[1664] 1. Order analysis: When a user places an order on their smartphone, the server uses generative AI to analyze the order and calculate the optimal route.

[1665] 2. Behavioral learning: The server uses generative AI to learn the user's behavioral patterns and preferences, making future orders smoother.

[1666] 3. Autonomous flight: The drones use GPS and sensors to fly autonomously and deliver meals to designated locations.

[1667] Specific examples

[1668] Consider a scenario where a user orders a pizza via smartphone and has it delivered to their home. Once the user confirms the order, the server uses generative AI to analyze the order and calculate the optimal delivery route. Next, the server uses generative AI to learn the user's past order history and behavioral patterns, allowing for smoother future orders. The drone then autonomously flies from the warehouse to the user's home to deliver the pizza.

[1669] Prompt Sentence Examples

[1670] "Build a system where a user can order a pizza on their smartphone and a drone will deliver it to their home. Use generative AI to analyze the order and calculate the optimal route. Also, use the advanced learning methods of generative AI to learn the user's behavioral patterns and preferences, making future orders smoother."

[1671] In this way, the system can solve problems such as providing daily support to the elderly and people with physical limitations, providing effective support to remote and disaster-stricken areas, and protecting the environment and preventing natural disasters.

[1672] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1673] Step 1:

[1674] The user places an order on their smartphone. The user opens the smartphone application, selects the meal they want to order, and enters the delivery address. The input data includes the order details and delivery address information.

[1675] Step 2:

[1676] The server receives the order details and analyzes them using generative AI. The server receives the order details and delivery destination information from the user as input, and calculates the optimal delivery route using a generative AI model. The output is the optimal delivery route information.

[1677] Step 3:

[1678] The server uses generative AI to learn the user's behavioral patterns and preferences. The server receives past order history and user behavior data as input, and uses a generative AI model to learn the user's behavioral patterns and preferences. The output is a learned user profile.

[1679] Step 4:

[1680] The server sends the optimal delivery route to the drone. The server then sends the calculated optimal delivery route information to the drone. The drone then begins autonomous flight based on the received delivery route information. The input is the optimal delivery route information, and the output is flight instructions for the drone.

[1681] Step 5:

[1682] The drone flies autonomously and delivers meals to the designated destination. The drone flies autonomously using GPS and sensors to head to the designated destination. The input is the drone's flight instructions, and the output is a delivery completion notification.

[1683] Step 6:

[1684] The server notifies the user that the delivery is complete. When the drone completes the delivery, the server sends a delivery completion notification to the user's smartphone. The input is the delivery completion notification, and the output is a notification to the user.

[1685] In this way, the system can efficiently carry out the entire process from the user's order to the completion of delivery.

[1686] Example 2

[1687] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1688] Issues such as providing support for the daily lives of the elderly and people with physical limitations, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters have not been adequately resolved with existing technologies. In particular, there is a need for systems that can effectively support elderly people by integrating multiple technologies, such as understanding their health status, learning their behavioral patterns, and transporting supplies using drones.

[1689] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1690] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, a means for collecting health data and behavioral data, a means for calculating drone flight routes and transporting supplies, a means for analyzing health conditions and lifestyle habits using a generative AI model and generating information, and a means for learning behavioral patterns using a machine learning algorithm and providing optimal support. This improves the quality of daily life for the elderly and people with physical limitations and enables rapid and effective support in remote areas and disaster areas.

[1691] "Drone technology" refers to the use of unmanned aerial vehicles to transport goods and assist with transportation.

[1692] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate information.

[1693] "The evolution of generative AI" is the process by which artificial intelligence improves its performance through learning.

[1694] "Health data" refers to data that indicates an individual's health condition, such as heart rate, blood pressure, and number of steps taken.

[1695] "Behavioral data" refers to data that indicates an individual's daily behavior, such as meal times and walking times.

[1696] A "flight route" is the route a drone will take to reach its destination.

[1697] A "generative AI model" is a specific implementation of generative AI, and includes algorithms and programs for data analysis and information generation.

[1698] A "machine learning algorithm" is a computational method for learning patterns based on data and making predictions and classifications.

[1699] "Optimal support" refers to support and services that are provided in a manner that best suits the needs of each individual user.

[1700] This invention is a system for solving problems such as providing daily support for the elderly and physically limited people, providing effective support to remote areas and disaster-stricken areas, and protecting the environment and preventing natural disasters. This system includes drone technology, generative AI, the evolution of generative AI, collection of health and behavioral data, calculation of drone flight routes, data analysis using generative AI models, and learning behavioral patterns using machine learning algorithms to provide optimal support.

[1701] 1. Program Generation

[1702] The server generates the system's program, which includes drone control, generative AI data processing, and learning of elderly people's behavioral patterns.

[1703] 2. Program processing explanation

[1704] The server performs the following processing using the generated program.

[1705] Drone Control

[1706] The server uses drone control software (e.g., DJI SDK) to assist elderly people with their mobility and deliver necessary supplies. Specifically, the server analyzes GPS data and calculates the drone's flight route. The drone then travels along that route and delivers the supplies to the elderly.

[1707] Data processing using generative AI

[1708] The server uses a generative AI model (e.g., OpenAI GPT-4) to understand the health status and lifestyle habits of the elderly. Specifically, the server collects the elderly's health data (e.g., heart rate, blood pressure) and inputs it into the generative AI model. The generative AI model analyzes this data and generates the necessary information (e.g., health advice).

[1709] Learning behavioral patterns

[1710] The server uses a machine learning algorithm (e.g., TensorFlow) to learn the behavioral patterns of the elderly. Specifically, the server collects data on the elderly's daily behavior (e.g., meal times, walking time) and inputs it into a machine learning model. The model uses this data to learn the elderly's behavioral patterns and provide optimal support.

[1711] 3. Examples and prompts

[1712] As a concrete example, the following scenario can be considered.

[1713] Example 1: Drone delivery of goods

[1714] Users (elderly people) use a smartphone app to order necessary supplies (e.g., medicine, food). The server receives the order information and uses drone control software to deliver the supplies to the elderly person.

[1715] Example 2: Understanding your health status

[1716] Users (elderly people) wear a wearable device to collect health data. The server inputs the collected data into a generative AI model and generates health advice.

[1717] Prompt Sentence Examples

[1718] "How long does a 70-year-old man take for a walk each day?"

[1719] What should elderly people do if they have high blood pressure?

[1720] Combining these measures can improve the quality of everyday life for older people.

[1721] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1722] Step 1:

[1723] Data collection

[1724] Users wear a wearable device to collect health data (e.g., heart rate, blood pressure, number of steps). The device (wearable device) measures this data in real time and transmits it to a server via Bluetooth or Wi-Fi.

[1725] Input: User health data

[1726] Data processing: The terminal formats the measurement data and converts it into a form that can be transmitted.

[1727] Output: Health data sent to the server

[1728] Step 2:

[1729] Drone control and cargo transportation

[1730] Users use a smartphone app to order necessary supplies (e.g., medicine, food). The server receives the order information and uses drone control software to deliver the supplies to the elderly person. The server analyzes GPS data and calculates the drone's flight route. The drone then travels along that route and delivers the supplies to the user.

[1731] Input: User order information, GPS data

[1732] Data processing: The server analyzes the order information and calculates the drone's flight route.

[1733] Output: Drone flight route, cargo transportation

[1734] Step 3:

[1735] Data analysis and information generation using generative AI

[1736] The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze the user's health status and lifestyle habits and generate the necessary information. The server inputs the collected health data into the generative AI model, which then analyzes this data and generates health advice and lifestyle improvement suggestions.

[1737] Input: Health data

[1738] Data processing: The generative AI model analyzes health data and generates the necessary information.

[1739] Output: Health advice, lifestyle improvement suggestions

[1740] Step 4:

[1741] Learning behavioral patterns and providing optimal support

[1742] The server uses a machine learning algorithm (e.g., TensorFlow) to learn the user's behavioral patterns and provide optimal support. The server collects the user's daily behavioral data (e.g., meal times, walking time) and inputs it into a machine learning model. The model uses this data to learn the user's behavioral patterns and provide optimal support.

[1743] Input: Daily behavior data

[1744] Data processing: Machine learning models analyze behavioral data and learn behavioral patterns.

[1745] Output: Optimal assistance (e.g., reminders, health advice)

[1746] Through these steps, the quality of daily life of older people can be improved.

[1747] (Application example 2)

[1748] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1749] In order to support the daily lives of the elderly and people with physical limitations, there is a lack of means to monitor their health status, provide appropriate meal plans, and efficiently deliver those meals. Similar support is also needed in remote areas and disaster areas, but the current system does not adequately address these issues.

[1750] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, a means for monitoring health data, a means for generating meal plans, a means for setting a delivery schedule by drone, and a means for optimizing the delivery schedule based on behavioral patterns. This enables efficient and effective support for the daily lives of elderly people and people with physical limitations, and also enables similar support in remote areas and disaster areas.

[1751] "Drone technology" is a technology that uses unmanned aerial vehicles to move objects, monitor them, collect data, and more.

[1752] "Generative AI" is a type of artificial intelligence, a technology that generates new information and content based on data.

[1753] A "learning method" is the method or process by which generative AI analyzes data and learns patterns and trends.

[1754] "Health data" refers to various physiological data that indicate an individual's health status, such as blood pressure, blood sugar level, and heart rate.

[1755] "Monitoring" is the act of continuously observing and recording specific data or conditions.

[1756] A "meal plan" is a suggested meal plan and schedule based on an individual's health status and preferences.

[1757] A "delivery schedule" is a plan or timetable for delivering goods or services at a specific time and place.

[1758] "Behavioral patterns" are data or information that show tendencies in an individual's behavior and habits.

[1759] "Optimization" is the process of deriving the most effective methods and results for specific purposes and conditions.

[1760] A system for carrying out this invention is configured as follows: A server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of the generative AI, a means for monitoring health data, a means for generating meal plans, a means for setting a delivery schedule by drone, and a means for optimizing the delivery schedule based on behavioral patterns.

[1761] Hardware and Software Configuration

[1762] Hardware:

[1763] Smartphone: A device that allows a user to operate applications.

[1764] Drone: An unmanned aerial vehicle used to move or deliver goods.

[1765] Sensors for health data monitoring: Devices for obtaining health data such as blood pressure and blood sugar levels.

[1766] software:

[1767] Python: A programming language used to implement programs.

[1768] Sensor API for Health Data Monitoring: A software interface for acquiring health data.

[1769] Drone control API: A software interface for controlling drone flight and setting delivery schedules.

[1770] Data processing and calculation

[1771] 1. Health Data Monitoring:

[1772] The server uses a sensor API to obtain health data (blood pressure, blood sugar levels, etc.) of the elderly.

[1773] The acquired data is stored on the server and used for analysis by generative AI.

[1774] 2. Generate meal plans:

[1775] The server uses generative AI to generate an optimal meal plan based on the acquired health data and user profile.

[1776] The generated meal plan is sent to the user's smartphone.

[1777] 3. Schedule your delivery:

[1778] The server uses a drone control API to schedule delivery of the generated meal plan at the specified time.

[1779] Delivery schedules are kept on a server and instructions are sent to the drones.

[1780] 4. Schedule optimization:

[1781] The server learns the behavioral patterns of the elderly and optimizes delivery schedules.

[1782] The optimized schedule is maintained on a server and instructions are sent to the drones.

[1783] Specific examples

[1784] If an elderly user likes Japanese food and has high blood pressure, the server operates as follows.

[1785] 1. A health data monitoring sensor measures the user's blood pressure and transmits the data to a server.

[1786] 2. The server uses generative AI to suggest low-salt Japanese meals based on the user's health data and dietary preferences.

[1787] 3. The server uses the drone control API to schedule delivery of the suggested meal to the user's home in one hour.

[1788] 4. The server learns the user's behavioral patterns and optimizes the delivery schedule.

[1789] Prompt Sentence Examples

[1790] User profile:

[1791] Name: Elderly User

[1792] Age: 75

[1793] Food preferences: Japanese, Western, Chinese

[1794] Health Data:

[1795] Blood pressure: 135

[1796] Blood sugar level: 90

[1797] Generated meal plan:

[1798] Low-salt Japanese food

[1799] Delivery Schedule:

[1800] Delivery in 1 hour

[1801] In this way, a system will be realized that efficiently and effectively supports the daily lives of elderly people and people with physical limitations.

[1802] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1803] Step 1:

[1804] The server uses a sensor API for health data monitoring to obtain the user's health data (blood pressure, blood glucose level, etc.). The input is physiological data from the sensor, and the output is health data stored in the server. Specifically, the sensor is worn on the user's body, periodically collects data, and sends it to the server.

[1805] Step 2:

[1806] The server analyzes the acquired health data and uses generative AI to evaluate the user's health condition. The input is the health data acquired in step 1, and the output is the health condition evaluation result. Specifically, the server runs a data analysis algorithm to detect abnormalities in blood pressure and blood sugar levels.

[1807] Step 3:

[1808] The server uses generative AI to generate an optimal meal plan based on the user profile (food preferences, allergy information, etc.) and the health assessment results. The input is the user profile and the health assessment results, and the output is the generated meal plan. Specifically, the server inputs a prompt statement into the generative AI model and generates the meal plan.

[1809] Step 4:

[1810] The server notifies the user of the generated meal plan on their smartphone. The input is the generated meal plan, and the output is a notification displayed on the user's smartphone. Specifically, the server calls the notification API and sends the contents of the meal plan to the user.

[1811] Step 5:

[1812] The server uses the drone control API to schedule delivery of the generated meal plan at the specified time. The input is the generated meal plan and delivery time, and the output is the drone delivery schedule. Specifically, the server calls the drone control API to set the delivery schedule.

[1813] Step 6:

[1814] The server learns the user's behavioral patterns and optimizes the delivery schedule. The input is the user's behavioral data and delivery schedule, and the output is the optimized delivery schedule. Specifically, the server executes a behavioral pattern analysis algorithm and calculates the optimal delivery time.

[1815] Step 7:

[1816] The server sends the optimized delivery schedule to the drone and executes the meal delivery. The input is the optimized delivery schedule, and the output is the meal delivered to the user's home. Specifically, the server sends instructions to the drone, and the drone delivers the meal to the user's home at the specified time.

[1817] Example 3

[1818] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1819] To provide prompt and effective support in remote areas and disaster-stricken areas, it is necessary to accurately grasp the local situation and quickly determine the appropriate support method. However, with conventional methods, it takes time to collect and analyze information, which often results in delays in support. Similar issues exist in providing support for the daily lives of the elderly and people with physical limitations, as well as in environmental protection and natural disaster prevention.

[1820] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1821] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means through the evolution of generative AI, an information gathering means using drones, a means for inputting the gathered data into a generative AI model, a data analysis means using the generative AI model, a means for proposing a support method based on the analysis results, and a means for transporting and delivering support supplies, thereby enabling fast and effective support in remote areas and disaster areas.

[1822] "Drone technology" is a technology that uses unmanned aerial vehicles to travel to specific locations such as remote areas or disaster areas, and transport supplies and collect information.

[1823] "Transportation means" means a means for using a drone to travel to a designated location.

[1824] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[1825] "Information processing means" refers to means for analyzing collected data and generating necessary information.

[1826] "Learning methods through the evolution of generative AI" refers to a method by which generative AI learns based on past data and experience and proposes more effective support methods.

[1827] "Information collection means" refers to the means of collecting local images and environmental data using drones.

[1828] "Means for inputting collected data into a generative AI model" refers to the means for inputting data collected by a drone into a generative AI model and performing analysis.

[1829] "Means for data analysis using generative AI models" refers to means for analyzing collected data using generative AI models to understand the local situation.

[1830] "Means for proposing support methods based on analysis results" refers to means for proposing optimal support methods based on the analysis results of a generative AI model.

[1831] "Means for transporting and implementing relief supplies" refers to the means for using drones to transport the proposed relief supplies and implement relief on site.

[1832] MODE FOR CARRYING OUT THE INVENTION

[1833] The present invention is a system for realizing rapid and effective support for remote areas and disaster areas. A specific embodiment of this system will be described below.

[1834] 1. Program Generation

[1835] The server generates programs for providing support to remote areas and disaster-stricken areas, including drone operation, information gathering, data analysis using generative AI, and support decision-making.

[1836] 2. Program processing explanation

[1837] The server dispatches drones to remote areas or disaster zones to deliver supplies and gather local information. The drones are equipped with cameras and sensors to collect video and environmental data in real time. The collected data is then sent to the server.

[1838] The server inputs the received data into a generative AI model, which determines the optimal method of assistance based on past disaster response data and assistance track record. This generative AI model uses, for example, OpenAI's GPT-4 or Google's BERT.

[1839] The generative AI model analyzes the collected information and suggests necessary relief supplies and methods. For example, if a shortage of food or medicine is identified, it will instruct the delivery of these supplies as a priority. It will also suggest evacuation routes and determine the need for medical support depending on the situation in the affected area.

[1840] 3. Examples of concrete examples and prompts

[1841] As a concrete example, consider the case where a flood occurs in a certain area. A drone takes aerial photographs of the flood damage and collects information on the location of victims and the status of evacuation routes. The collected data is sent to a server and analyzed by a generative AI model.

[1842] Example prompts for generative AI models:

[1843] "Analyze the damage situation in flood-affected areas and propose the necessary relief supplies and methods of support. The collected data will include the location information of victims, the status of evacuation routes, and the shortage of supplies."

[1844] Based on this prompt, the generative AI model proposes the optimal method of assistance, and the server sends the instructions to the drone, which then transports the proposed relief supplies and delivers them to the disaster area, thereby achieving fast and effective assistance.

[1845] This system combines drone technology and generative AI technology to efficiently provide support to remote areas and disaster areas. Furthermore, by using a learning method based on the evolution of the generative AI model, it is possible to learn the optimal support method from past experience and provide more effective support. The flow of the specific processing in Example 3 is explained using FIG. 15.

[1846] Program processing flow

[1847] Step 1: Dispatch drones and gather information

[1848] The server issues instructions to dispatch drones to remote locations or disaster areas. The drones are equipped with cameras and sensors and collect local video and environmental data. Specifically, the drones use GPS to fly along designated routes and capture video of the disaster area with their cameras. They also use temperature and humidity sensors to collect environmental data. The input is the dispatch instruction from the server, and the output is the collected video data and environmental data.

[1849] Step 2: Sending and Receiving Data

[1850] The drone transmits the collected data to a server in real time. The server stores the received data and prepares it for analysis. Specifically, the video data captured by the drone and the environmental data acquired from the sensors are transmitted to the server via wireless communication. The input is the data collected from the drone, and the output is the data stored on the server.

[1851] Step 3: Data analysis using generative AI models

[1852] The server inputs the received data into a generative AI model. The generative AI model analyzes the optimal method of support based on past disaster response data and support track record. Specifically, the generative AI model identifies the extent of flood damage and determines evacuation routes for victims and necessary support supplies. The input is the collected data stored on the server, and the output is the analysis results.

[1853] Step 4: Propose support methods

[1854] The generative AI model proposes assistance methods based on the analysis results. The server checks the proposed assistance methods and generates specific instructions. Specifically, if the generative AI model detects a shortage of food or medicine, the server issues instructions to prioritize the transportation of these supplies. The input is the analysis results of the generative AI model, and the output is specific assistance instructions.

[1855] Step 5: Delivery and implementation of relief supplies

[1856] The server issues instructions to the drone to transport relief supplies. The drone loads the specified supplies and flies toward the disaster area. After arriving at the disaster area, the drone drops off the supplies and provides the necessary support. Specifically, the drone delivers medicines to the disaster area and hands them over to local medical staff. The input is support instructions from the server, and the output is the delivery of supplies to the disaster area and the implementation of support.

[1857] This series of processes enables rapid and effective support to remote areas and disaster-stricken areas.

[1858] (Application example 3)

[1859] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1860] In order to provide prompt and effective support to remote areas and disaster-stricken areas, it is necessary to accurately grasp the local situation and quickly determine the optimal support method. However, with conventional methods, it takes time to gather information and determine the support method, which often results in delays in support. Optimizing disaster support has also been an issue for logistics centers.

[1861] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1862] In this invention, the server includes a means of transportation using drone technology, an information processing means using generative AI, a learning means based on the evolution of generative AI, a remote control means using a smartphone, a data analysis means using a cloud server, and a means for optimizing disaster relief at a logistics center, thereby enabling fast and effective support to remote areas and disaster-stricken areas.

[1863] "Drone technology" refers to the use of unmanned aerial vehicles to transport supplies and gather information.

[1864] "Generative AI" is an artificial intelligence technology that generates and analyzes information based on collected data.

[1865] "Learning through the evolution of generative AI" is a learning method for artificial intelligence that evolves by learning the optimal support methods based on past data and experience.

[1866] "Remote control using a smartphone" is a technology that allows you to control a drone in a remote location using a smartphone.

[1867] "Data analysis using cloud servers" is a technology that analyzes data collected using servers on the cloud.

[1868] "Optimization of disaster relief at logistics centers" refers to technology that determines the optimal method of transporting relief supplies and providing support in the event of a disaster at a logistics center.

[1869] ...

Claims

1. Transportation using drone technology, An information processing method using generative AI; A learning means by evolution of the generative AI; A means for receiving an instruction from a user, generating information using the generative AI model, and providing an analysis result to the user; A means for learning optimal actions based on the user's behavioral patterns and circumstances, and taking action based on that learning; A system including a problem solving means for solving a user's problem using the transportation means.

2. The system of claim 1 , wherein the means for moving includes means for performing flight control of a drone.

3. The system according to claim 1 , wherein the information processing means includes means for generating and analyzing information using a generative AI.

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