System

Abstract

An object of the system according to the embodiment is to recognize an insect pest and generate and release a pheromone suitable for the insect pest.SOLUTION: A system includes a recognition unit, a generation unit, and a release unit. The recognition unit recognizes a pest. The generator generates the pheromone in accordance with the insect pest recognized by the recognizer. The releasing part releases the pheromone produced by the producing part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires the use of different pheromones for each pest, which poses the problem of requiring the time and effort of remaking pheromone traps for each target.

[0005] The system according to the embodiment aims to recognize pests and generate and release pheromones suitable for the pests. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a generation unit, and an emission unit. The recognition unit recognizes pest insects. The generation unit generates pheromones in accordance with the pest insects recognized by the recognition unit. The emission unit emits the pheromones generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can recognize pests and generate and release pheromones appropriate for the pests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A pest control system according to an embodiment of the present invention recognizes pests and generates and releases appropriate pheromones. The pest control system recognizes pests, generates pheromones preferred by the pests, and releases the pheromones into traps to attract and capture the pests. For example, the pest control system uses cameras to recognize pests. The cameras are installed around agricultural crops and monitor pest movements in real time. For example, when a camera detects a pest, image data is sent to the system. The system uses image analysis technology to identify the type of pest. The pest control system then generates a pheromone preferred by the recognized pest. The system retrieves a pheromone generation method for the type of pest from a database and generates the pheromone. For example, the system retrieves the chemical structure of a pheromone preferred by a specific pest from a database and generates a pheromone based on the chemical structure. The pest control system then releases the generated pheromone into a trap to attract the pests. For example, a pheromone-releasing device is installed in the trap to lure the pests into the trap. This allows the pest control system to reduce the risk of pesticide residues and eliminate the need to remake pheromone traps for each target. This allows the pest control system to effectively capture pests and reduce damage to crops. For example, farmers can save the effort of remaking pheromone traps for specific pests. In addition, reducing the risk of pesticide residues can protect consumer health. Furthermore, reducing damage to crops caused by pests can increase crop yields.

[0029] A pest control system according to an embodiment includes a recognition unit, a generation unit, and a release unit. The recognition unit recognizes pests. The recognition unit, for example, uses a camera to recognize pests and identifies the type of pest using image analysis technology. For example, when the camera detects a pest, the recognition unit transmits the image data to the system. The system identifies the type of pest using image analysis technology. The generation unit generates a pheromone according to the pest recognized by the recognition unit. For example, the generation unit obtains a pheromone generation method according to the type of pest from a database and generates the pheromone. For example, the generation unit obtains the chemical structure of a pheromone preferred by a specific pest from the database and generates the pheromone based on the chemical structure. The release unit releases the pheromone generated by the generation unit. For example, the release unit releases the generated pheromone into a trap to attract the pest. For example, the release unit installs a pheromone releaser in the trap to lure the pest into the trap. As a result, the pest control system according to the embodiment can effectively capture pests and reduce damage to agricultural crops. For example, farmers can save time and effort in remaking pheromone traps for specific pests. In addition, the health of consumers can be protected by reducing the risk of pesticide residues. Furthermore, by reducing damage to crops caused by pests, agricultural yields can be increased.

[0030] The recognition unit can recognize pests using a camera and identify the type of pest using image analysis technology. Examples of cameras include, but are not limited to, high-resolution cameras, infrared cameras, and night-time cameras. For example, the recognition unit can take detailed images of pests using a high-resolution camera and identify the type of pest using image analysis technology. The recognition unit can also recognize pests at night using an infrared camera. Furthermore, the recognition unit can recognize pests in dark places using a night-time camera. Examples of image analysis technology include, but are not limited to, machine learning algorithms, pattern recognition technology, and deep learning technology. For example, the recognition unit can identify the type of pest using a machine learning algorithm. Furthermore, the recognition unit can identify the type of pest using pattern recognition technology. Furthermore, the recognition unit can identify the type of pest using deep learning technology. Thus, the type of pest can be accurately identified by using a camera and image analysis technology. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input image data acquired by a camera into the generation AI and have the generation AI identify the type of pest.

[0031] The generation unit can obtain a pheromone generation method corresponding to the type of pest insect from a database and generate the pheromone. The database includes, for example, information on pheromones corresponding to the type of pest insect, the chemical structure of the pheromone, and a pheromone generation method, but is not limited to these examples. The generation unit can, for example, obtain the chemical structure of a pheromone preferred by a specific pest insect from the database and generate the pheromone based on the chemical structure. The generation unit can also obtain a pheromone generation method corresponding to the type of pest insect from the database and generate the pheromone based on the method. Furthermore, the generation unit can obtain materials and procedures necessary for pheromone generation from the database and generate the pheromone based on the materials and procedures. In this way, by using the database, it is possible to generate an optimal pheromone for the type of pest insect. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the chemical structure of the pheromone obtained from the database into the generation AI and cause the generation AI to generate the pheromone.

[0032] The release unit can release the generated pheromone into a trap to attract pests. Examples of traps include, but are not limited to, sticky traps, capture traps, and electric shock traps. For example, the release unit can release the generated pheromone into a sticky trap to trap pests by sticking them to the trap. The release unit can also release the generated pheromone into a capture trap to capture pests. Furthermore, the release unit can release the generated pheromone into an electric shock trap to capture pests by electric shock. In this way, pests can be effectively attracted by releasing the generated pheromone into the trap. Some or all of the above-described processing in the release unit can be performed using, or without, AI. For example, the release unit can input the timing and amount of the generated pheromone to be released into the trap into the generation AI, and cause the generation AI to release the pheromone.

[0033] The generation unit can obtain the chemical structure of a pheromone preferred by a specific pest insect from a database and generate a pheromone based on the chemical structure. Examples of chemical structures include, but are not limited to, molecular formulas, chemical formulas, and 3D structures. For example, the generation unit can obtain the molecular formula of a pheromone preferred by a specific pest insect from a database and generate a pheromone based on the molecular formula. The generation unit can also obtain the chemical formula of a pheromone preferred by a specific pest insect from a database and generate a pheromone based on the chemical formula. Furthermore, the generation unit can obtain the 3D structure of a pheromone preferred by a specific pest insect from a database and generate a pheromone based on the 3D structure. This generates a pheromone optimal for a specific pest insect, thereby enhancing the attraction effect. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the chemical structure of a pheromone obtained from the database into a generation AI and cause the generation AI to generate a pheromone.

[0034] The release unit can install a pheromone release device in the trap to attract pest insects to the trap. Examples of such devices include, but are not limited to, a pheromone release device, a pheromone dispenser, and a pheromone sprayer. The release unit can install a pheromone release device in the trap to release the generated pheromone. The release unit can also install a pheromone dispenser in the trap to release a fixed amount of pheromone. Furthermore, the release unit can install a pheromone sprayer in the trap to spray the pheromone. Thus, by installing the pheromone release device in the trap, pest insects can be effectively captured. Some or all of the above-described processing in the release unit can be performed using, or without, AI. For example, the release unit can input the operation of the pheromone release device into the generation AI and cause the generation AI to release pheromones.

[0035] The recognition unit can learn patterns of pest movement and predict pest appearance. For example, the recognition unit can learn and predict patterns of pest appearance during specific time periods based on past data. The recognition unit can also learn and predict seasonal patterns of pest movement. Furthermore, the recognition unit can predict pest appearance based on weather conditions and take measures. In this way, by learning pest movement patterns, it is possible to predict pest appearance and take measures. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input data on past pest movement into a generation AI and have the generation AI execute appearance prediction.

[0036] The recognition unit can analyze the size and shape of the pest in detail and identify the growth stage of the pest. For example, the recognition unit can analyze images captured by a camera, measure the size of the pest, and identify the growth stage. The recognition unit can also use shape analysis technology to analyze the shape of the pest in detail and identify the growth stage. Furthermore, the recognition unit can compare it with past data to identify the growth stage of the pest and take appropriate measures. In this way, by identifying the growth stage of the pest, appropriate measures can be taken. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input image data of the pest captured by a camera to the generation AI and have the generation AI identify the growth stage.

[0037] The recognition unit can analyze the behavioral patterns of pests and identify the time periods during which the pests are active. For example, the recognition unit can analyze video captured by a camera and identify the behavioral patterns of pests. The recognition unit can also predict the time periods during which pests are active based on past data. Furthermore, the recognition unit can monitor the behavior of pests in real time and identify the time periods during which they are active. This allows effective countermeasures to be taken by identifying the time periods during which pests are active. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input pest behavior data acquired by a camera into the generation AI and have the generation AI identify the time periods during which the pests are active.

[0038] The recognition unit can detect the sounds and vibrations of pests and identify the type of pest. The recognition unit can, for example, use a microphone to detect the sounds of pests and identify the type. The recognition unit can also use a vibration sensor to detect the movements of pests and identify the type. Furthermore, the recognition unit can combine sound and vibration data to more accurately identify the type of pest. This makes it possible to accurately identify the type of pest by detecting sound and vibration. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input sound data of pests acquired by a microphone to the generation AI and have the generation AI identify the type.

[0039] The recognition unit can detect the pheromone response of a pest and identify the type of pest. The recognition unit can detect the pest response using, for example, a pheromone sensor and identify the type. The recognition unit can also analyze the pheromone response data and identify the type of pest. Furthermore, the recognition unit can combine the pheromone response with other data to more accurately identify the type of pest. In this way, the type of pest can be accurately identified by detecting the pheromone response. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input pest response data obtained by a pheromone sensor into the generation AI and have the generation AI identify the type.

[0040] The recognition unit can analyze the flight patterns of pests and identify the type of pest. The recognition unit can, for example, analyze video captured by a camera and identify the flight patterns of pests. The recognition unit can also identify the type of pest based on flight pattern data. Furthermore, the recognition unit can combine the flight pattern with other data to more accurately identify the type of pest. In this way, the type of pest can be accurately identified by analyzing the flight pattern. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input pest flight data acquired by a camera into a generation AI and have the generation AI identify the type.

[0041] The generation unit can optimize the amount of pheromone produced based on the ecological information of the pest. The generation unit, for example, calculates the optimal amount of pheromone produced based on the ecological information of the pest. The generation unit can also adjust the amount of pheromone produced in accordance with the breeding cycle of the pest. Furthermore, the generation unit can optimize the amount of pheromone produced in accordance with the time period when the pest is active. This enables effective pest control by optimizing the amount of pheromone produced based on the ecological information of the pest. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the ecological information of the pest into the generation AI and cause the generation AI to optimize the amount of pheromone produced.

[0042] The generation unit can fine-tune the chemical structure of the pheromone according to the habitat of the pest. The generation unit fine-tunes the chemical structure of the pheromone according to, for example, the habitat of the pest. The generation unit can also optimize the chemical structure of the pheromone according to the type of pest. Furthermore, the generation unit can adjust the chemical structure of the pheromone according to the activity pattern of the pest. In this way, the attracting effect is enhanced by fine-tuning the chemical structure of the pheromone according to the habitat of the pest. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input habitat data of the pest into the generation AI and cause the generation AI to fine-tune the chemical structure of the pheromone.

[0043] The generation unit can adjust the timing of pheromone generation in accordance with the reproductive cycle of the pest insect. The generation unit adjusts the timing of pheromone generation in accordance with, for example, the reproductive cycle of the pest insect. The generation unit can also optimize the timing of pheromone generation in accordance with the pest insect's active time period. Furthermore, the generation unit can adjust the timing of pheromone generation based on the pest insect's ecological information. This enables effective pest control by adjusting the timing of pheromone generation in accordance with the pest insect's reproductive cycle. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input pest insect reproductive cycle data into the generation AI and cause the generation AI to adjust the timing of pheromone generation.

[0044] The generation unit generates pheromones of natural enemies of pest insects to suppress the behavior of the pest insects. The generation unit, for example, generates pheromones of natural enemies of pest insects to suppress the behavior of the pest insects. The generation unit can also suppress the propagation of pest insects using pheromones of natural enemies. Furthermore, the generation unit can combine pheromones of natural enemies to effectively suppress the behavior of pest insects. In this way, by generating pheromones of natural enemies of pest insects, the behavior of pest insects can be effectively suppressed. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate pheromones of natural enemies.

[0045] The generation unit can generate pest repellent pheromones to repel pests from a specific area. The generation unit can, for example, generate pest repellent pheromones to repel pests from a specific area. The generation unit can also use the repellent pheromones to prevent pests from invading. Furthermore, the generation unit can combine repellent pheromones to effectively repel pests. In this way, by generating repellent pheromones, pests can be repelled from a specific area. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can cause a generation AI to generate repellent pheromones.

[0046] The generation unit can combine multiple pest pheromones to attract pests more effectively. The generation unit, for example, combines multiple pheromones to attract pests effectively. The generation unit can also optimize the combination of pheromones to enhance the attracting effect. Furthermore, the generation unit can select the optimal combination of pheromones depending on the type of pest. In this way, by combining multiple pheromones, the attracting effect of pests can be enhanced. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a combination of multiple pheromones to the generation AI and cause the generation AI to optimize the attracting effect.

[0047] The emission unit can set a pheromone emission schedule in accordance with the pest's activity time period. The emission unit can set a pheromone emission schedule in accordance with the pest's activity time period, for example. The emission unit can also adjust the pheromone emission schedule in accordance with the pest's breeding cycle. Furthermore, the emission unit can optimize the pheromone emission schedule based on the pest's ecological information. This enables effective pest control by setting a pheromone emission schedule in accordance with the pest's activity time period. Some or all of the above-described processing in the emission unit can be performed using, or without, AI, for example. For example, the emission unit can input pest activity time period data into the generation AI and cause the generation AI to set the pheromone emission schedule.

[0048] The emission unit can adjust the amount of pheromone emitted according to the density of pest insects. For example, when the density of pest insects is high, the emission unit can increase the amount of pheromone emitted. Furthermore, when the density of pest insects is low, the emission unit can decrease the amount of pheromone emitted. Furthermore, the emission unit can optimize the amount of pheromone emitted according to the density of pest insects. This enables effective pest control by adjusting the amount of pheromone emitted according to the density of pest insects. Some or all of the above-mentioned processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can input pest density data to the generation AI and cause the generation AI to adjust the amount of pheromone emitted.

[0049] The releasing unit can analyze the movement patterns of the pest insects and identify the optimal release point. The releasing unit can, for example, analyze the movement patterns of the pest insects and identify the optimal pheromone release point. The releasing unit can also set the optimal release point according to the pest's active time period. Furthermore, the releasing unit can also identify the optimal release point based on the pest's ecological information. In this way, the optimal pheromone release point can be identified by analyzing the pest's movement patterns. Some or all of the above-mentioned processing in the releasing unit can be performed using AI, for example, or can be performed without using AI. For example, the releasing unit can input pest insect movement pattern data into the generating AI and cause the generating AI to identify the optimal release point.

[0050] The emission unit can emit pheromones of natural enemies of pest insects to suppress their behavior. The emission unit can, for example, emit pheromones of natural enemies of pest insects to suppress their behavior. The emission unit can also use pheromones of natural enemies to suppress the reproduction of pest insects. Furthermore, the emission unit can combine pheromones of natural enemies to effectively suppress the behavior of pest insects. In this way, by releasing pheromones of natural enemies of pest insects, the behavior of pest insects can be effectively suppressed. Some or all of the above-mentioned processing in the emission unit can be performed using AI, for example, or without AI. For example, the emission unit can cause the generation AI to execute the release of pheromones of natural enemies.

[0051] The emission unit can emit pest repellent pheromones to repel pests from a specific area. For example, the emission unit can emit pest repellent pheromones to repel pests from a specific area. The emission unit can also use repellent pheromones to prevent pests from entering. Furthermore, the emission unit can combine repellent pheromones to effectively repel pests. In this way, pests can be repelled from a specific area by emitting repellent pheromones. Some or all of the above-described processing in the emission unit can be performed using AI, or can be performed without using AI. For example, the emission unit can cause the generation AI to emit repellent pheromones.

[0052] The release unit can combine multiple pest pheromones to attract pests more effectively. For example, the release unit can combine multiple pheromones to attract pests effectively. The release unit can also optimize the combination of pheromones to enhance the attracting effect. Furthermore, the release unit can select the optimal combination of pheromones depending on the type of pest. In this way, by combining multiple pheromones, the attracting effect of pests can be enhanced. Some or all of the above-described processing in the release unit may be performed using, or without, AI. For example, the release unit can input the combination of multiple pheromones to the generation AI and cause the generation AI to optimize the attracting effect.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The recognition unit can analyze the behavioral patterns of pests and identify the time periods during which the pests are active. For example, it can analyze video captured by a camera and identify the behavioral patterns of pests. The recognition unit can also predict the time periods during which pests are active based on past data. Furthermore, the recognition unit can monitor the behavior of pests in real time and identify the time periods during which they are active. This allows effective countermeasures to be taken by identifying the time periods during which pests are active. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input pest behavior data acquired by a camera into a generation AI and have the generation AI identify the time periods during which the pests are active.

[0055] The generation unit can generate pheromones of natural enemies of pest insects to suppress the behavior of the pest insects. For example, the generation unit can generate pheromones of natural enemies of pest insects to suppress the behavior of the pest insects. The generation unit can also use pheromones of natural enemies to suppress the reproduction of pest insects. Furthermore, the generation unit can combine pheromones of natural enemies to effectively suppress the behavior of pest insects. In this way, by generating pheromones of natural enemies of pest insects, the behavior of pest insects can be effectively suppressed. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate pheromones of natural enemies.

[0056] The emission unit can emit pest repellent pheromones to repel pests from a specific area. For example, the emission unit can emit pest repellent pheromones to repel pests from a specific area. The emission unit can also use repellent pheromones to prevent pests from invading. Furthermore, the emission unit can combine repellent pheromones to effectively repel pests. In this way, pests can be repelled from a specific area by emitting repellent pheromones. Some or all of the above-described processing in the emission unit can be performed using AI, or can be performed without using AI. For example, the emission unit can cause the generation AI to emit repellent pheromones.

[0057] The generation unit can optimize the amount of pheromone produced based on the ecological information of the pest. For example, the generation unit calculates the optimal amount of pheromone produced based on the ecological information of the pest. The generation unit can also adjust the amount of pheromone produced in accordance with the pest's breeding cycle. Furthermore, the generation unit can optimize the amount of pheromone produced in accordance with the pest's activity period. This enables effective pest control by optimizing the amount of pheromone produced based on the ecological information of the pest. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the ecological information of the pest into the generation AI and cause the generation AI to optimize the amount of pheromone produced.

[0058] The recognition unit can detect the sounds and vibrations of pests and identify the type of pest. For example, the recognition unit can detect the sounds of pests using a microphone and identify the type. The recognition unit can also detect the movements of pests using a vibration sensor and identify the type. Furthermore, the recognition unit can combine sound and vibration data to more accurately identify the type of pest. This makes it possible to accurately identify the type of pest by detecting sound and vibration. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input sound data of pests acquired by a microphone to the generation AI and have the generation AI identify the type.

[0059] The generation unit can adjust the timing of pheromone generation in accordance with the reproductive cycle of the pest insect. For example, the generation unit adjusts the timing of pheromone generation in accordance with the reproductive cycle of the pest insect. The generation unit can also optimize the timing of pheromone generation in accordance with the pest insect's active time period. Furthermore, the generation unit can adjust the timing of pheromone generation based on the pest insect's ecological information. This enables effective pest control by adjusting the timing of pheromone generation in accordance with the pest insect's reproductive cycle. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input pest insect reproductive cycle data into the generation AI and cause the generation AI to adjust the timing of pheromone generation.

[0060] The emission unit can adjust the amount of pheromone emitted according to the density of pest insects. For example, when the density of pest insects is high, the emission unit can increase the amount of pheromone emitted. Also, when the density of pest insects is low, the emission unit can decrease the amount of pheromone emitted. Furthermore, the emission unit can optimize the amount of pheromone emitted according to the density of pest insects. This allows for effective pest control by adjusting the amount of pheromone emitted according to the density of pest insects. Some or all of the above-described processing in the emission unit may be performed using, or without, AI, for example. For example, the emission unit can input pest density data into the generation AI and cause the generation AI to adjust the amount of pheromone emitted.

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

[0062] Step 1: The recognition unit recognizes pests. The recognition unit, for example, uses a camera to recognize pests and identifies the type of pest using image analysis technology. When the camera detects a pest, the image data is sent to the system, and the system uses image analysis technology to identify the type of pest. Step 2: The generator generates a pheromone that matches the pest insect recognized by the recognizer. For example, the generator retrieves a pheromone generation method for each type of pest insect from a database, and generates a pheromone based on the chemical structure of a pheromone that the specific pest insect prefers. Step 3: The emission unit emits the pheromone generated by the generation unit. The emission unit, for example, emits the generated pheromone into a trap to attract pests. A device for emitting pheromones is installed in the trap so that pests are attracted to the trap.

[0063] (Example 2) A pest control system according to an embodiment of the present invention recognizes pests and generates and releases appropriate pheromones. The pest control system recognizes pests, generates pheromones preferred by the pests, and releases the pheromones into traps to attract and capture the pests. For example, the pest control system uses cameras to recognize pests. The cameras are installed around agricultural crops and monitor pest movements in real time. For example, when a camera detects a pest, image data is sent to the system. The system uses image analysis technology to identify the type of pest. The pest control system then generates a pheromone preferred by the recognized pest. The system retrieves a pheromone generation method for the type of pest from a database and generates the pheromone. For example, the system retrieves the chemical structure of a pheromone preferred by a specific pest from a database and generates a pheromone based on the chemical structure. The pest control system then releases the generated pheromone into a trap to attract the pests. For example, a pheromone-releasing device is installed in the trap to lure the pests into the trap. This allows the pest control system to reduce the risk of pesticide residues and eliminate the need to remake pheromone traps for each target. This allows the pest control system to effectively capture pests and reduce damage to crops. For example, farmers can save the effort of remaking pheromone traps for specific pests. In addition, reducing the risk of pesticide residues can protect consumer health. Furthermore, reducing damage to crops caused by pests can increase crop yields.

[0064] A pest control system according to an embodiment includes a recognition unit, a generation unit, and a release unit. The recognition unit recognizes pests. The recognition unit, for example, uses a camera to recognize pests and identifies the type of pest using image analysis technology. For example, when the camera detects a pest, the recognition unit transmits the image data to the system. The system identifies the type of pest using image analysis technology. The generation unit generates a pheromone according to the pest recognized by the recognition unit. For example, the generation unit obtains a pheromone generation method according to the type of pest from a database and generates the pheromone. For example, the generation unit obtains the chemical structure of a pheromone preferred by a specific pest from the database and generates the pheromone based on the chemical structure. The release unit releases the pheromone generated by the generation unit. For example, the release unit releases the generated pheromone into a trap to attract the pest. For example, the release unit installs a pheromone releaser in the trap to lure the pest into the trap. As a result, the pest control system according to the embodiment can effectively capture pests and reduce damage to agricultural crops. For example, farmers can save time and effort in remaking pheromone traps for specific pests. In addition, the health of consumers can be protected by reducing the risk of pesticide residues. Furthermore, by reducing damage to crops caused by pests, agricultural yields can be increased.

[0065] The recognition unit can recognize pests using a camera and identify the type of pest using image analysis technology. Examples of cameras include, but are not limited to, high-resolution cameras, infrared cameras, and night-time cameras. For example, the recognition unit can take detailed images of pests using a high-resolution camera and identify the type of pest using image analysis technology. The recognition unit can also recognize pests at night using an infrared camera. Furthermore, the recognition unit can recognize pests in dark places using a night-time camera. Examples of image analysis technology include, but are not limited to, machine learning algorithms, pattern recognition technology, and deep learning technology. For example, the recognition unit can identify the type of pest using a machine learning algorithm. Furthermore, the recognition unit can identify the type of pest using pattern recognition technology. Furthermore, the recognition unit can identify the type of pest using deep learning technology. Thus, the type of pest can be accurately identified by using a camera and image analysis technology. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input image data acquired by a camera into the generation AI and have the generation AI identify the type of pest.

[0066] The generation unit can obtain a pheromone generation method corresponding to the type of pest insect from a database and generate the pheromone. The database includes, for example, information on pheromones corresponding to the type of pest insect, the chemical structure of the pheromone, and a pheromone generation method, but is not limited to these examples. The generation unit can, for example, obtain the chemical structure of a pheromone preferred by a specific pest insect from the database and generate the pheromone based on the chemical structure. The generation unit can also obtain a pheromone generation method corresponding to the type of pest insect from the database and generate the pheromone based on the method. Furthermore, the generation unit can obtain materials and procedures necessary for pheromone generation from the database and generate the pheromone based on the materials and procedures. In this way, by using the database, it is possible to generate an optimal pheromone for the type of pest insect. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the chemical structure of the pheromone obtained from the database into the generation AI and cause the generation AI to generate the pheromone.

[0067] The release unit can release the generated pheromone into a trap to attract pests. Examples of traps include, but are not limited to, sticky traps, capture traps, and electric shock traps. For example, the release unit can release the generated pheromone into a sticky trap to trap pests by sticking them to the trap. The release unit can also release the generated pheromone into a capture trap to capture pests. Furthermore, the release unit can release the generated pheromone into an electric shock trap to capture pests by electric shock. In this way, pests can be effectively attracted by releasing the generated pheromone into the trap. Some or all of the above-described processing in the release unit can be performed using, or without, AI. For example, the release unit can input the timing and amount of the generated pheromone to be released into the trap into the generation AI, and cause the generation AI to release the pheromone.

[0068] The generation unit can obtain the chemical structure of a pheromone preferred by a specific pest insect from a database and generate a pheromone based on the chemical structure. Examples of chemical structures include, but are not limited to, molecular formulas, chemical formulas, and 3D structures. For example, the generation unit can obtain the molecular formula of a pheromone preferred by a specific pest insect from a database and generate a pheromone based on the molecular formula. The generation unit can also obtain the chemical formula of a pheromone preferred by a specific pest insect from a database and generate a pheromone based on the chemical formula. Furthermore, the generation unit can obtain the 3D structure of a pheromone preferred by a specific pest insect from a database and generate a pheromone based on the 3D structure. This generates a pheromone optimal for a specific pest insect, thereby enhancing the attraction effect. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the chemical structure of a pheromone obtained from the database into a generation AI and cause the generation AI to generate a pheromone.

[0069] The release unit can install a pheromone release device in the trap to attract pest insects to the trap. Examples of such devices include, but are not limited to, a pheromone release device, a pheromone dispenser, and a pheromone sprayer. The release unit can install a pheromone release device in the trap to release the generated pheromone. The release unit can also install a pheromone dispenser in the trap to release a fixed amount of pheromone. Furthermore, the release unit can install a pheromone sprayer in the trap to spray the pheromone. Thus, by installing the pheromone release device in the trap, pest insects can be effectively captured. Some or all of the above-described processing in the release unit can be performed using, or without, AI. For example, the release unit can input the operation of the pheromone release device into the generation AI and cause the generation AI to release pheromones.

[0070] The recognition unit can estimate the user's emotions and adjust the timing of pest insect recognition based on the estimated user emotions. For example, if the user is stressed, the recognition unit can increase the frequency of pest insect recognition and take prompt measures. The recognition unit can also reduce the frequency of pest insect recognition and reduce the load on the system if the user is relaxed. Furthermore, if the user is in a hurry, the recognition unit can immediately perform pest insect recognition and quickly start pheromone production. This improves system efficiency by adjusting the timing of pest insect recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit can be performed using, for example, an AI, or without an AI. For example, the recognition unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0071] The recognition unit can learn patterns of pest movement and predict pest appearance. For example, the recognition unit can learn and predict patterns of pest appearance during specific time periods based on past data. The recognition unit can also learn and predict seasonal patterns of pest movement. Furthermore, the recognition unit can predict pest appearance based on weather conditions and take measures. In this way, by learning pest movement patterns, it is possible to predict pest appearance and take measures. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input data on past pest movement into a generation AI and have the generation AI execute appearance prediction.

[0072] The recognition unit can analyze the size and shape of the pest in detail and identify the growth stage of the pest. For example, the recognition unit can analyze images captured by a camera, measure the size of the pest, and identify the growth stage. The recognition unit can also use shape analysis technology to analyze the shape of the pest in detail and identify the growth stage. Furthermore, the recognition unit can compare it with past data to identify the growth stage of the pest and take appropriate measures. In this way, by identifying the growth stage of the pest, appropriate measures can be taken. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input image data of the pest captured by a camera to the generation AI and have the generation AI identify the growth stage.

[0073] The recognition unit can analyze the behavioral patterns of pests and identify the time periods during which the pests are active. For example, the recognition unit can analyze video captured by a camera and identify the behavioral patterns of pests. The recognition unit can also predict the time periods during which pests are active based on past data. Furthermore, the recognition unit can monitor the behavior of pests in real time and identify the time periods during which they are active. This allows effective countermeasures to be taken by identifying the time periods during which pests are active. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input pest behavior data acquired by a camera into the generation AI and have the generation AI identify the time periods during which the pests are active.

[0074] The recognition unit can estimate the user's emotions and determine the priority of pest insect recognition based on the estimated user emotions. For example, if the user is stressed, the recognition unit can set the priority of pest insect recognition high. Furthermore, if the user is relaxed, the recognition unit can also immediately set the priority of pest insect recognition high if the user is in a hurry. This improves system efficiency by determining the priority of pest insect recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit can be performed using, for example, an AI, or without an AI. For example, the recognition unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] The recognition unit can detect the sounds and vibrations of pests and identify the type of pest. The recognition unit can, for example, use a microphone to detect the sounds of pests and identify the type. The recognition unit can also use a vibration sensor to detect the movements of pests and identify the type. Furthermore, the recognition unit can combine sound and vibration data to more accurately identify the type of pest. This makes it possible to accurately identify the type of pest by detecting sound and vibration. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input sound data of pests acquired by a microphone to the generation AI and have the generation AI identify the type.

[0076] The recognition unit can detect the pheromone response of a pest and identify the type of pest. The recognition unit can detect the pest response using, for example, a pheromone sensor and identify the type. The recognition unit can also analyze the pheromone response data and identify the type of pest. Furthermore, the recognition unit can combine the pheromone response with other data to more accurately identify the type of pest. In this way, the type of pest can be accurately identified by detecting the pheromone response. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input pest response data obtained by a pheromone sensor into the generation AI and have the generation AI identify the type.

[0077] The recognition unit can analyze the flight patterns of pests and identify the type of pest. The recognition unit can, for example, analyze video captured by a camera and identify the flight patterns of pests. The recognition unit can also identify the type of pest based on flight pattern data. Furthermore, the recognition unit can combine the flight pattern with other data to more accurately identify the type of pest. In this way, the type of pest can be accurately identified by analyzing the flight pattern. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input pest flight data acquired by a camera into a generation AI and have the generation AI identify the type.

[0078] The generation unit can estimate the user's emotion and adjust the timing of pheromone generation based on the estimated user emotion. For example, if the user is stressed, the generation unit can accelerate the timing of pheromone generation. Furthermore, if the user is relaxed, the generation unit can also delay the timing of pheromone generation. Furthermore, if the user is in a hurry, the generation unit can immediately start pheromone generation. This improves system efficiency by adjusting the timing of pheromone generation according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] The generation unit can optimize the amount of pheromone produced based on the ecological information of the pest. The generation unit, for example, calculates the optimal amount of pheromone produced based on the ecological information of the pest. The generation unit can also adjust the amount of pheromone produced in accordance with the breeding cycle of the pest. Furthermore, the generation unit can optimize the amount of pheromone produced in accordance with the time period when the pest is active. This enables effective pest control by optimizing the amount of pheromone produced based on the ecological information of the pest. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the ecological information of the pest into the generation AI and cause the generation AI to optimize the amount of pheromone produced.

[0080] The generation unit can fine-tune the chemical structure of the pheromone according to the habitat of the pest. The generation unit fine-tunes the chemical structure of the pheromone according to, for example, the habitat of the pest. The generation unit can also optimize the chemical structure of the pheromone according to the type of pest. Furthermore, the generation unit can adjust the chemical structure of the pheromone according to the activity pattern of the pest. In this way, the attracting effect is enhanced by fine-tuning the chemical structure of the pheromone according to the habitat of the pest. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input habitat data of the pest into the generation AI and cause the generation AI to fine-tune the chemical structure of the pheromone.

[0081] The generation unit can adjust the timing of pheromone generation in accordance with the reproductive cycle of the pest insect. The generation unit adjusts the timing of pheromone generation in accordance with, for example, the reproductive cycle of the pest insect. The generation unit can also optimize the timing of pheromone generation in accordance with the pest insect's active time period. Furthermore, the generation unit can adjust the timing of pheromone generation based on the pest insect's ecological information. This enables effective pest control by adjusting the timing of pheromone generation in accordance with the pest insect's reproductive cycle. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input pest insect reproductive cycle data into the generation AI and cause the generation AI to adjust the timing of pheromone generation.

[0082] The generation unit can estimate the user's emotion and determine the type of pheromone to generate based on the estimated user emotion. For example, if the user is stressed, the generation unit can generate a pheromone that has a rapid effect. Furthermore, if the user is relaxed, the generation unit can generate a pheromone that has a sustained effect. Furthermore, if the user is in a hurry, the generation unit can generate a pheromone that has a fast effect. This improves system efficiency by determining the type of pheromone to generate based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0083] The generation unit generates pheromones of natural enemies of pest insects to suppress the behavior of the pest insects. The generation unit, for example, generates pheromones of natural enemies of pest insects to suppress the behavior of the pest insects. The generation unit can also suppress the propagation of pest insects using pheromones of natural enemies. Furthermore, the generation unit can combine pheromones of natural enemies to effectively suppress the behavior of pest insects. In this way, by generating pheromones of natural enemies of pest insects, the behavior of pest insects can be effectively suppressed. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate pheromones of natural enemies.

[0084] The generation unit can generate pest repellent pheromones to repel pests from a specific area. The generation unit can, for example, generate pest repellent pheromones to repel pests from a specific area. The generation unit can also use the repellent pheromones to prevent pests from invading. Furthermore, the generation unit can combine repellent pheromones to effectively repel pests. In this way, by generating repellent pheromones, pests can be repelled from a specific area. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can cause a generation AI to generate repellent pheromones.

[0085] The generation unit can combine multiple pest pheromones to attract pests more effectively. The generation unit, for example, combines multiple pheromones to attract pests effectively. The generation unit can also optimize the combination of pheromones to enhance the attracting effect. Furthermore, the generation unit can select the optimal combination of pheromones depending on the type of pest. In this way, by combining multiple pheromones, the attracting effect of pests can be enhanced. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a combination of multiple pheromones to the generation AI and cause the generation AI to optimize the attracting effect.

[0086] The emission unit can estimate the user's emotion and adjust the timing of pheromone emission based on the estimated user emotion. For example, if the user is stressed, the emission unit can accelerate the timing of pheromone emission. Furthermore, if the user is relaxed, the emission unit can also delay the timing of pheromone emission. Furthermore, if the user is in a hurry, the emission unit can immediately start pheromone emission. This improves system efficiency by adjusting the timing of pheromone emission according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the emission unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0087] The emission unit can set a pheromone emission schedule in accordance with the pest's activity time period. The emission unit can set a pheromone emission schedule in accordance with the pest's activity time period, for example. The emission unit can also adjust the pheromone emission schedule in accordance with the pest's breeding cycle. Furthermore, the emission unit can optimize the pheromone emission schedule based on the pest's ecological information. This enables effective pest control by setting a pheromone emission schedule in accordance with the pest's activity time period. Some or all of the above-described processing in the emission unit can be performed using, or without, AI, for example. For example, the emission unit can input pest activity time period data into the generation AI and cause the generation AI to set the pheromone emission schedule.

[0088] The emission unit can adjust the amount of pheromone emitted according to the density of pest insects. For example, when the density of pest insects is high, the emission unit can increase the amount of pheromone emitted. Furthermore, when the density of pest insects is low, the emission unit can decrease the amount of pheromone emitted. Furthermore, the emission unit can optimize the amount of pheromone emitted according to the density of pest insects. This enables effective pest control by adjusting the amount of pheromone emitted according to the density of pest insects. Some or all of the above-mentioned processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can input pest density data to the generation AI and cause the generation AI to adjust the amount of pheromone emitted.

[0089] The releasing unit can analyze the movement patterns of the pest insects and identify the optimal release point. The releasing unit can, for example, analyze the movement patterns of the pest insects and identify the optimal pheromone release point. The releasing unit can also set the optimal release point according to the pest's active time period. Furthermore, the releasing unit can also identify the optimal release point based on the pest's ecological information. In this way, the optimal pheromone release point can be identified by analyzing the pest's movement patterns. Some or all of the above-mentioned processing in the releasing unit can be performed using AI, for example, or can be performed without using AI. For example, the releasing unit can input pest insect movement pattern data into the generating AI and cause the generating AI to identify the optimal release point.

[0090] The emission unit can estimate the user's emotions and determine the priority of pheromone emission based on the estimated user emotions. For example, if the user is feeling stressed, the emission unit can set the priority of pheromone emission high. Furthermore, if the user is relaxed, the emission unit can also immediately set the priority of pheromone emission high if the user is in a hurry. This improves system efficiency by determining the priority of pheromone emission according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emission unit can be performed using, for example, an AI, or without an AI. For example, the emission unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0091] The emission unit can emit pheromones of natural enemies of pest insects to suppress their behavior. The emission unit can, for example, emit pheromones of natural enemies of pest insects to suppress their behavior. The emission unit can also use pheromones of natural enemies to suppress the reproduction of pest insects. Furthermore, the emission unit can combine pheromones of natural enemies to effectively suppress the behavior of pest insects. In this way, by releasing pheromones of natural enemies of pest insects, the behavior of pest insects can be effectively suppressed. Some or all of the above-mentioned processing in the emission unit can be performed using AI, for example, or without AI. For example, the emission unit can cause the generation AI to execute the release of pheromones of natural enemies.

[0092] The emission unit can emit pest repellent pheromones to repel pests from a specific area. For example, the emission unit can emit pest repellent pheromones to repel pests from a specific area. The emission unit can also use repellent pheromones to prevent pests from entering. Furthermore, the emission unit can combine repellent pheromones to effectively repel pests. In this way, pests can be repelled from a specific area by emitting repellent pheromones. Some or all of the above-described processing in the emission unit can be performed using AI, or can be performed without using AI. For example, the emission unit can cause the generation AI to emit repellent pheromones.

[0093] The release unit can combine multiple pest pheromones to attract pests more effectively. For example, the release unit can combine multiple pheromones to attract pests effectively. The release unit can also optimize the combination of pheromones to enhance the attracting effect. Furthermore, the release unit can select the optimal combination of pheromones depending on the type of pest. In this way, by combining multiple pheromones, the attracting effect of pests can be enhanced. Some or all of the above-described processing in the release unit may be performed using, or without, AI. For example, the release unit can input the combination of multiple pheromones to the generation AI and cause the generation AI to optimize the attracting effect. === Hard Collateral 1-1 === Each of the multiple elements including the recognition unit, generation unit, and emission unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit is realized by the camera 42 and control unit 46A of the smart device 14, acquires image data of the pest, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, acquires a pheromone generation method corresponding to the type of pest from the database 24, and generates the pheromone. The emission unit is realized, for example, by the output device 40 of the smart device 14, and emits the generated pheromone into the trap. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned recognition unit, generation unit, and emission unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit is realized by the camera 42 and control unit 46A of the smart glasses 214, acquires image data of the pest, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, acquires a pheromone generation method corresponding to the type of pest from the database 24, and generates the pheromone. The emission unit is realized, for example, by the output device 40 of the smart glasses 214, and emits the generated pheromone into the trap. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned recognition unit, generation unit, and emission unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the recognition unit is realized by the camera 42 and control unit 46A of the headset terminal 314, acquires image data of the pest, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, acquires a pheromone generation method according to the type of pest from the database 24, and generates the pheromone. The emission unit is realized, for example, by the output device 40 of the headset terminal 314, and emits the generated pheromone into the trap. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned recognition unit, generation unit, and emission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit is realized by the camera 42 and control unit 46A of the robot 414, acquires image data of the pest, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, acquires a pheromone generation method according to the type of pest from the database 24, and generates the pheromone. The emission unit is realized, for example, by the output device 40 of the robot 414, and emits the generated pheromone into a trap.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The recognition unit can analyze the behavioral patterns of pests and identify the time periods during which the pests are active. For example, it can analyze video captured by a camera and identify the behavioral patterns of pests. The recognition unit can also predict the time periods during which pests are active based on past data. Furthermore, the recognition unit can monitor the behavior of pests in real time and identify the time periods during which they are active. This allows effective countermeasures to be taken by identifying the time periods during which pests are active. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input pest behavior data acquired by a camera into a generation AI and have the generation AI identify the time periods during which the pests are active.

[0096] The generation unit can generate pheromones of natural enemies of pest insects to suppress the behavior of the pest insects. For example, the generation unit can generate pheromones of natural enemies of pest insects to suppress the behavior of the pest insects. The generation unit can also use pheromones of natural enemies to suppress the reproduction of pest insects. Furthermore, the generation unit can combine pheromones of natural enemies to effectively suppress the behavior of pest insects. In this way, by generating pheromones of natural enemies of pest insects, the behavior of pest insects can be effectively suppressed. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate pheromones of natural enemies.

[0097] The emission unit can emit pest repellent pheromones to repel pests from a specific area. For example, the emission unit can emit pest repellent pheromones to repel pests from a specific area. The emission unit can also use repellent pheromones to prevent pests from invading. Furthermore, the emission unit can combine repellent pheromones to effectively repel pests. In this way, pests can be repelled from a specific area by emitting repellent pheromones. Some or all of the above-described processing in the emission unit can be performed using AI, or can be performed without using AI. For example, the emission unit can cause the generation AI to emit repellent pheromones.

[0098] The recognition unit can estimate the user's emotions and adjust the timing of pest insect recognition based on the estimated user emotions. For example, if the user is stressed, the recognition unit can increase the frequency of pest insect recognition and take prompt measures. The recognition unit can also reduce the frequency of pest insect recognition and reduce the load on the system if the user is relaxed. Furthermore, if the user is in a hurry, the recognition unit can immediately perform pest insect recognition and quickly start pheromone production. This improves system efficiency by adjusting the timing of pest insect recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit can be performed using AI, or without AI. For example, the recognition unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0099] The generation unit can optimize the amount of pheromone produced based on the ecological information of the pest. For example, the generation unit calculates the optimal amount of pheromone produced based on the ecological information of the pest. The generation unit can also adjust the amount of pheromone produced in accordance with the pest's breeding cycle. Furthermore, the generation unit can optimize the amount of pheromone produced in accordance with the pest's activity period. This enables effective pest control by optimizing the amount of pheromone produced based on the ecological information of the pest. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the ecological information of the pest into the generation AI and cause the generation AI to optimize the amount of pheromone produced.

[0100] The emission unit can estimate the user's emotions and adjust the timing of pheromone emission based on the estimated user emotions. For example, if the user is stressed, the emission unit can accelerate the timing of pheromone emission. Furthermore, if the user is relaxed, the emission unit can also delay the timing of pheromone emission. Furthermore, if the user is in a hurry, the emission unit can immediately start pheromone emission. This improves system efficiency by adjusting the timing of pheromone emission according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emission unit can be performed using, for example, an AI, or without an AI. For example, the emission unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0101] The recognition unit can detect the sounds and vibrations of pests and identify the type of pest. For example, the recognition unit can detect the sounds of pests using a microphone and identify the type. The recognition unit can also detect the movements of pests using a vibration sensor and identify the type. Furthermore, the recognition unit can combine sound and vibration data to more accurately identify the type of pest. This makes it possible to accurately identify the type of pest by detecting sound and vibration. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input sound data of pests acquired by a microphone to the generation AI and have the generation AI identify the type.

[0102] The generation unit can adjust the timing of pheromone generation in accordance with the reproductive cycle of the pest insect. For example, the generation unit adjusts the timing of pheromone generation in accordance with the reproductive cycle of the pest insect. The generation unit can also optimize the timing of pheromone generation in accordance with the pest insect's active time period. Furthermore, the generation unit can adjust the timing of pheromone generation based on the pest insect's ecological information. This enables effective pest control by adjusting the timing of pheromone generation in accordance with the pest insect's reproductive cycle. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input pest insect reproductive cycle data into the generation AI and cause the generation AI to adjust the timing of pheromone generation.

[0103] The emission unit can adjust the amount of pheromone emitted according to the density of pest insects. For example, when the density of pest insects is high, the emission unit can increase the amount of pheromone emitted. Also, when the density of pest insects is low, the emission unit can decrease the amount of pheromone emitted. Furthermore, the emission unit can optimize the amount of pheromone emitted according to the density of pest insects. This allows for effective pest control by adjusting the amount of pheromone emitted according to the density of pest insects. Some or all of the above-described processing in the emission unit may be performed using, or without, AI, for example. For example, the emission unit can input pest density data into the generation AI and cause the generation AI to adjust the amount of pheromone emitted.

[0104] The generation unit can estimate the user's emotion and determine the type of pheromone to generate based on the estimated user emotion. For example, if the user is stressed, the generation unit can generate a pheromone that has a rapid effect. Furthermore, if the user is relaxed, the generation unit can generate a pheromone that has a sustained effect. Furthermore, if the user is in a hurry, the generation unit can generate a pheromone that has a fast effect. This improves system efficiency by determining the type of pheromone to generate based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The recognition unit recognizes pests. The recognition unit, for example, uses a camera to recognize pests and identifies the type of pest using image analysis technology. When the camera detects a pest, the image data is sent to the system, and the system uses image analysis technology to identify the type of pest. Step 2: The generator generates a pheromone that matches the pest insect recognized by the recognizer. For example, the generator retrieves a pheromone generation method for each type of pest insect from a database, and generates a pheromone based on the chemical structure of a pheromone that the specific pest insect prefers. Step 3: The emission unit emits the pheromone generated by the generation unit. The emission unit, for example, emits the generated pheromone into a trap to attract pests. A device for emitting pheromones is installed in the trap so that pests are attracted to the trap.

[0107] 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.

[0108] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 recognition unit that recognizes pests; a generating unit that generates a pheromone in accordance with the pest insect recognized by the recognizing unit; a release unit that releases the pheromone generated by the generation unit; Equipped with A system characterized by:

2. The recognition unit Cameras are used to recognize pests, and image analysis technology is used to identify the type of pest.

2. The system of claim 1.

3. The generation unit The method for producing pheromones according to the type of pest is obtained from the database, and pheromones are produced.

2. The system of claim 1.

4. The release section is The pheromone produced is released into the trap, attracting pests.

2. The system of claim 1.

5. The generation unit The chemical structure of the pheromone that a particular pest prefers is obtained from a database, and a pheromone is generated based on that chemical structure.

2. The system of claim 1.

6. The release section is Place a pheromone-emitting device on the trap to attract pests to the trap.

2. The system of claim 1.

7. The recognition unit Estimates the user's emotions and adjusts the timing of pest recognition based on the estimated user emotions.

2. The system of claim 1.

8. The recognition unit Learns pest movement patterns and predicts their appearance 2. The system of claim 1.

Images