system

Abstract

The system according to the embodiment aims to prevent pollen from scattering before it actually happens. [Solution] A system according to an embodiment includes an identification unit, a countermeasure planning unit, and a control unit. The identification unit analyzes the type and location information of plants to identify plants that emit large amounts of pollen. The countermeasure planning unit plans appropriate countermeasures based on the location information of the plants identified by the identification unit and the pollen dispersion situation. The control unit automatically controls the drone based on the countermeasures planned by the countermeasure planning unit.

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] Conventional techniques have been limited to symptomatic treatments for hay fever, and have had the problem of making it difficult to fundamentally prevent pollen from scattering.

[0005] The system according to the embodiment aims to prevent pollen from scattering before it actually happens. [Means for solving the problem]

[0006] The system according to the embodiment includes an identification unit, a countermeasure planning unit, and a control unit. The identification unit analyzes the type and location information of plants and identifies plants that emit large amounts of pollen. The countermeasure planning unit plans appropriate countermeasures based on the location information of the plants identified by the identification unit and the pollen dispersion situation. The control unit automatically controls the drone based on the countermeasures planned by the countermeasure planning unit. [Effects of the Invention]

[0007] The system according to the embodiment can prevent pollen from scattering before it actually happens. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The pollen-prevention drone system of this embodiment of the present invention utilizes AI to help people suffering from hay fever. This system identifies plants that emit large amounts of pollen, then uses AI to develop optimal countermeasures before pollen dispersals occur, automatically controlling the drone to prevent pollen dispersal. While conventional hay fever countermeasures have focused on symptomatic treatment, this system offers a solution that fundamentally prevents pollen dispersal. For urban businesses and municipalities, this system not only reduces pollen dispersal but also improves labor productivity and reduces medical costs. It aims to improve the quality of life for all people suffering from hay fever. For example, AI can identify plants that emit large amounts of pollen. To do this, AI analyzes plant types and location information to identify plants that emit large amounts of pollen. For example, if a specific tree or flower emits a large amount of pollen, it can identify that plant. Next, AI develops optimal countermeasures. Based on the location information of the identified plants and the pollen dispersal situation, AI develops optimal countermeasures. For example, it is possible to plan methods to spray chemicals to prevent pollen dispersion using drones or to physically prevent pollen dispersion. Based on the countermeasures proposed by the AI, the drone is automatically controlled to prevent pollen dispersion before it occurs. The drone then flies automatically according to the countermeasures proposed by the AI ​​and performs tasks to prevent pollen dispersion. For example, a drone can approach specific plants and spray chemicals to prevent pollen dispersion. This system reduces the amount of pollen dispersion, contributing to improved labor productivity and reduced medical costs. For example, reducing the number of employees suffering from hay fever can improve a company's labor productivity. It can also reduce medical costs associated with hay fever. Furthermore, this system can be provided to urban companies and municipalities, aiming to improve the quality of life of all people suffering from hay fever. For example, introducing this system to urban parks and street trees can significantly reduce pollen dispersion and improve the living environment of residents. This allows the pollen prevention drone system to prevent pollen from spreading before it actually happens.

[0029] A pollen dispersion prevention drone system according to an embodiment includes an identification unit, a countermeasure planning unit, and a control unit. The identification unit analyzes the type and location information of plants and identifies plants that emit large amounts of pollen. For example, the identification unit uses a generation AI to analyze the type and location information of plants and identify plants that emit large amounts of pollen. The identification unit can also predict and identify periods when plants will emit large amounts of pollen, taking into account the growth cycle of the plants. Furthermore, when identifying plants, the identification unit can also evaluate the risk of pollen dispersion by referring to weather data. For example, the generation AI acquires real-time weather data and evaluates the risk of pollen dispersion. The countermeasure planning unit plans appropriate countermeasures based on the location information and pollen dispersion status of the plants identified by the identification unit. For example, the countermeasure planning unit plans appropriate countermeasures based on the location information and pollen dispersion status of the plants identified using the generation AI. Furthermore, when planning countermeasures, the countermeasure planning unit can also plan different countermeasures for each type of plant. Furthermore, when planning countermeasures, the countermeasure planning unit can plan an appropriate flight route taking into account the remaining battery power of the drone. The control unit automatically controls the drone based on the countermeasures planned by the countermeasure planning unit. The control unit, for example, automatically controls the drone using a generative AI to prevent pollen from scattering. The control unit can also automatically control the drone to approach specific plants and spray pesticides. The control unit can also automatically control the drone to physically prevent pollen from scattering. In this way, the pollen scattering prevention drone system according to the embodiment can prevent pollen from scattering.

[0030] The identification unit can use the generation AI to analyze the type and location information of plants and identify plants that emit a lot of pollen. For example, the identification unit can use the generation AI to analyze the type and location information of plants and identify plants that emit a lot of pollen. The generation AI analyzes the type and location information of plants using technologies such as deep learning and natural language processing. For example, the generation AI can analyze image data of plants and identify plants that emit a lot of pollen. The generation AI can also analyze location information of plants and identify plants that emit a lot of pollen. As a result, the use of the generation AI improves the accuracy of plant identification.

[0031] The countermeasure planning unit can plan appropriate countermeasures based on the location information of plants identified using the generation AI and the pollen dispersion status. The countermeasure planning unit plans appropriate countermeasures based on, for example, the location information of plants identified using the generation AI and the pollen dispersion status. The generation AI analyzes the location information of identified plants and the pollen dispersion status using technologies such as deep learning and natural language processing. For example, the generation AI plans a method for spraying chemicals to prevent pollen dispersion based on the location information of identified plants. The generation AI can also plan a method for physically preventing pollen dispersion based on the pollen dispersion status of identified plants. As a result, the use of the generation AI improves the accuracy of countermeasures.

[0032] The control unit can use the generative AI to automatically control the drone and prevent pollen from spreading. For example, the control unit can use the generative AI to automatically control the drone and prevent pollen from spreading. The generative AI uses technologies such as deep learning and natural language processing to plan and automatically control the drone's flight route. For example, the generative AI can plan the drone's flight route, approach specific plants, and spray pesticides. The generative AI can also plan the drone's flight route and physically prevent pollen from spreading. As a result, using the generative AI improves the drone's control accuracy, making it possible to effectively prevent pollen from spreading.

[0033] The control unit can automatically control the drone to approach specific plants and spray pesticides. The control unit, for example, automatically controls the drone to approach specific plants and spray pesticides. The pesticides use specific chemical substances and their concentrations. For example, the control unit can automatically control the drone to approach specific plants and spray pesticides containing specific chemical substances. The control unit can also automatically control the drone to approach specific plants and spray pesticides at specific concentrations. In this way, using a drone to spray pesticides can effectively prevent pollen from spreading.

[0034] The control unit can automatically control the drone to physically prevent pollen from scattering. The control unit, for example, can automatically control the drone to physically prevent pollen from scattering. Physical prevention of pollen scattering includes installing nets and suctioning pollen. For example, the control unit can automatically control the drone to install nets to prevent pollen from scattering. The control unit can also automatically control the drone to suction pollen to prevent pollen from scattering. This improves the effectiveness of hay fever countermeasures by using drones to physically prevent pollen from scattering.

[0035] The identification unit can predict and identify the period when a lot of pollen will be released, taking into account the growth cycle of the plant. For example, the identification unit predicts and identifies the period when a lot of pollen will be released, taking into account the growth cycle of the plant. The generation AI analyzes plant growth data and past weather data to predict the peak period of pollen release. For example, the generation AI analyzes plant growth data and predicts the peak period of pollen release. The generation AI can also refer to past weather data to identify the period of pollen release. Furthermore, the generation AI can predict the period of pollen release, taking into account the growth cycles that differ for each type of plant. In this way, by taking into account the growth cycle of the plant, it is possible to accurately predict the peak period of pollen release and take measures.

[0036] The identification unit can evaluate the risk of pollen dispersion by referring to weather data when identifying a plant. For example, the identification unit evaluates the risk of pollen dispersion by referring to weather data when identifying a plant. The generation AI analyzes real-time weather data and past weather data to evaluate the risk of pollen dispersion. For example, the generation AI obtains real-time weather data and evaluates the risk of pollen dispersion. The generation AI can also analyze past weather data to predict the risk of pollen dispersion. Furthermore, the generation AI can combine weather data with plant location information to evaluate the risk of pollen dispersion. In this way, by referring to weather data, the risk of pollen dispersion can be accurately evaluated and appropriate measures can be taken.

[0037] The identification unit can improve the accuracy of identification by taking geographical environmental data into consideration when identifying plants. For example, the identification unit improves the accuracy of identification by taking geographical environmental data into consideration when identifying plants. The generation AI analyzes geographical environmental data and topographical data to improve the accuracy of identifying plants. For example, the generation AI obtains geographical environmental data and improves the accuracy of identifying plants. The generation AI can also refer to topographical data to improve the accuracy of identifying plants. Furthermore, the generation AI can also improve the accuracy of identifying plants by taking land use data into consideration. In this way, the accuracy of identifying plants is improved by taking geographical environmental data into consideration.

[0038] The identification unit can improve the accuracy of identification by referring to past pollen dispersal data when identifying a plant. For example, the identification unit improves the accuracy of identification by referring to past pollen dispersal data when identifying a plant. The generation AI analyzes past pollen dispersal data and dispersal patterns to improve the accuracy of identifying a plant. For example, the generation AI analyzes past pollen dispersal data to improve the accuracy of identification. The generation AI can also improve the accuracy of identification by referring to past pollen dispersal patterns. Furthermore, the generation AI can improve the accuracy of identification by taking into account past pollen dispersal data. In this way, the accuracy of identifying a plant is improved by referring to past pollen dispersal data.

[0039] The countermeasure planning unit can plan different countermeasures for each type of plant when planning countermeasures. For example, the countermeasure planning unit plans different countermeasures for each type of plant when planning countermeasures. The generation AI proposes different countermeasures for each type of plant. For example, the generation AI may propose spraying pesticides on specific trees. The generation AI may also propose installing a physical barrier for specific flowers and plants. Furthermore, the generation AI may propose pruning or removal for specific plants. This allows for more effective countermeasures by planning different countermeasures for each type of plant.

[0040] The countermeasure planning unit can plan an appropriate flight route taking into account the remaining battery level of the drone when planning countermeasures. For example, the countermeasure planning unit plans an appropriate flight route taking into account the remaining battery level of the drone when planning countermeasures. The generation AI monitors the remaining battery level of the drone and plans the optimal flight route. For example, the generation AI monitors the remaining battery level of the drone and plans the optimal flight route. The generation AI can also suggest an efficient flight route based on the remaining battery level. Furthermore, the generation AI can take into account the remaining battery level and set charging points along the way. This makes it possible to plan an efficient flight route by taking into account the remaining battery level of the drone.

[0041] The countermeasure planning unit can determine the priority of countermeasures by taking into account the population density of the area when planning countermeasures. For example, the countermeasure planning unit determines the priority of countermeasures by taking into account the population density of the area when planning countermeasures. The generation AI analyzes the population density data of the area and determines the priority of countermeasures. For example, the generation AI can plan countermeasures by prioritizing areas with high population density. The generation AI can also plan countermeasures by putting areas with low population density on the back burner. Furthermore, the generation AI can maximize the effectiveness of countermeasures based on population density. In this way, the effectiveness of countermeasures can be maximized by taking into account the population density of the area.

[0042] The countermeasure planning unit can improve the accuracy of countermeasures by referring to past countermeasure effectiveness data when planning countermeasures. For example, the countermeasure planning unit improves the accuracy of countermeasures by referring to past countermeasure effectiveness data when planning countermeasures. The generation AI analyzes past countermeasure effectiveness data and proposes optimal countermeasures. For example, the generation AI analyzes past countermeasure effectiveness data and proposes optimal countermeasures. The generation AI can also refer to past countermeasure effectiveness and prioritize effective countermeasures. Furthermore, the generation AI can improve the accuracy of countermeasures based on past data. In this way, the accuracy of countermeasures is improved by referring to past countermeasure effectiveness data.

[0043] The control unit can add a function to automatically avoid obstacles when the drone is flying. For example, the control unit can add a function to automatically avoid obstacles when the drone is flying. The generation AI detects obstacles in real time and changes the drone's flight route. For example, the generation AI can detect obstacles in real time and change the drone's flight route. The generation AI can also obtain location information of obstacles and optimize the drone's flight route. Furthermore, the generation AI can use an algorithm for obstacle avoidance to control the drone's flight. This enables the drone to fly safely by automatically avoiding obstacles.

[0044] The control unit can measure pollen concentrations in real time while the drone is flying and optimize the flight route. For example, the control unit can measure pollen concentrations in real time while the drone is flying and optimize the flight route. The generation AI can measure pollen concentrations in real time and change the flight route. For example, the generation AI can measure pollen concentrations in real time and change the flight route. The generation AI can also plan the optimal flight route based on pollen concentration data. Furthermore, the generation AI can optimize the flight route to avoid areas with high pollen concentrations. This makes it possible to plan the optimal flight route by measuring pollen concentrations in real time.

[0045] The control unit can add a function to coordinate the control of multiple drones when flying. For example, the control unit can add a function to coordinate the control of multiple drones when flying. The generation AI acquires the position information of multiple drones and plans a flight route in coordination. For example, the generation AI acquires the position information of multiple drones and plans a flight route in coordination. The generation AI can also monitor the remaining battery levels of multiple drones and efficiently divide up work. Furthermore, the generation AI can adjust the flight speed of multiple drones and work cooperatively. This enables efficient work by controlling multiple drones in coordination.

[0046] The control unit can add a function to store flight route data in the cloud when the drone is flying and share it with other drones. For example, the control unit can add a function to store flight route data in the cloud when the drone is flying and share it with other drones. The generation AI stores flight route data in the cloud and shares it with other drones. For example, the generation AI stores flight route data in the cloud and shares it with other drones. The generation AI can also plan an optimal flight route based on the data on the cloud. Furthermore, the generation AI can refer to flight route data of other drones and suggest efficient flight routes. As a result, efficient flight route planning becomes possible by storing flight route data in the cloud and sharing it with other drones.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When analyzing the type and location information of a plant, the identification unit can improve the accuracy of identification by taking geographical environmental data into consideration. For example, the identification unit analyzes geographical environmental data and topographical data to improve the accuracy of identifying a plant. The identification unit can also improve the accuracy of identifying a plant by taking land use data into consideration. Furthermore, the identification unit can predict the growth pattern of a plant based on the geographical environmental data to improve the accuracy of identifying a plant. In this way, the accuracy of identifying a plant is improved by taking geographical environmental data into consideration.

[0049] When formulating countermeasures, the countermeasure planning unit can determine the priority of countermeasures by taking into account the population density of the area. For example, the countermeasure planning unit analyzes population density data of the area and determines the priority of countermeasures. The countermeasure planning unit can also plan countermeasures by prioritizing areas with high population density. Furthermore, the countermeasure planning unit can also plan countermeasures by putting areas with low population density on the back burner. In this way, by taking into account the population density of the area, the effectiveness of the countermeasures can be maximized.

[0050] The control unit can add a function to automatically avoid obstacles during drone flight. For example, the control unit can detect obstacles in real time and change the drone's flight route. The control unit can also obtain location information of obstacles and optimize the drone's flight route. Furthermore, the control unit can use an algorithm to avoid obstacles and control the flight of the drone. This enables safe drone flight by automatically avoiding obstacles.

[0051] The control unit can measure pollen concentrations in real time while the drone is flying and optimize the flight route. For example, the control unit can measure pollen concentrations in real time and change the flight route. The control unit can also plan an optimal flight route based on the pollen concentration data. Furthermore, the control unit can optimize the flight route to avoid areas with high pollen concentrations. This makes it possible to plan an optimal flight route by measuring pollen concentrations in real time.

[0052] The control unit can add a function to coordinate the control of multiple drones during drone flight. For example, the control unit acquires the position information of multiple drones and coordinates them to plan flight routes. The control unit can also monitor the remaining battery levels of multiple drones and efficiently divide up tasks. Furthermore, the control unit can adjust the flight speed of multiple drones and coordinate tasks. This allows for efficient work by controlling multiple drones in a coordinated manner.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The identification unit analyzes the type of plant and its location information to identify plants that emit large amounts of pollen. The identification unit uses generation AI to analyze the type of plant and its location information to identify plants that emit large amounts of pollen. It can also predict and identify the period when a plant will emit a lot of pollen, taking into account the plant's growth cycle. It can also refer to weather data to evaluate the risk of pollen dispersion. Step 2: The countermeasure planning unit plans appropriate countermeasures based on the location information of the plants identified by the identification unit and the pollen dispersion status. The countermeasure planning unit plans appropriate countermeasures based on the location information of the plants identified using the generation AI and the pollen dispersion status. It can also plan different countermeasures for each type of plant. It can also plan an appropriate flight route taking into account the remaining battery power of the drone. Step 3: The control unit automatically controls the drone based on the countermeasures proposed by the countermeasures planning unit. The control unit automatically controls the drone using generative AI to prevent pollen from spreading. It can also approach specific plants and spray pesticides. It can also physically prevent pollen from spreading.

[0055] (Example 2) The pollen-prevention drone system of this embodiment of the present invention utilizes AI to help people suffering from hay fever. This system identifies plants that emit large amounts of pollen, then uses AI to develop optimal countermeasures before pollen dispersals occur, automatically controlling the drone to prevent pollen dispersal. While conventional hay fever countermeasures have focused on symptomatic treatment, this system offers a solution that fundamentally prevents pollen dispersal. For urban businesses and municipalities, this system not only reduces pollen dispersal but also improves labor productivity and reduces medical costs. It aims to improve the quality of life for all people suffering from hay fever. For example, AI can identify plants that emit large amounts of pollen. To do this, AI analyzes plant types and location information to identify plants that emit large amounts of pollen. For example, if a specific tree or flower emits a large amount of pollen, it can identify that plant. Next, AI develops optimal countermeasures. Based on the location information of the identified plants and the pollen dispersal situation, AI develops optimal countermeasures. For example, it is possible to plan methods to spray chemicals to prevent pollen dispersion using drones or to physically prevent pollen dispersion. Based on the countermeasures proposed by the AI, the drone is automatically controlled to prevent pollen dispersion before it occurs. The drone then flies automatically according to the countermeasures proposed by the AI ​​and performs tasks to prevent pollen dispersion. For example, a drone can approach specific plants and spray chemicals to prevent pollen dispersion. This system reduces the amount of pollen dispersion, contributing to improved labor productivity and reduced medical costs. For example, reducing the number of employees suffering from hay fever can improve a company's labor productivity. It can also reduce medical costs associated with hay fever. Furthermore, this system can be provided to urban companies and municipalities, aiming to improve the quality of life of all people suffering from hay fever. For example, introducing this system to urban parks and street trees can significantly reduce pollen dispersion and improve the living environment of residents. This allows the pollen prevention drone system to prevent pollen from spreading before it actually happens.

[0056] A pollen dispersion prevention drone system according to an embodiment includes an identification unit, a countermeasure planning unit, and a control unit. The identification unit analyzes the type and location information of plants and identifies plants that emit large amounts of pollen. For example, the identification unit uses a generation AI to analyze the type and location information of plants and identify plants that emit large amounts of pollen. The identification unit can also predict and identify periods when plants will emit large amounts of pollen, taking into account the growth cycle of the plants. Furthermore, when identifying plants, the identification unit can also evaluate the risk of pollen dispersion by referring to weather data. For example, the generation AI acquires real-time weather data and evaluates the risk of pollen dispersion. The countermeasure planning unit plans appropriate countermeasures based on the location information and pollen dispersion status of the plants identified by the identification unit. For example, the countermeasure planning unit plans appropriate countermeasures based on the location information and pollen dispersion status of the plants identified using the generation AI. Furthermore, when planning countermeasures, the countermeasure planning unit can also plan different countermeasures for each type of plant. Furthermore, when planning countermeasures, the countermeasure planning unit can plan an appropriate flight route taking into account the remaining battery power of the drone. The control unit automatically controls the drone based on the countermeasures planned by the countermeasure planning unit. The control unit, for example, automatically controls the drone using a generative AI to prevent pollen from scattering. The control unit can also automatically control the drone to approach specific plants and spray pesticides. The control unit can also automatically control the drone to physically prevent pollen from scattering. In this way, the pollen scattering prevention drone system according to the embodiment can prevent pollen from scattering.

[0057] The identification unit can use the generation AI to analyze the type and location information of plants and identify plants that emit a lot of pollen. For example, the identification unit can use the generation AI to analyze the type and location information of plants and identify plants that emit a lot of pollen. The generation AI analyzes the type and location information of plants using technologies such as deep learning and natural language processing. For example, the generation AI can analyze image data of plants and identify plants that emit a lot of pollen. The generation AI can also analyze location information of plants and identify plants that emit a lot of pollen. As a result, the use of the generation AI improves the accuracy of plant identification.

[0058] The countermeasure planning unit can plan appropriate countermeasures based on the location information of plants identified using the generation AI and the pollen dispersion status. The countermeasure planning unit plans appropriate countermeasures based on, for example, the location information of plants identified using the generation AI and the pollen dispersion status. The generation AI analyzes the location information of identified plants and the pollen dispersion status using technologies such as deep learning and natural language processing. For example, the generation AI plans a method for spraying chemicals to prevent pollen dispersion based on the location information of identified plants. The generation AI can also plan a method for physically preventing pollen dispersion based on the pollen dispersion status of identified plants. As a result, the use of the generation AI improves the accuracy of countermeasures.

[0059] The control unit can use the generative AI to automatically control the drone and prevent pollen from spreading. For example, the control unit can use the generative AI to automatically control the drone and prevent pollen from spreading. The generative AI uses technologies such as deep learning and natural language processing to plan and automatically control the drone's flight route. For example, the generative AI can plan the drone's flight route, approach specific plants, and spray pesticides. The generative AI can also plan the drone's flight route and physically prevent pollen from spreading. As a result, using the generative AI improves the drone's control accuracy, making it possible to effectively prevent pollen from spreading.

[0060] The control unit can automatically control the drone to approach specific plants and spray pesticides. The control unit, for example, automatically controls the drone to approach specific plants and spray pesticides. The pesticides use specific chemical substances and their concentrations. For example, the control unit can automatically control the drone to approach specific plants and spray pesticides containing specific chemical substances. The control unit can also automatically control the drone to approach specific plants and spray pesticides at specific concentrations. In this way, using a drone to spray pesticides can effectively prevent pollen from spreading.

[0061] The control unit can automatically control the drone to physically prevent pollen from scattering. The control unit, for example, can automatically control the drone to physically prevent pollen from scattering. Physical prevention of pollen scattering includes installing nets and suctioning pollen. For example, the control unit can automatically control the drone to install nets to prevent pollen from scattering. The control unit can also automatically control the drone to suction pollen to prevent pollen from scattering. This improves the effectiveness of hay fever countermeasures by using drones to physically prevent pollen from scattering.

[0062] The identification unit can estimate the user's emotions and adjust the accuracy of plant identification based on the estimated user emotions. The identification unit, for example, estimates the user's emotions and adjusts the accuracy of plant identification based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the identification unit causes the generation AI to increase the identification accuracy and reduce false positives. Furthermore, if the user is relaxed, the identification unit can cause the generation AI to return the identification accuracy to normal and prioritize processing speed. Furthermore, if the user is in a hurry, the identification unit can cause the generation AI to optimize the identification accuracy and quickly identify plants. This allows for more appropriate plant identification by adjusting the plant identification accuracy according to the user's emotions.

[0063] The identification unit can predict and identify the period when a lot of pollen will be released, taking into account the growth cycle of the plant. For example, the identification unit predicts and identifies the period when a lot of pollen will be released, taking into account the growth cycle of the plant. The generation AI analyzes plant growth data and past weather data to predict the peak period of pollen release. For example, the generation AI analyzes plant growth data and predicts the peak period of pollen release. The generation AI can also refer to past weather data to identify the period of pollen release. Furthermore, the generation AI can predict the period of pollen release, taking into account the growth cycles that differ for each type of plant. In this way, by taking into account the growth cycle of the plant, it is possible to accurately predict the peak period of pollen release and take measures.

[0064] The identification unit can evaluate the risk of pollen dispersion by referring to weather data when identifying a plant. For example, the identification unit evaluates the risk of pollen dispersion by referring to weather data when identifying a plant. The generation AI analyzes real-time weather data and past weather data to evaluate the risk of pollen dispersion. For example, the generation AI obtains real-time weather data and evaluates the risk of pollen dispersion. The generation AI can also analyze past weather data to predict the risk of pollen dispersion. Furthermore, the generation AI can combine weather data with plant location information to evaluate the risk of pollen dispersion. In this way, by referring to weather data, the risk of pollen dispersion can be accurately evaluated and appropriate measures can be taken.

[0065] The identification unit can estimate the user's emotions and determine the priority of plants to be identified based on the estimated user's emotions. The identification unit, for example, estimates the user's emotions and determines the priority of plants to be identified based on the estimated user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the identification unit can cause the generation AI to prioritize plants that emit a large amount of pollen. Also, if the user is relaxed, the identification unit can cause the generation AI to identify plants with normal priority. Furthermore, if the user is in a hurry, the identification unit can prioritize plants that the generation AI can quickly identify. This enables more effective plant identification by prioritizing plants according to the user's emotions.

[0066] The identification unit can improve the accuracy of identification by taking geographical environmental data into consideration when identifying plants. For example, the identification unit improves the accuracy of identification by taking geographical environmental data into consideration when identifying plants. The generation AI analyzes geographical environmental data and topographical data to improve the accuracy of identifying plants. For example, the generation AI obtains geographical environmental data and improves the accuracy of identifying plants. The generation AI can also refer to topographical data to improve the accuracy of identifying plants. Furthermore, the generation AI can also improve the accuracy of identifying plants by taking land use data into consideration. In this way, the accuracy of identifying plants is improved by taking geographical environmental data into consideration.

[0067] The identification unit can improve the accuracy of identification by referring to past pollen dispersal data when identifying a plant. For example, the identification unit improves the accuracy of identification by referring to past pollen dispersal data when identifying a plant. The generation AI analyzes past pollen dispersal data and dispersal patterns to improve the accuracy of identifying a plant. For example, the generation AI analyzes past pollen dispersal data to improve the accuracy of identification. The generation AI can also improve the accuracy of identification by referring to past pollen dispersal patterns. Furthermore, the generation AI can improve the accuracy of identification by taking into account past pollen dispersal data. In this way, the accuracy of identifying a plant is improved by referring to past pollen dispersal data.

[0068] The countermeasure planning unit can estimate the user's emotions and adjust the priority of countermeasures based on the estimated user emotions. The countermeasure planning unit, for example, estimates the user's emotions and adjusts the priority of countermeasures based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the countermeasure planning unit prioritizes the most effective countermeasure for the generation AI. In addition, if the user is relaxed, the countermeasure planning unit can also plan countermeasures with normal priority. Furthermore, if the user is in a hurry, the countermeasure planning unit can prioritize countermeasures that the generation AI can implement quickly. This enables more effective countermeasures by adjusting the priority of countermeasures according to the user's emotions.

[0069] The countermeasure planning unit can plan different countermeasures for each type of plant when planning countermeasures. For example, the countermeasure planning unit plans different countermeasures for each type of plant when planning countermeasures. The generation AI proposes different countermeasures for each type of plant. For example, the generation AI may propose spraying pesticides on specific trees. The generation AI may also propose installing a physical barrier for specific flowers and plants. Furthermore, the generation AI may propose pruning or removal for specific plants. This allows for more effective countermeasures by planning different countermeasures for each type of plant.

[0070] The countermeasure planning unit can plan an appropriate flight route taking into account the remaining battery level of the drone when planning countermeasures. For example, the countermeasure planning unit plans an appropriate flight route taking into account the remaining battery level of the drone when planning countermeasures. The generation AI monitors the remaining battery level of the drone and plans the optimal flight route. For example, the generation AI monitors the remaining battery level of the drone and plans the optimal flight route. The generation AI can also suggest an efficient flight route based on the remaining battery level. Furthermore, the generation AI can take into account the remaining battery level and set charging points along the way. This makes it possible to plan an efficient flight route by taking into account the remaining battery level of the drone.

[0071] The countermeasure planning unit can estimate the user's emotions and adjust the level of detail of the countermeasures based on the estimated user emotions. The countermeasure planning unit, for example, estimates the user's emotions and adjusts the level of detail of the countermeasures based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the countermeasure planning unit can have the generation AI suggest simple and effective countermeasures. In addition, if the user is relaxed, the countermeasure planning unit can have the generation AI suggest detailed countermeasures. Furthermore, if the user is in a hurry, the countermeasure planning unit can suggest countermeasures that can be implemented quickly. This enables more appropriate countermeasures to be taken by adjusting the level of detail of the countermeasures according to the user's emotions.

[0072] The countermeasure planning unit can determine the priority of countermeasures by taking into account the population density of the area when planning countermeasures. For example, the countermeasure planning unit determines the priority of countermeasures by taking into account the population density of the area when planning countermeasures. The generation AI analyzes the population density data of the area and determines the priority of countermeasures. For example, the generation AI can plan countermeasures by prioritizing areas with high population density. The generation AI can also plan countermeasures by putting areas with low population density on the back burner. Furthermore, the generation AI can maximize the effectiveness of countermeasures based on population density. In this way, the effectiveness of countermeasures can be maximized by taking into account the population density of the area.

[0073] The countermeasure planning unit can improve the accuracy of countermeasures by referring to past countermeasure effectiveness data when planning countermeasures. For example, the countermeasure planning unit improves the accuracy of countermeasures by referring to past countermeasure effectiveness data when planning countermeasures. The generation AI analyzes past countermeasure effectiveness data and proposes optimal countermeasures. For example, the generation AI analyzes past countermeasure effectiveness data and proposes optimal countermeasures. The generation AI can also refer to past countermeasure effectiveness and prioritize effective countermeasures. Furthermore, the generation AI can improve the accuracy of countermeasures based on past data. In this way, the accuracy of countermeasures is improved by referring to past countermeasure effectiveness data.

[0074] The control unit can estimate the user's emotions and adjust the drone's flight speed based on the estimated user's emotions. The control unit, for example, estimates the user's emotions and adjusts the drone's flight speed based on the estimated user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the control unit can have the generation AI slow down the drone's flight speed to ensure stable flight. The control unit can also have the generation AI control the drone at a normal flight speed if the user is relaxed. Furthermore, if the user is in a hurry, the control unit can have the generation AI increase the drone's flight speed to perform tasks quickly. This allows the drone's flight speed to be adjusted according to the user's emotions, enabling more appropriate flight.

[0075] The control unit can add a function to automatically avoid obstacles when the drone is flying. For example, the control unit can add a function to automatically avoid obstacles when the drone is flying. The generation AI detects obstacles in real time and changes the drone's flight route. For example, the generation AI can detect obstacles in real time and change the drone's flight route. The generation AI can also obtain location information of obstacles and optimize the drone's flight route. Furthermore, the generation AI can use an algorithm for obstacle avoidance to control the drone's flight. This enables the drone to fly safely by automatically avoiding obstacles.

[0076] The control unit can measure pollen concentrations in real time while the drone is flying and optimize the flight route. For example, the control unit can measure pollen concentrations in real time while the drone is flying and optimize the flight route. The generation AI can measure pollen concentrations in real time and change the flight route. For example, the generation AI can measure pollen concentrations in real time and change the flight route. The generation AI can also plan the optimal flight route based on pollen concentration data. Furthermore, the generation AI can optimize the flight route to avoid areas with high pollen concentrations. This makes it possible to plan the optimal flight route by measuring pollen concentrations in real time.

[0077] The control unit can estimate the user's emotions and adjust the drone's flight altitude based on the estimated user's emotions. The control unit, for example, estimates the user's emotions and adjusts the drone's flight altitude based on the estimated user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the control unit can have the generation AI lower the drone's flight altitude to ensure stable flight. The control unit can also have the generation AI control the drone at a normal flight altitude if the user is relaxed. Furthermore, if the user is in a hurry, the control unit can have the generation AI raise the drone's flight altitude to enable quicker work. This allows the drone's flight altitude to be adjusted according to the user's emotions, enabling more appropriate flight.

[0078] The control unit can add a function to coordinate the control of multiple drones when flying. For example, the control unit can add a function to coordinate the control of multiple drones when flying. The generation AI acquires the position information of multiple drones and plans a flight route in coordination. For example, the generation AI acquires the position information of multiple drones and plans a flight route in coordination. The generation AI can also monitor the remaining battery levels of multiple drones and efficiently divide up work. Furthermore, the generation AI can adjust the flight speed of multiple drones and work cooperatively. This enables efficient work by controlling multiple drones in coordination.

[0079] The control unit can add a function to store flight route data in the cloud when the drone is flying and share it with other drones. For example, the control unit can add a function to store flight route data in the cloud when the drone is flying and share it with other drones. The generation AI stores flight route data in the cloud and shares it with other drones. For example, the generation AI stores flight route data in the cloud and shares it with other drones. The generation AI can also plan an optimal flight route based on the data on the cloud. Furthermore, the generation AI can refer to flight route data of other drones and suggest efficient flight routes. As a result, efficient flight route planning becomes possible by storing flight route data in the cloud and sharing it with other drones. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned identification unit, countermeasure planning unit, and control unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the identification unit analyzes the type and location information of plants using the camera 42 and sensors of the smart device 14 and identifies plants that emit large amounts of pollen using a generation AI. The countermeasure planning unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and plans optimal countermeasures based on the location information of the identified plants and the pollen dispersion situation. The control unit is realized, for example, by the control unit 46A of the smart device 14 and automatically controls the drone based on the countermeasures planned by the generation AI to prevent pollen dispersion. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned identification unit, countermeasure planning unit, and control unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the identification unit analyzes the type and location information of plants using the camera 42 and sensors of the smart glasses 214 and identifies plants that emit large amounts of pollen using a generation AI. The countermeasure planning unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and plans optimal countermeasures based on the location information of the identified plants and the pollen dispersion situation. The control unit is realized, for example, by the control unit 46A of the smart glasses 214 and automatically controls the drone based on the countermeasures planned by the generation AI to prevent pollen dispersion. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned identification unit, countermeasure planning unit, and control unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the identification unit analyzes the type and location information of plants using the camera 42 and sensors of the headset terminal 314 and identifies plants that emit large amounts of pollen using a generation AI. The countermeasure planning unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and plans optimal countermeasures based on the location information of the identified plants and the pollen dispersion situation. The control unit is realized, for example, by the control unit 46A of the headset terminal 314 and automatically controls the drone based on the countermeasures planned by the generation AI to prevent pollen dispersion. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned identification unit, countermeasure planning unit, and control unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the identification unit analyzes the type and location information of plants using the camera 42 and sensors of the robot 414 and identifies plants that release large amounts of pollen using a generation AI. The countermeasure planning unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and plans optimal countermeasures based on the location information of the identified plants and the pollen dispersion situation. The control unit is realized, for example, by the control unit 46A of the robot 414 and automatically controls the drone based on the countermeasures planned by the generation AI to prevent pollen dispersion.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] When analyzing the type and location information of a plant, the identification unit can improve the accuracy of identification by taking geographical environmental data into consideration. For example, the identification unit analyzes geographical environmental data and topographical data to improve the accuracy of identifying a plant. The identification unit can also improve the accuracy of identifying a plant by taking land use data into consideration. Furthermore, the identification unit can predict the growth pattern of a plant based on the geographical environmental data to improve the accuracy of identifying a plant. In this way, the accuracy of identifying a plant is improved by taking geographical environmental data into consideration.

[0082] When formulating countermeasures, the countermeasure planning unit can determine the priority of countermeasures by taking into account the population density of the area. For example, the countermeasure planning unit analyzes population density data of the area and determines the priority of countermeasures. The countermeasure planning unit can also plan countermeasures by prioritizing areas with high population density. Furthermore, the countermeasure planning unit can also plan countermeasures by putting areas with low population density on the back burner. In this way, by taking into account the population density of the area, the effectiveness of the countermeasures can be maximized.

[0083] The control unit can add a function to automatically avoid obstacles during drone flight. For example, the control unit can detect obstacles in real time and change the drone's flight route. The control unit can also obtain location information of obstacles and optimize the drone's flight route. Furthermore, the control unit can use an algorithm to avoid obstacles and control the flight of the drone. This enables safe drone flight by automatically avoiding obstacles.

[0084] The control unit can measure pollen concentrations in real time while the drone is flying and optimize the flight route. For example, the control unit can measure pollen concentrations in real time and change the flight route. The control unit can also plan an optimal flight route based on the pollen concentration data. Furthermore, the control unit can optimize the flight route to avoid areas with high pollen concentrations. This makes it possible to plan an optimal flight route by measuring pollen concentrations in real time.

[0085] The control unit can add a function to coordinate the control of multiple drones during drone flight. For example, the control unit acquires the position information of multiple drones and coordinates them to plan flight routes. The control unit can also monitor the remaining battery levels of multiple drones and efficiently divide up tasks. Furthermore, the control unit can adjust the flight speed of multiple drones and coordinate tasks. This allows for efficient work by controlling multiple drones in a coordinated manner.

[0086] The identification unit can estimate the user's emotions and adjust the accuracy of plant identification based on the estimated user's emotions. For example, if the user is feeling stressed, the identification unit can have the generation AI increase the identification accuracy and reduce false positives. Also, if the user is relaxed, the identification unit can have the generation AI return the identification accuracy to normal and prioritize processing speed. Furthermore, if the user is in a hurry, the identification unit can have the generation AI optimize the identification accuracy and quickly identify plants. This allows for more appropriate plant identification by adjusting the plant identification accuracy according to the user's emotions.

[0087] The countermeasure planning unit can estimate the user's emotions and adjust the priority of countermeasures based on the estimated user's emotions. For example, if the user is feeling stressed, the countermeasure planning unit can prioritize the most effective countermeasures for the generation AI. Also, if the user is relaxed, the countermeasure planning unit can also plan countermeasures with normal priority. Furthermore, if the user is in a hurry, the countermeasure planning unit can also prioritize countermeasures that the generation AI can implement quickly. In this way, more effective countermeasures can be implemented by adjusting the priority of countermeasures according to the user's emotions.

[0088] The control unit can estimate the user's emotions and adjust the drone's flight speed based on the estimated user's emotions. For example, if the user is feeling stressed, the control unit can have the generation AI slow down the drone's flight speed to ensure stable flight. Alternatively, if the user is relaxed, the control unit can have the generation AI control the drone at a normal flight speed. Furthermore, if the user is in a hurry, the control unit can have the generation AI increase the drone's flight speed to perform tasks quickly. This allows for more appropriate flight by adjusting the drone's flight speed according to the user's emotions.

[0089] The control unit can estimate the user's emotions and adjust the drone's flight altitude based on the estimated user's emotions. For example, if the user is feeling stressed, the control unit can have the generation AI lower the drone's flight altitude to ensure stable flight. Alternatively, if the user is relaxed, the control unit can have the generation AI control the drone at a normal flight altitude. Furthermore, if the user is in a hurry, the control unit can have the generation AI raise the drone's flight altitude to perform the task quickly. This allows for more appropriate flight by adjusting the drone's flight altitude according to the user's emotions.

[0090] The identification unit can estimate the user's emotions and determine the priority of plants to be identified based on the estimated user's emotions. For example, if the user is feeling stressed, the identification unit causes the generation AI to prioritize identifying plants that emit a large amount of pollen. Also, if the user is relaxed, the identification unit can cause the generation AI to identify plants with normal priority. Furthermore, if the user is in a hurry, the identification unit can prioritize plants that the generation AI can quickly identify. This allows for more effective plant identification by prioritizing plants according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The identification unit analyzes the type of plant and its location information to identify plants that emit large amounts of pollen. The identification unit uses generation AI to analyze the type of plant and its location information to identify plants that emit large amounts of pollen. It can also predict and identify the period when a plant will emit a lot of pollen, taking into account the plant's growth cycle. It can also refer to weather data to evaluate the risk of pollen dispersion. Step 2: The countermeasure planning unit plans appropriate countermeasures based on the location information of the plants identified by the identification unit and the pollen dispersion status. The countermeasure planning unit plans appropriate countermeasures based on the location information of the plants identified using the generation AI and the pollen dispersion status. It can also plan different countermeasures for each type of plant. It can also plan an appropriate flight route taking into account the remaining battery power of the drone. Step 3: The control unit automatically controls the drone based on the countermeasures proposed by the countermeasures planning unit. The control unit automatically controls the drone using generative AI to prevent pollen from spreading. It can also approach specific plants and spray pesticides. It can also physically prevent pollen from spreading.

[0093] 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.

[0094] 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> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] 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.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system characterized by comprising: an identification unit that analyzes the type and location information of plants and identifies plants that emit large amounts of pollen; a countermeasure planning unit that plans appropriate countermeasures based on the location information of the plants identified by the identification unit and the pollen dispersion situation; and a control unit that automatically controls a drone based on the countermeasures planned by the countermeasure planning unit.

2. The identification unit Generative AI is used to analyze plant types and location information to identify plants that release large amounts of pollen. The system of claim 1 .

3. The system described in claim 1, characterized in that the countermeasure planning unit plans appropriate countermeasures based on the location information of plants identified using generation AI and the pollen dispersion situation.

4. The control unit Using generative AI to automatically control drones and prevent pollen from spreading The system of claim 1 .

5. The control unit Automatically control drones to approach specific plants and spray pesticides The system of claim 1 .

6. The control unit Drones are automatically controlled to physically prevent pollen from spreading. The system of claim 1 .

7. The identification unit Estimate the user's emotions and adjust the accuracy of plant identification based on the estimated user emotions. The system of claim 1 .

8. The identification unit Predict and identify the period when the most pollen is released, taking into account the plant's growth cycle The system of claim 1 .

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