System

Abstract

An object of the system according to the embodiment is to effectively and efficiently drive away harmful birds.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a discharge unit, and a chase unit. The reception part receives data related to the kind of the harmful bird as an input. The generation unit generates a cry of a natural enemy of the bird aversion based on the data received by the reception unit. The emission unit emits the sound generated by the generation unit from a speaker mounted on the drone. The chase unit chases the bird aversion based on the cries flown by the outflow unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the drawback of requiring a lot of effort to control pest birds and having limited effectiveness.

[0005] The system according to the embodiment aims to repel harmful birds effectively and efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a sending unit, and a repelling unit. The receiving unit receives data on the type of nuisance bird as input. The generating unit generates the calls of the nuisance bird's natural enemy based on the data received by the receiving unit. The sending unit broadcasts the calls generated by the generating unit from a speaker mounted on the drone. The repelling unit repels the nuisance bird based on the calls broadcast by the sending unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively and efficiently repel harmful birds. [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 bird control system according to an embodiment of the present invention effectively repels pest birds by generating the calls of natural predators corresponding to the type of pest bird and broadcasting them through a speaker mounted on a drone. The pest bird control system receives data on the type of pest bird as input, and a generation AI generates the calls of the natural predators and broadcasts them through a speaker mounted on the drone, thereby repelling the pest birds. For example, the pest bird control system generates the calls of hawks to repel crows, and the drone flies over the entire farmland, broadcasting the calls to repel the pest birds. This allows the pest bird control system to cover a wide area and be highly effective. Furthermore, by utilizing the generation AI, it is possible to generate the calls of natural predators tailored to various types of pest birds, making it highly flexible. This allows the pest bird control system to increase farmers' yields, reduce labor, and contribute to solving food problems. For example, reducing damage caused by pest birds increases crop yields and improves farmers' profits. Furthermore, reducing labor reduces the burden on farmers. This can also contribute to solving food problems.

[0029] A pest bird control system according to an embodiment includes a receiving unit, a generating unit, a sending unit, and a repelling unit. The receiving unit receives input data related to the type of pest bird. For example, the receiving unit receives information related to the type of pest bird input by a user. The receiving unit can also automatically detect the type of pest bird using a sensor. The receiving unit can also estimate the type of pest bird based on past data on pest bird damage. The generating unit uses a generation AI to generate the calls of the pest bird's natural enemies based on the data received by the receiving unit. For example, the generating unit generates a hawk call for crows. The generating unit can also generate an owl call for sparrows. The generating unit can also generate a hawk call for pigeons. The sending unit broadcasts the calls generated by the generating unit from a speaker mounted on the drone. For example, the sending unit broadcasts the calls while the drone flies to cover the entire farmland. The sending unit can also adjust the drone's flight pattern to focus on covering a specific area. The output unit can also adjust the volume and playback frequency of the bird calls. The repelling unit repels nuisance birds based on the bird calls output by the output unit. For example, the repelling unit repels nuisance birds by adjusting the volume and playback frequency of the bird calls. The repelling unit can also monitor the reactions of nuisance birds in real time and select the optimal repelling method. The repelling unit can also apply different repelling methods to different types of nuisance birds. As a result, the nuisance bird control system according to the embodiment can generate the calls of natural enemies according to the type of nuisance bird, covering a wide area and effectively repelling nuisance birds.

[0030] The generation unit can generate the cry of a natural enemy according to the type of pest bird. For example, the generation unit generates the cry of a hawk for a crow. The generation unit can also generate the cry of an owl for a sparrow. The generation unit can also generate the cry of a hawk for a pigeon. This makes it possible to generate effective cries according to the type of pest bird. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the type of pest bird into the generation AI, which then generates the cry of a natural enemy.

[0031] The output unit can broadcast the generated bird calls from a speaker mounted on the drone. For example, the output unit broadcasts the calls while the drone flies to cover the entire farmland. The output unit can also adjust the drone's flight pattern to focus on covering a specific area. The output unit can also adjust the volume and playback frequency of the calls. This allows the drone to broadcast the calls over a wide area. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs the drone's flight pattern into AI, which then calculates the optimal flight pattern.

[0032] The repelling unit can repel nuisance birds based on the generated calls. The repelling unit repels nuisance birds, for example, by adjusting the volume or playback frequency of the calls. The repelling unit can also monitor the reactions of nuisance birds in real time and select the optimal repelling means. The repelling unit can also apply different repelling means to each type of nuisance bird. This makes it possible to effectively repel nuisance birds based on the generated calls. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs nuisance bird reaction data into AI, which selects the optimal repelling means.

[0033] The generation unit can generate the sound of a hawk for a crow. For example, the generation unit generates the sound of a hawk for a crow. The generation unit can also generate the sound of an owl for a sparrow. The generation unit can also generate the sound of a hawk for a pigeon. This makes it possible to generate sounds that are effective against specific pest birds. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data of a crow into the generation AI, which then generates the sound of a hawk.

[0034] The output unit can fly the drone to cover the entire farmland and broadcast the generated bird calls. For example, the output unit can broadcast the bird calls while the drone flies to cover the entire farmland. The output unit can also adjust the drone's flight pattern to focus on covering specific areas. The output unit can also adjust the volume and playback frequency of the calls. This allows the entire farmland to be covered and pest birds to be effectively repelled. Some or all of the above-mentioned processing in the output unit can be performed using AI, or can be performed without using AI. For example, the output unit inputs the drone's flight pattern into AI, which then calculates the optimal flight pattern.

[0035] The reception unit can analyze past pest bird damage data and select an effective data reception method. For example, the reception unit can prioritize reception of data from time periods when specific pest birds frequently appear based on past damage data. The reception unit can also prioritize reception of data from areas with heavy damage. The reception unit can also analyze past damage data and adjust the data reception method based on specific seasons and weather conditions. This enables effective data reception by selecting the optimal reception method based on past data. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs past damage data into AI, which selects the optimal data reception method.

[0036] When receiving data on types of pest birds, the receiving unit can filter the data based on the current status of the user's crops. For example, when the user's crops are in the harvest season, the receiving unit preferentially receives data on types of pest birds. Furthermore, when the user's crops are in the growing season, the receiving unit can filter and receive data on specific pest birds. Furthermore, when the user's crops are infested with pests, the receiving unit can filter and receive data on types of pest birds. This makes it possible to receive data according to the status of the crops. Some or all of the above-mentioned processing in the receiving unit may be performed using AI, or may be performed without using AI. For example, the receiving unit inputs the status data of the user's crops into AI, and the AI ​​selects the optimal data reception method.

[0037] When receiving data regarding the type of pest bird, the reception unit can select an effective reception means depending on the user's input method. For example, if the user selects voice input, the reception unit can receive data using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also receive data using text analysis technology. Furthermore, if the user selects image input, the reception unit can also receive data using image recognition technology. This makes it possible to receive data optimally depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs voice data into AI, which converts the voice into text.

[0038] When receiving data on types of pest birds, the reception unit can prioritize receiving highly relevant data, taking into account the user's geographical location information. For example, the reception unit can prioritize receiving pest bird data from the area where the user's farmland is located. The reception unit can also prioritize receiving pest bird data from neighboring areas based on the user's geographical location information. The reception unit can also prioritize receiving data from areas where specific pest birds are frequently found based on the user's geographical location information. This makes it possible to receive highly relevant data based on geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information into AI, which then selects the optimal data reception method.

[0039] When receiving data on the types of pest birds, the reception unit can analyze the user's social media activities and receive related data. For example, the reception unit receives data based on information about pest bird damage shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related pest bird data. The reception unit can also receive related pest bird data by referring to the activities of the user's friends on social media. This makes it possible to receive highly relevant data based on social media activities. Some or all of the above-mentioned processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's social media data into AI, which selects related data.

[0040] When receiving data on types of pest birds, the reception unit can adjust the reception method by reflecting the user's past feedback. The reception unit adjusts the data reception method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially accept data on specific pest birds based on the user's past feedback. The reception unit can also customize the data reception interface by referring to the user's past feedback. This makes it possible to receive data that is customized based on past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's feedback data into AI, which then selects the optimal data reception method.

[0041] When generating the calls of natural enemies, the generation unit can determine the type of call to generate based on the activity hours of the pest birds. For example, if the pest birds are active at night, the generation unit generates the calls of nocturnal natural enemies. Furthermore, if the pest birds are active during the day, the generation unit can also generate the calls of diurnal natural enemies. Furthermore, the generation unit can generate the calls of different natural enemies depending on the activity hours of the pest birds. This makes it possible to generate optimal calls according to the activity hours of the pest birds. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the activity hours of the pest birds into the generation AI, which then generates the optimal calls.

[0042] When generating the calls of natural enemies, the generation unit can apply different generation algorithms depending on the habitat of the pest birds. For example, if the pest birds live in a forest, the generation unit applies an algorithm that generates calls of natural enemies that are suitable for forests. Furthermore, if the pest birds live in an urban area, the generation unit can also apply an algorithm that generates calls of natural enemies that are suitable for urban areas. Furthermore, the generation unit can also apply different generation algorithms depending on the habitat of the pest birds. This makes it possible to generate optimal calls that are suitable for the habitat of the pest birds. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs habitat data of the pest birds into the generation AI, which then generates the optimal calls.

[0043] When generating the calls of natural enemies, the generation unit can improve the accuracy of generation by referring to past generation results. For example, the generation unit analyzes the effects of calls generated in the past, and the generation AI improves the accuracy. The generation unit can also have the generation AI generate the optimal call based on past generation results. The generation unit can also have the generation AI adjust the generation algorithm by referring to past generation results. This makes it possible to generate highly accurate calls based on past generation results. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs past generation result data into the generation AI, and the generation AI generates the optimal call.

[0044] When generating the calls of natural enemies, the generation unit can determine the order of the calls to be generated based on the breeding season of the pest birds. For example, the generation unit causes the generation AI to prioritize generating the calls of specific natural enemies according to the breeding season of the pest birds. The generation unit can also generate calls of natural enemies with different generation AIs based on the breeding season of the pest birds. The generation unit can also cause the generation AI to adjust the priority of the calls according to the breeding season of the pest birds. This makes it possible to generate optimal calls according to the breeding season of the pest birds. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs breeding season data of pest birds into the generation AI, and the generation AI generates the optimal calls.

[0045] When generating the calls of natural enemies, the generation unit can improve the accuracy of generation by referring to literature related to pest birds. For example, the generation unit refers to literature on the ecology of pest birds, and the generation AI generates the optimal call. The generation unit can also have the generation AI generate the call based on literature on the behavioral patterns of pest birds. The generation unit can also have the generation AI generate the call by referring to literature on the breeding seasons of pest birds. In this way, by referring to related literature, it becomes possible to generate calls with high accuracy. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs literature data related to pest birds into the generation AI, and the generation AI generates the optimal call.

[0046] When generating the calls of natural enemies, the generation unit can take into account the ecological information of pest birds. For example, the generation unit allows the generation AI to generate the optimal call based on the ecological information of the pest birds. The generation unit can also allow the generation AI to generate the call by taking into account the behavioral patterns of the pest birds. The generation unit can also allow the generation AI to generate the call by taking into account the breeding season of the pest birds. This makes it possible to generate more effective calls by taking into account the ecological information. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs ecological information data of pest birds into the generation AI, and the generation AI generates the optimal call.

[0047] The output unit can determine the timing of outputting the bird calls based on the frequency of pest bird appearances when outputting the bird calls. For example, the output unit adjusts the timing of outputting the bird calls to match the time period when pest birds frequently appear. The output unit can also adjust the timing of outputting the bird calls based on the frequency of pest bird appearances. The output unit can also analyze the appearance patterns of pest birds and output the bird calls at the optimal timing. This enables optimal output timing based on the frequency of appearance. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs pest bird appearance frequency data into AI, which calculates the optimal output timing.

[0048] The output unit can optimize the drone's flight pattern when distributing bird calls to improve the effectiveness of the distribution. For example, the output unit can optimize the drone's flight pattern to effectively distribute bird calls over a wide area. The output unit can also adjust the drone's flight pattern to selectively cover areas where pest birds are frequently found. The output unit can also optimize the drone's flight pattern to effectively distribute bird calls while reducing battery consumption. This enables effective bird call distribution through optimized flight patterns. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs drone flight pattern data into AI, which then calculates the optimal flight pattern.

[0049] When outputting the bird calls, the output unit can improve the accuracy of the output by referring to past output results. For example, the output unit analyzes past output results to find an effective method for outputting the bird calls. The output unit can also adjust the output timing and method based on past output results. The output unit can also improve the accuracy of the output by referring to past output results. This enables highly accurate output of bird calls based on past results. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs past output result data into AI, which then calculates the optimal output method.

[0050] When distributing the bird calls, the distributing unit can determine the range of distributing the calls based on the activity area of ​​the pest birds. For example, the distributing unit adjusts the distributing range of the calls to match the area where many pest birds appear. The distributing unit can also optimize the distributing range of the calls based on the activity area of ​​the pest birds. The distributing unit can also analyze the activity patterns of the pest birds and distribute the calls in an optimal range. This enables an optimal distributing range based on the activity area. Some or all of the above-mentioned processing in the distributing unit may be performed using AI, or may be performed without using AI. For example, the distributing unit inputs pest bird activity area data into AI, which then calculates the optimal distributing range.

[0051] The output unit can adjust the frequency of output of the cry taking into account the remaining battery level of the drone when outputting the cry. For example, the output unit adjusts the frequency of output of the cry according to the remaining battery level of the drone. Furthermore, the output unit can also reduce the frequency of output when the remaining battery level is low to save battery. Furthermore, the output unit can increase the frequency of output when the remaining battery level is sufficient to improve effectiveness. This enables the optimal output frequency according to the remaining battery level. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs remaining battery level data of the drone into AI, which then calculates the optimal output frequency.

[0052] The output unit can analyze the behavioral patterns of the pest birds when outputting their calls and determine the timing of the output. For example, the output unit analyzes the behavioral patterns of the pest birds and outputs their calls at the optimal timing. The output unit can also adjust the output timing based on the appearance patterns of the pest birds. The output unit can also optimize the output timing by referring to the behavioral patterns of the pest birds. This enables the optimal output timing based on the behavioral patterns. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs behavioral pattern data of the pest birds into AI, which then calculates the optimal output timing.

[0053] When repelling, the repelling unit can monitor the reaction of the pest birds in real time and determine the repelling means. For example, the repelling unit can monitor the reaction of the pest birds in real time and select the optimal repelling means. The repelling unit can also adjust the repelling means based on the reaction of the pest birds. The repelling unit can also monitor the behavior of the pest birds in real time and optimize the repelling means. This makes it possible to determine the optimal repelling means through real-time monitoring. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs reaction data of the pest birds into AI, which then calculates the optimal repelling means.

[0054] The repelling unit can apply different repelling means to each type of pest bird when repelling. For example, the repelling unit selects different repelling means depending on the type of pest bird. The repelling unit can also apply the most appropriate repelling means to each type of pest bird. The repelling unit can also adjust the repelling means based on the type of pest bird. This makes it possible to use the most appropriate repelling means depending on the type of pest bird. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs data on the type of pest bird into AI, which then calculates the most appropriate repelling means.

[0055] When chasing, the chasing unit can improve the accuracy of the chasing by referring to past chasing results. The chasing unit, for example, analyzes past chasing results and finds the optimal chasing means. The chasing unit can also adjust the chasing means based on past chasing results. The chasing unit can also improve the accuracy of the chasing by referring to past chasing results. This enables highly accurate chasing based on past results. Some or all of the above-mentioned processing in the chasing unit may be performed using AI, or may be performed without using AI. For example, the chasing unit inputs past chasing result data into AI, and the AI ​​calculates the optimal chasing means.

[0056] When repelling, the repelling unit can determine the repelling means taking into account the geographical distribution of nuisance birds. For example, the repelling unit selects the optimal repelling means based on the geographical distribution of nuisance birds. The repelling unit can also adjust the repelling means according to the distribution area of ​​nuisance birds. The repelling unit can also select the repelling means with reference to the geographical distribution of nuisance birds. This makes it possible to determine the optimal repelling means based on the geographical distribution. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs geographical distribution data of nuisance birds into AI, which then calculates the optimal repelling means.

[0057] When repelling, the repelling unit can improve the accuracy of repelling by referring to literature related to nuisance birds. The repelling unit, for example, refers to literature on the ecology of nuisance birds and selects the optimal repelling means. The repelling unit can also adjust the repelling means based on literature on the behavioral patterns of nuisance birds. The repelling unit can also select the repelling means by referring to literature on the breeding season of nuisance birds. In this way, by referring to the related literature, highly accurate repelling is possible. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs literature data related to nuisance birds into AI, which then calculates the optimal repelling means.

[0058] When repelling, the repelling unit can analyze the behavioral patterns of the nuisance birds and determine the repelling means. For example, the repelling unit analyzes the behavioral patterns of the nuisance birds and selects the optimal repelling means. The repelling unit can also adjust the repelling means based on the behavioral patterns of the nuisance birds. The repelling unit can also optimize the repelling means by referring to the behavioral patterns of the nuisance birds. This makes it possible to determine the optimal repelling means based on the behavioral patterns. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs behavioral pattern data of the nuisance birds into AI, which then calculates the optimal repelling means.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit not only receives data on the types of pest birds as input, but can also filter the data based on the type and growth stage of the user's crops. For example, if the user is growing tomatoes, the reception unit will preferentially receive data on pest birds that are particularly harmful to tomatoes. Furthermore, if the crops are in their growing season, the reception unit can also preferentially receive data on pest birds that are particularly susceptible to damage during the growing season. Furthermore, if the crops are in their harvest season, the reception unit can also preferentially receive data on pest birds that are particularly susceptible to damage during the harvest season. This makes it possible to receive optimal data according to the type and growth stage of the crops.

[0061] The generator not only generates the calls of natural enemies according to the type of nuisance bird, but can also adjust the type and timing of the calls based on the behavioral patterns of the nuisance bird. For example, if the nuisance bird is active at night, the generator generates the calls of nocturnal natural enemies and plays the calls at night. In addition, if nuisance birds appear more frequently in a particular season, the generator can generate the calls of natural enemies that are appropriate for that season. Furthermore, the generator can adjust the playback frequency and volume of the calls based on the behavioral patterns of the nuisance bird. This makes it possible to generate optimal calls according to the behavioral patterns of the nuisance bird.

[0062] The outflow unit not only broadcasts the bird calls generated from the drone's onboard speaker, but also optimizes the drone's flight pattern to maximize the effectiveness of the calls. For example, the outflow unit not only broadcasts bird calls while the drone flies to cover the entire farmland, but also adjusts the flight pattern to focus on areas where pest birds are most prevalent. The outflow unit can also optimize the flight pattern taking into account the drone's remaining battery level, effectively broadcasting the bird calls while minimizing battery consumption. Furthermore, the outflow unit can adjust the drone's flight speed and altitude to maximize the effectiveness of the calls. This allows for an optimized flight pattern to effectively broadcast bird calls.

[0063] The repelling unit not only repels nuisance birds based on the generated bird calls, but also monitors the responses of the nuisance birds in real time and selects the optimal repelling method. For example, the repelling unit not only repels nuisance birds by adjusting the volume and playback frequency of the bird calls, but also monitors the responses of the nuisance birds and adjusts the type of bird calls and playback timing. The repelling unit can also apply different repelling methods to different types of nuisance birds to achieve optimal repelling effects. Furthermore, the repelling unit can analyze the behavioral patterns of the nuisance birds and select the optimal repelling method. This makes it possible to select the optimal repelling method through real-time monitoring.

[0064] The generation unit can not only generate the cry of a hawk for a crow, but also apply different generation algorithms depending on the habitat of the pest bird. For example, if the pest bird lives in a forest, the generation unit applies an algorithm that generates the cry of a natural enemy that is suitable for forests. Also, if the pest bird lives in an urban area, the generation unit can apply an algorithm that generates the cry of a natural enemy that is suitable for urban areas. Furthermore, the generation unit can apply different generation algorithms depending on the habitat of the pest bird. This makes it possible to generate the optimal cry depending on the habitat of the pest bird.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives data on the type of pest bird as input. For example, it receives information on the type of pest bird entered by the user. It is also possible to automatically detect the type of pest bird using a sensor. It is also possible to estimate the type of pest bird based on past data on pest bird damage. Step 2: The generator generates the sounds of the pest bird's natural enemy based on the data received by the receiver. For example, the generator AI can be used to generate the sound of a hawk for a crow. It can also generate the sound of an owl for a sparrow. It can also generate the sound of a hawk for a pigeon. Step 3: The output unit plays the bird calls generated by the generator from the drone's onboard speaker. For example, the drone can play the bird calls while flying to cover the entire farmland. It can also adjust the drone's flight pattern to focus on specific areas. Furthermore, the volume and playback frequency of the bird calls can be adjusted. Step 4: The repelling unit repels nuisance birds based on the bird calls played by the bird-playing unit. For example, the volume and frequency of the bird calls can be adjusted to repel nuisance birds. The system can also monitor the responses of nuisance birds in real time and select the optimal repelling method. Furthermore, different repelling methods can be applied to different types of nuisance birds.

[0067] (Example 2) A pest bird control system according to an embodiment of the present invention effectively repels pest birds by generating the calls of natural predators corresponding to the type of pest bird and broadcasting them through a speaker mounted on a drone. The pest bird control system receives data on the type of pest bird as input, and a generation AI generates the calls of the natural predators and broadcasts them through a speaker mounted on the drone, thereby repelling the pest birds. For example, the pest bird control system generates the calls of hawks to repel crows, and the drone flies over the entire farmland, broadcasting the calls to repel the pest birds. This allows the pest bird control system to cover a wide area and be highly effective. Furthermore, by utilizing the generation AI, it is possible to generate the calls of natural predators tailored to various types of pest birds, making it highly flexible. This allows the pest bird control system to increase farmers' yields, reduce labor, and contribute to solving food problems. For example, reducing damage caused by pest birds increases crop yields and improves farmers' profits. Furthermore, reducing labor reduces the burden on farmers. This can also contribute to solving food problems.

[0068] A pest bird control system according to an embodiment includes a receiving unit, a generating unit, a sending unit, and a repelling unit. The receiving unit receives input data related to the type of pest bird. For example, the receiving unit receives information related to the type of pest bird input by a user. The receiving unit can also automatically detect the type of pest bird using a sensor. The receiving unit can also estimate the type of pest bird based on past data on pest bird damage. The generating unit uses a generation AI to generate the calls of the pest bird's natural enemies based on the data received by the receiving unit. For example, the generating unit generates a hawk call for crows. The generating unit can also generate an owl call for sparrows. The generating unit can also generate a hawk call for pigeons. The sending unit broadcasts the calls generated by the generating unit from a speaker mounted on the drone. For example, the sending unit broadcasts the calls while the drone flies to cover the entire farmland. The sending unit can also adjust the drone's flight pattern to focus on covering a specific area. The output unit can also adjust the volume and playback frequency of the bird calls. The repelling unit repels nuisance birds based on the bird calls output by the output unit. For example, the repelling unit repels nuisance birds by adjusting the volume and playback frequency of the bird calls. The repelling unit can also monitor the reactions of nuisance birds in real time and select the optimal repelling method. The repelling unit can also apply different repelling methods to different types of nuisance birds. As a result, the nuisance bird control system according to the embodiment can generate the calls of natural enemies according to the type of nuisance bird, covering a wide area and effectively repelling nuisance birds.

[0069] The generation unit can generate the cry of a natural enemy according to the type of pest bird. For example, the generation unit generates the cry of a hawk for a crow. The generation unit can also generate the cry of an owl for a sparrow. The generation unit can also generate the cry of a hawk for a pigeon. This makes it possible to generate effective cries according to the type of pest bird. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the type of pest bird into the generation AI, which then generates the cry of a natural enemy.

[0070] The output unit can broadcast the generated bird calls from a speaker mounted on the drone. For example, the output unit broadcasts the calls while the drone flies to cover the entire farmland. The output unit can also adjust the drone's flight pattern to focus on covering a specific area. The output unit can also adjust the volume and playback frequency of the calls. This allows the drone to broadcast the calls over a wide area. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs the drone's flight pattern into AI, which then calculates the optimal flight pattern.

[0071] The repelling unit can repel nuisance birds based on the generated calls. The repelling unit repels nuisance birds, for example, by adjusting the volume or playback frequency of the calls. The repelling unit can also monitor the reactions of nuisance birds in real time and select the optimal repelling means. The repelling unit can also apply different repelling means to each type of nuisance bird. This makes it possible to effectively repel nuisance birds based on the generated calls. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs nuisance bird reaction data into AI, which selects the optimal repelling means.

[0072] The generation unit can generate the sound of a hawk for a crow. For example, the generation unit generates the sound of a hawk for a crow. The generation unit can also generate the sound of an owl for a sparrow. The generation unit can also generate the sound of a hawk for a pigeon. This makes it possible to generate sounds that are effective against specific pest birds. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data of a crow into the generation AI, which then generates the sound of a hawk.

[0073] The output unit can fly the drone to cover the entire farmland and broadcast the generated bird calls. For example, the output unit can broadcast the bird calls while the drone flies to cover the entire farmland. The output unit can also adjust the drone's flight pattern to focus on covering specific areas. The output unit can also adjust the volume and playback frequency of the calls. This allows the entire farmland to be covered and pest birds to be effectively repelled. Some or all of the above-mentioned processing in the output unit can be performed using AI, or can be performed without using AI. For example, the output unit inputs the drone's flight pattern into AI, which then calculates the optimal flight pattern.

[0074] The reception unit can estimate the user's emotions and adjust the timing of receiving data related to the type of pest bird based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of receiving data and wait until the user is relaxed. Furthermore, if the user is relaxed, the reception unit can immediately receive data and quickly start processing. Furthermore, if the user is in a hurry, the reception unit can accelerate the timing of receiving data and quickly process the data. This allows data to be received at a more appropriate time by adjusting the timing of receiving data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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 reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions.

[0075] The reception unit can analyze past pest bird damage data and select an effective data reception method. For example, the reception unit can prioritize reception of data from time periods when specific pest birds frequently appear based on past damage data. The reception unit can also prioritize reception of data from areas with heavy damage. The reception unit can also analyze past damage data and adjust the data reception method based on specific seasons and weather conditions. This enables effective data reception by selecting the optimal reception method based on past data. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs past damage data into AI, which selects the optimal data reception method.

[0076] When receiving data on types of pest birds, the receiving unit can filter the data based on the current status of the user's crops. For example, when the user's crops are in the harvest season, the receiving unit preferentially receives data on types of pest birds. Furthermore, when the user's crops are in the growing season, the receiving unit can filter and receive data on specific pest birds. Furthermore, when the user's crops are infested with pests, the receiving unit can filter and receive data on types of pest birds. This makes it possible to receive data according to the status of the crops. Some or all of the above-mentioned processing in the receiving unit may be performed using AI, or may be performed without using AI. For example, the receiving unit inputs the status data of the user's crops into AI, and the AI ​​selects the optimal data reception method.

[0077] When receiving data regarding the type of pest bird, the reception unit can select an effective reception means depending on the user's input method. For example, if the user selects voice input, the reception unit can receive data using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also receive data using text analysis technology. Furthermore, if the user selects image input, the reception unit can also receive data using image recognition technology. This makes it possible to receive data optimally depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs voice data into AI, which converts the voice into text.

[0078] The reception unit can estimate the user's emotions and determine the priority of data to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit postpones data of low importance and prioritizes data of high importance. Furthermore, when the user is relaxed, the reception unit can equally accept all data. Furthermore, when the user is in a hurry, the reception unit can prioritize data that requires rapid processing. Thus, by determining the priority of data according to the user's emotions, important data can be preferentially received. 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 reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions.

[0079] When receiving data on types of pest birds, the reception unit can prioritize receiving highly relevant data, taking into account the user's geographical location information. For example, the reception unit can prioritize receiving pest bird data from the area where the user's farmland is located. The reception unit can also prioritize receiving pest bird data from neighboring areas based on the user's geographical location information. The reception unit can also prioritize receiving data from areas where specific pest birds are frequently found based on the user's geographical location information. This makes it possible to receive highly relevant data based on geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information into AI, which then selects the optimal data reception method.

[0080] When receiving data on the types of pest birds, the reception unit can analyze the user's social media activities and receive related data. For example, the reception unit receives data based on information about pest bird damage shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related pest bird data. The reception unit can also receive related pest bird data by referring to the activities of the user's friends on social media. This makes it possible to receive highly relevant data based on social media activities. Some or all of the above-mentioned processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's social media data into AI, which selects related data.

[0081] When receiving data on types of pest birds, the reception unit can adjust the reception method by reflecting the user's past feedback. The reception unit adjusts the data reception method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially accept data on specific pest birds based on the user's past feedback. The reception unit can also customize the data reception interface by referring to the user's past feedback. This makes it possible to receive data that is customized based on past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's feedback data into AI, which then selects the optimal data reception method.

[0082] The generation unit can estimate the user's emotions and adjust the method for generating the predator's cry based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can generate the cry at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate the cry quickly. Furthermore, if the user is excited, the generation AI can generate the cry with a visually stimulating effect. This enables the generation of an optimal cry 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, which then generates the optimal cry.

[0083] When generating the calls of natural enemies, the generation unit can determine the type of call to generate based on the activity hours of the pest birds. For example, if the pest birds are active at night, the generation unit generates the calls of nocturnal natural enemies. Furthermore, if the pest birds are active during the day, the generation unit can also generate the calls of diurnal natural enemies. Furthermore, the generation unit can generate the calls of different natural enemies depending on the activity hours of the pest birds. This makes it possible to generate optimal calls according to the activity hours of the pest birds. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the activity hours of the pest birds into the generation AI, which then generates the optimal calls.

[0084] When generating the calls of natural enemies, the generation unit can apply different generation algorithms depending on the habitat of the pest birds. For example, if the pest birds live in a forest, the generation unit applies an algorithm that generates calls of natural enemies that are suitable for forests. Furthermore, if the pest birds live in an urban area, the generation unit can also apply an algorithm that generates calls of natural enemies that are suitable for urban areas. Furthermore, the generation unit can also apply different generation algorithms depending on the habitat of the pest birds. This makes it possible to generate optimal calls that are suitable for the habitat of the pest birds. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs habitat data of the pest birds into the generation AI, which then generates the optimal calls.

[0085] When generating the calls of natural enemies, the generation unit can improve the accuracy of generation by referring to past generation results. For example, the generation unit analyzes the effects of calls generated in the past, and the generation AI improves the accuracy. The generation unit can also have the generation AI generate the optimal call based on past generation results. The generation unit can also have the generation AI adjust the generation algorithm by referring to past generation results. This makes it possible to generate highly accurate calls based on past generation results. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs past generation result data into the generation AI, and the generation AI generates the optimal call.

[0086] The generation unit can estimate the user's emotions and adjust the length of the generated cry based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can generate a longer cry. Furthermore, if the user is in a hurry, the generation unit can generate a shorter cry. Furthermore, if the user is excited, the generation AI can generate a cry with a visually stimulating effect. This allows the generation of an optimal cry length 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, which then generates the optimal cry.

[0087] When generating the calls of natural enemies, the generation unit can determine the order of the calls to be generated based on the breeding season of the pest birds. For example, the generation unit causes the generation AI to prioritize generating the calls of specific natural enemies according to the breeding season of the pest birds. The generation unit can also generate calls of natural enemies with different generation AIs based on the breeding season of the pest birds. The generation unit can also cause the generation AI to adjust the priority of the calls according to the breeding season of the pest birds. This makes it possible to generate optimal calls according to the breeding season of the pest birds. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs breeding season data of pest birds into the generation AI, and the generation AI generates the optimal calls.

[0088] When generating the calls of natural enemies, the generation unit can improve the accuracy of generation by referring to literature related to pest birds. For example, the generation unit refers to literature on the ecology of pest birds, and the generation AI generates the optimal call. The generation unit can also have the generation AI generate the call based on literature on the behavioral patterns of pest birds. The generation unit can also have the generation AI generate the call by referring to literature on the breeding seasons of pest birds. In this way, by referring to related literature, it becomes possible to generate calls with high accuracy. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs literature data related to pest birds into the generation AI, and the generation AI generates the optimal call.

[0089] When generating the calls of natural enemies, the generation unit can take into account the ecological information of pest birds. For example, the generation unit allows the generation AI to generate the optimal call based on the ecological information of the pest birds. The generation unit can also allow the generation AI to generate the call by taking into account the behavioral patterns of the pest birds. The generation unit can also allow the generation AI to generate the call by taking into account the breeding season of the pest birds. This makes it possible to generate more effective calls by taking into account the ecological information. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs ecological information data of pest birds into the generation AI, and the generation AI generates the optimal call.

[0090] The output unit can estimate the user's emotions and adjust the method of outputting the cries based on the estimated user emotions. For example, if the user is relaxed, the output unit causes the generation AI to output the cries at a leisurely pace. Furthermore, if the user is in a hurry, the output unit can also cause the generation AI to output the cries quickly. Furthermore, if the user is excited, the output unit can also output the cries with visually stimulating effects. This enables optimal cries to be output 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 may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without AI. For example, the output unit inputs the user's emotion data into the generation AI, which then outputs the optimal cries.

[0091] The output unit can determine the timing of outputting the bird calls based on the frequency of pest bird appearances when outputting the bird calls. For example, the output unit adjusts the timing of outputting the bird calls to match the time period when pest birds frequently appear. The output unit can also adjust the timing of outputting the bird calls based on the frequency of pest bird appearances. The output unit can also analyze the appearance patterns of pest birds and output the bird calls at the optimal timing. This enables optimal output timing based on the frequency of appearance. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs pest bird appearance frequency data into AI, which calculates the optimal output timing.

[0092] The output unit can optimize the drone's flight pattern when distributing bird calls to improve the effectiveness of the distribution. For example, the output unit can optimize the drone's flight pattern to effectively distribute bird calls over a wide area. The output unit can also adjust the drone's flight pattern to selectively cover areas where pest birds are frequently found. The output unit can also optimize the drone's flight pattern to effectively distribute bird calls while reducing battery consumption. This enables effective bird call distribution through optimized flight patterns. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs drone flight pattern data into AI, which then calculates the optimal flight pattern.

[0093] When outputting the bird calls, the output unit can improve the accuracy of the output by referring to past output results. For example, the output unit analyzes past output results to find an effective method for outputting the bird calls. The output unit can also adjust the output timing and method based on past output results. The output unit can also improve the accuracy of the output by referring to past output results. This enables highly accurate output of bird calls based on past results. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs past output result data into AI, which then calculates the optimal output method.

[0094] The output unit can estimate the user's emotions and adjust the volume of the cries to be output based on the estimated user emotions. For example, when the user is relaxed, the output unit causes the generation AI to output the cries at a moderate volume. Furthermore, when the user is in a hurry, the output unit can also cause the generation AI to output the cries at a higher volume. Furthermore, when the user is excited, the output unit can also output the cries at a volume that adds a visually stimulating effect. This allows the cries to be output at an optimal volume according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without AI. For example, the output unit inputs the user's emotion data into the generation AI, which then calculates the optimal volume.

[0095] When distributing the bird calls, the distributing unit can determine the range of distributing the calls based on the activity area of ​​the pest birds. For example, the distributing unit adjusts the distributing range of the calls to match the area where many pest birds appear. The distributing unit can also optimize the distributing range of the calls based on the activity area of ​​the pest birds. The distributing unit can also analyze the activity patterns of the pest birds and distribute the calls in an optimal range. This enables an optimal distributing range based on the activity area. Some or all of the above-mentioned processing in the distributing unit may be performed using AI, or may be performed without using AI. For example, the distributing unit inputs pest bird activity area data into AI, which then calculates the optimal distributing range.

[0096] The output unit can adjust the frequency of output of the cry taking into account the remaining battery level of the drone when outputting the cry. For example, the output unit adjusts the frequency of output of the cry according to the remaining battery level of the drone. Furthermore, the output unit can also reduce the frequency of output when the remaining battery level is low to save battery. Furthermore, the output unit can increase the frequency of output when the remaining battery level is sufficient to improve effectiveness. This enables the optimal output frequency according to the remaining battery level. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs remaining battery level data of the drone into AI, which then calculates the optimal output frequency.

[0097] The output unit can analyze the behavioral patterns of the pest birds when outputting their calls and determine the timing of the output. For example, the output unit analyzes the behavioral patterns of the pest birds and outputs their calls at the optimal timing. The output unit can also adjust the output timing based on the appearance patterns of the pest birds. The output unit can also optimize the output timing by referring to the behavioral patterns of the pest birds. This enables the optimal output timing based on the behavioral patterns. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit inputs behavioral pattern data of the pest birds into AI, which then calculates the optimal output timing.

[0098] The repelling unit can estimate the user's emotions and adjust the repelling method based on the estimated user's emotions. For example, if the user is relaxed, the generation AI of the repelling unit can select a relaxed repelling method. Furthermore, if the user is in a hurry, the generation AI of the repelling unit can select a rapid repelling method. Furthermore, if the user is excited, the generation AI of the repelling unit can select a repelling method that adds a visually stimulating effect. This enables the optimal repelling method to be tailored 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the repelling unit can be performed using AI, or can be performed without AI. For example, the repelling unit inputs the user's emotion data into the generation AI, which then calculates the optimal repelling method.

[0099] When repelling, the repelling unit can monitor the reaction of the pest birds in real time and determine the repelling means. For example, the repelling unit can monitor the reaction of the pest birds in real time and select the optimal repelling means. The repelling unit can also adjust the repelling means based on the reaction of the pest birds. The repelling unit can also monitor the behavior of the pest birds in real time and optimize the repelling means. This makes it possible to determine the optimal repelling means through real-time monitoring. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs reaction data of the pest birds into AI, which then calculates the optimal repelling means.

[0100] The repelling unit can apply different repelling means to each type of pest bird when repelling. For example, the repelling unit selects different repelling means depending on the type of pest bird. The repelling unit can also apply the most appropriate repelling means to each type of pest bird. The repelling unit can also adjust the repelling means based on the type of pest bird. This makes it possible to use the most appropriate repelling means depending on the type of pest bird. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs data on the type of pest bird into AI, which then calculates the most appropriate repelling means.

[0101] When chasing, the chasing unit can improve the accuracy of the chasing by referring to past chasing results. The chasing unit, for example, analyzes past chasing results and finds the optimal chasing means. The chasing unit can also adjust the chasing means based on past chasing results. The chasing unit can also improve the accuracy of the chasing by referring to past chasing results. This enables highly accurate chasing based on past results. Some or all of the above-mentioned processing in the chasing unit may be performed using AI, or may be performed without using AI. For example, the chasing unit inputs past chasing result data into AI, and the AI ​​calculates the optimal chasing means.

[0102] The repelling unit can estimate the user's emotions and prioritize repelling methods based on the estimated user emotions. For example, if the user is relaxed, the repelling unit can prioritize methods that the generation AI uses to repel the user at a leisurely pace. Furthermore, if the user is in a hurry, the repelling unit can prioritize methods that the generation AI uses to repel the user quickly. Furthermore, if the user is excited, the repelling unit can prioritize methods that the generation AI uses to repel the user with visually stimulating effects. This enables optimal prioritization of repelling methods 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the repelling unit can be performed using AI, or can be performed without AI. For example, the repelling unit inputs the user's emotion data into the generation AI, which then calculates the optimal repelling method.

[0103] When repelling, the repelling unit can determine the repelling means taking into account the geographical distribution of nuisance birds. For example, the repelling unit selects the optimal repelling means based on the geographical distribution of nuisance birds. The repelling unit can also adjust the repelling means according to the distribution area of ​​nuisance birds. The repelling unit can also select the repelling means with reference to the geographical distribution of nuisance birds. This makes it possible to determine the optimal repelling means based on the geographical distribution. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs geographical distribution data of nuisance birds into AI, which then calculates the optimal repelling means.

[0104] When repelling, the repelling unit can improve the accuracy of repelling by referring to literature related to nuisance birds. The repelling unit, for example, refers to literature on the ecology of nuisance birds and selects the optimal repelling means. The repelling unit can also adjust the repelling means based on literature on the behavioral patterns of nuisance birds. The repelling unit can also select the repelling means by referring to literature on the breeding season of nuisance birds. In this way, by referring to the related literature, highly accurate repelling is possible. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs literature data related to nuisance birds into AI, which then calculates the optimal repelling means.

[0105] When repelling, the repelling unit can analyze the behavioral patterns of the nuisance birds and determine the repelling means. For example, the repelling unit analyzes the behavioral patterns of the nuisance birds and selects the optimal repelling means. The repelling unit can also adjust the repelling means based on the behavioral patterns of the nuisance birds. The repelling unit can also optimize the repelling means by referring to the behavioral patterns of the nuisance birds. This makes it possible to determine the optimal repelling means based on the behavioral patterns. Some or all of the above-mentioned processing in the repelling unit may be performed using AI, or may be performed without using AI. For example, the repelling unit inputs behavioral pattern data of the nuisance birds into AI, which then calculates the optimal repelling means. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, outflow unit, and repelling unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives information about the type of pest bird entered by the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the calls of the pest bird's natural enemy using a generation AI. The outflow unit is realized by the speaker 40B of the smart device 14 and plays the generated calls from a speaker mounted on the drone. The repelling unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the volume and playback frequency of the calls to repel pest birds. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, outflow unit, and repelling unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives information about the type of pest bird entered by the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the calls of the pest bird's natural enemy using a generation AI. The outflow unit is realized by the speaker 240 of the smart glasses 214 and plays the generated calls from a speaker mounted on the drone. The repelling unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the volume and playback frequency of the calls to repel pest birds. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, emission unit, and repelling unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives information about the type of pest bird entered by the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the calls of natural enemies of pest birds using a generation AI. The emission unit is realized by the speaker 240 of the headset-type terminal 314 and plays the generated calls from a speaker mounted on the drone. The repelling unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the volume and playback frequency of the calls to repel pest birds. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, outflow unit, and repelling unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information about the type of pest bird entered by the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the calls of natural enemies of pest birds using a generation AI. The outflow unit is realized by the speaker 240 of the robot 414 and plays the generated calls from a speaker mounted on the drone. The repelling unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the volume and playback frequency of the calls to repel pest birds.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit not only receives data on the types of pest birds as input, but can also filter the data based on the type and growth stage of the user's crops. For example, if the user is growing tomatoes, the reception unit will preferentially receive data on pest birds that are particularly harmful to tomatoes. Furthermore, if the crops are in their growing season, the reception unit can also preferentially receive data on pest birds that are particularly susceptible to damage during the growing season. Furthermore, if the crops are in their harvest season, the reception unit can also preferentially receive data on pest birds that are particularly susceptible to damage during the harvest season. This makes it possible to receive optimal data according to the type and growth stage of the crops.

[0108] The generator not only generates the calls of natural enemies according to the type of nuisance bird, but can also adjust the type and timing of the calls based on the behavioral patterns of the nuisance bird. For example, if the nuisance bird is active at night, the generator generates the calls of nocturnal natural enemies and plays the calls at night. In addition, if nuisance birds appear more frequently in a particular season, the generator can generate the calls of natural enemies that are appropriate for that season. Furthermore, the generator can adjust the playback frequency and volume of the calls based on the behavioral patterns of the nuisance bird. This makes it possible to generate optimal calls according to the behavioral patterns of the nuisance bird.

[0109] The outflow unit not only broadcasts the bird calls generated from the drone's onboard speaker, but also optimizes the drone's flight pattern to maximize the effectiveness of the calls. For example, the outflow unit not only broadcasts bird calls while the drone flies to cover the entire farmland, but also adjusts the flight pattern to focus on areas where pest birds are most prevalent. The outflow unit can also optimize the flight pattern taking into account the drone's remaining battery level, effectively broadcasting the bird calls while minimizing battery consumption. Furthermore, the outflow unit can adjust the drone's flight speed and altitude to maximize the effectiveness of the calls. This allows for an optimized flight pattern to effectively broadcast bird calls.

[0110] The repelling unit not only repels nuisance birds based on the generated bird calls, but also monitors the responses of the nuisance birds in real time and selects the optimal repelling method. For example, the repelling unit not only repels nuisance birds by adjusting the volume and playback frequency of the bird calls, but also monitors the responses of the nuisance birds and adjusts the type of bird calls and playback timing. The repelling unit can also apply different repelling methods to different types of nuisance birds to achieve optimal repelling effects. Furthermore, the repelling unit can analyze the behavioral patterns of the nuisance birds and select the optimal repelling method. This makes it possible to select the optimal repelling method through real-time monitoring.

[0111] The generation unit can not only generate the cry of a hawk for a crow, but also apply different generation algorithms depending on the habitat of the pest bird. For example, if the pest bird lives in a forest, the generation unit applies an algorithm that generates the cry of a natural enemy that is suitable for forests. Also, if the pest bird lives in an urban area, the generation unit can apply an algorithm that generates the cry of a natural enemy that is suitable for urban areas. Furthermore, the generation unit can apply different generation algorithms depending on the habitat of the pest bird. This makes it possible to generate the optimal cry depending on the habitat of the pest bird.

[0112] The reception unit not only estimates the user's emotions and adjusts the timing of receiving data related to the types of pest birds based on the estimated user's emotions, but also determines the priority of data according to the user's emotions. For example, when the user is feeling stressed, the reception unit postpones data of low importance and prioritizes receiving data of high importance. Furthermore, when the user is relaxed, the reception unit can equally accept all data. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving data that requires quick processing. In this way, by determining the priority of data according to the user's emotions, important data can be preferentially accepted.

[0113] The generation unit estimates the user's emotions and adjusts the generation method of the predator's cry based on the estimated user's emotions, as well as the length and volume of the cry to be generated. For example, if the user is relaxed, the generation AI generates a longer cry at a moderate volume. If the user is in a hurry, the generation unit can also generate a shorter cry at a higher volume. Furthermore, if the user is excited, the generation AI can generate a cry with a visually stimulating effect, thereby adding visual effects. This makes it possible to generate optimal cries according to the user's emotions.

[0114] The output unit estimates the user's emotions and adjusts not only the method of outputting the sounds based on the estimated user's emotions, but also the range and timing of the sounds' output. For example, if the user is relaxed, the output unit's generation AI can output the sounds at a leisurely pace over a wide area. Alternatively, if the user is in a hurry, the output unit can output the sounds quickly to focus on a specific area. Furthermore, if the user is excited, the output unit's generation AI can add visually stimulating effects to the sounds, adding visual effects as well. This enables the optimal output of sounds to match the user's emotions.

[0115] The repelling unit not only estimates the user's emotions and adjusts the repelling method based on the estimated user's emotions, but also determines the priority of repelling means. For example, if the user is relaxed, the repelling unit may prioritize a means for the generation AI to repel at a leisurely pace. Also, if the user is in a hurry, the repelling unit may prioritize a means for the generation AI to repel quickly. Furthermore, if the user is excited, the repelling unit may prioritize a repelling method with a visually stimulating effect. This enables optimal prioritization of repelling means according to the user's emotions.

[0116] The repelling unit not only estimates the user's emotions and adjusts the repelling means based on the estimated user's emotions, but also monitors the reactions of the nuisance birds in real time and selects the optimal repelling means. For example, the repelling unit not only adjusts the volume and playback frequency of the bird calls to repel nuisance birds, but also monitors the reactions of the nuisance birds and adjusts the type of bird calls and playback timing. The repelling unit can also apply different repelling means to each type of nuisance bird to achieve the optimal repelling effect. Furthermore, the repelling unit can analyze the behavior patterns of the nuisance birds and select the optimal repelling means. This makes it possible to select the optimal repelling means through real-time monitoring.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives data on the type of pest bird as input. For example, it receives information on the type of pest bird entered by the user. It is also possible to automatically detect the type of pest bird using a sensor. It is also possible to estimate the type of pest bird based on past data on pest bird damage. Step 2: The generator generates the sounds of the pest bird's natural enemy based on the data received by the receiver. For example, the generator AI can be used to generate the sound of a hawk for a crow. It can also generate the sound of an owl for a sparrow. It can also generate the sound of a hawk for a pigeon. Step 3: The output unit plays the bird calls generated by the generator from the drone's onboard speaker. For example, the drone can play the bird calls while flying to cover the entire farmland. It can also adjust the drone's flight pattern to focus on specific areas. Furthermore, the volume and playback frequency of the bird calls can be adjusted. Step 4: The repelling unit repels nuisance birds based on the bird calls played by the bird-playing unit. For example, the volume and frequency of the bird calls can be adjusted to repel nuisance birds. The system can also monitor the responses of nuisance birds in real time and select the optimal repelling method. Furthermore, different repelling methods can be applied to different types of nuisance birds.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 reception unit that receives data on the types of pest birds as input; a generator that generates the sounds of natural enemies of harmful birds based on the data received by the receiver; an output unit that outputs the sound generated by the generation unit from a speaker mounted on the drone; The device is provided with a repelling unit that repels harmful birds based on the sounds emitted by the outflow unit. A system characterized by:

2. The generation unit Generates the calls of natural enemies according to the type of pest bird 2. The system of claim 1.

3. The outflow portion is The generated sounds are played from the drone's onboard speaker.

2. The system of claim 1.

4. The chasing unit is Scare away nuisance birds based on the sounds they generate 2. The system of claim 1.

5. The generation unit Generates hawk calls to crows 2. The system of claim 1.

6. The outflow portion is Drones fly over the entire farmland and broadcast the generated sounds. The system of claim 1 .

7. The reception unit The system estimates the user's emotions and adjusts the timing of receiving data on the types of pest birds based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze past data on pest bird damage and select an effective method for receiving data 2. The system of claim 1.

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