system

Abstract

The system according to the embodiment aims to provide effective measures against a wide variety of harmful animals. [Solution] A system according to an embodiment includes a recognition unit, an analysis unit, an output unit, and an operation unit. The recognition unit recognizes vermin using a camera. The analysis unit analyzes the type of vermin recognized by the recognition unit. The output unit outputs a specific appearance or sound for the vermin identified by the analysis unit. The operation unit enables remote operation.

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 problem that pest control measures are only temporary and it is difficult to deal with a wide variety of pests.

[0005] The system according to the embodiment aims to provide effective measures against a wide variety of harmful animals. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, an analysis unit, an output unit, and an operation unit. The recognition unit recognizes vermin using a camera. The analysis unit analyzes the type of vermin recognized by the recognition unit. The output unit outputs a specific appearance or sound of the vermin identified by the analysis unit. The operation unit enables remote operation. [Effects of the Invention]

[0007] The system according to the embodiment can provide effective measures against a wide variety of pests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A digital scarecrow system according to an embodiment of the present invention is a system for reducing damage caused by pests on farms. This digital scarecrow system effectively repels pests by identifying them with a camera and outputting images and sounds that repel the pests. For example, the digital scarecrow system uses a camera to monitor the surrounding environment. The high-resolution camera can recognize pests day or night. Next, AI analyzes the type of pest detected by the camera. The AI ​​identifies the type of pest based on pre-trained data. Once the type of pest is identified, it outputs images and sounds that repel the pest. For example, wild boars are frightened by loud noises, and deer are scared by flashing specific lights. Furthermore, crows are frightened by projecting the image of a bird of prey. This encourages pests to avoid approaching fields and rice paddies. Furthermore, this digital scarecrow system can be remotely controlled, allowing farmers to check the situation in real time from their smartphones or computers and change settings as needed. This allows farmers to take effective measures against a wide variety of pests, potentially significantly reducing crop damage. This allows the digital scarecrow system to effectively reduce damage caused by pests on farms. For example, it is more cost-effective than traditional scarecrows and can be used for a long period of time. In addition, farmers can check the situation in real time and change settings as needed, allowing for quick and flexible response.

[0029] A digital scarecrow system according to an embodiment includes a recognition unit, an analysis unit, an output unit, and an operation unit. The recognition unit recognizes vermin using a camera. For example, the recognition unit recognizes vermin day or night using a high-resolution camera. The recognition unit can also recognize vermin at night using an infrared camera. For example, the recognition unit detects vermin even in the dark using an infrared camera. The analysis unit analyzes the type of vermin recognized by the recognition unit using AI. For example, the analysis unit identifies vermin such as wild boars, deer, and crows based on pre-trained data. For example, the analysis unit inputs images of vermin into an AI model to identify the type of vermin. The output unit outputs images and sounds that vermin identify by the analysis unit dislike. For example, the output unit scares wild boars by making loud noises and scares deer by flashing specific lights. The output unit also frightens crows by displaying the image of a bird of prey. For example, the output unit outputs loud sounds using a speaker and flashes specific lights using an LED light. The operation unit enables remote operation. The operation unit can check the situation in real time and change settings from, for example, a smartphone or a PC. For example, the operation unit accesses the digital scarecrow system via the Internet and changes settings. As a result, the digital scarecrow system according to the embodiment can effectively repel harmful animals and reduce damage to agricultural crops.

[0030] The recognition unit can recognize vermin even at night using an infrared camera. The infrared camera has, for example, high resolution and high sensitivity. The recognition unit can detect vermin even in the dark using an infrared camera. For example, the recognition unit can detect vermin that are active at night using an infrared camera. The recognition unit can also detect the body temperature of vermin using an infrared camera. For example, the recognition unit can detect the body temperature of vermin using an infrared camera and identify the vermin based on the body temperature. This allows vermin to be recognized even at night, enabling vermin control measures to be implemented 24 hours a day.

[0031] The output unit can flash a specific light. Examples of the specific light include an LED light and a strobe light. The output unit flashes the specific light using, for example, an LED light. For example, the output unit flashes the light at a specific frequency to frighten pests. The output unit can also flash a strong light using a strobe light. For example, the output unit flashes a strong light using a strobe light to repel pests. In this way, flashing the specific light can effectively repel pests.

[0032] The output unit can display the image of a bird of prey. The image of a bird of prey includes, for example, a hawk or an eagle. The output unit displays the image of the bird of prey using, for example, a display. For example, the output unit displays the image of a hawk to scare away crows. The output unit can also display the image of an eagle to frighten harmful animals. For example, the output unit displays the image of an eagle to scare away harmful animals. In this way, displaying the image of a bird of prey can frighten and drive away harmful animals.

[0033] The operation unit can check the status and change settings in real time from a smartphone or PC. The operation unit accesses the digital scarecrow system using, for example, a smartphone app or a PC web browser. For example, the operation unit can check the status and change settings in real time using a smartphone app. The operation unit can also access the digital scarecrow system using a PC web browser and change settings. For example, the operation unit can access the digital scarecrow system via the Internet and change settings. This enables remote operation, allowing the status to be checked in real time and settings to be changed as necessary.

[0034] The recognition unit can learn patterns of pest movement and predict its movements. The recognition unit learns patterns of pest movement using, for example, a machine learning algorithm. For example, the recognition unit predicts times when pests will appear based on past data and strengthens camera monitoring. The recognition unit can also install cameras in locations where pests are likely to take a specific route. For example, the recognition unit learns patterns of pest movement and installs cameras in locations where pests are likely to take a specific route. Furthermore, the recognition unit can predict the speed of pest movement and adjust the frame rate of the camera to make it easier to capture its movements. For example, the recognition unit predicts the speed of pest movement and adjusts the frame rate of the camera. In this way, more effective pest control measures can be implemented by learning and predicting patterns of pest movement.

[0035] The recognition unit can detect the body temperature of a vermin and identify the vermin based on changes in body temperature. The recognition unit can detect the body temperature of a vermin using, for example, thermography. For example, the recognition unit can detect the body temperature of a vermin in real time using an infrared camera. The recognition unit can also identify the type of vermin based on changes in body temperature and take appropriate measures. For example, the recognition unit can identify the type of vermin based on changes in body temperature and take appropriate measures. Furthermore, the recognition unit can accumulate body temperature data and identify vermin with specific body temperature patterns. For example, the recognition unit accumulates body temperature data and identifies vermin with specific body temperature patterns. This allows for more accurate vermin control measures by identifying vermin based on changes in body temperature.

[0036] The recognition unit can identify vermin by detecting their cries and performing audio analysis. The recognition unit, for example, uses a microphone to detect vermin cries in real time. For example, the recognition unit, for example, uses a microphone to detect vermin cries in real time. The recognition unit can also use audio analysis technology to analyze the cries pattern and identify the type of vermin. For example, the recognition unit can use audio analysis technology to analyze the cries pattern and identify the type of vermin. Furthermore, the recognition unit can accumulate cries data and identify vermin with specific cries patterns. For example, the recognition unit accumulates cries data and identifies vermin with specific cries patterns. This allows for more effective vermin control measures by identifying vermin based on their cries.

[0037] The recognition unit can detect vermin footprints and identify the vermin from the shape of the footprints. The recognition unit, for example, analyzes images of footprints taken by a camera and identifies the type of vermin. For example, the recognition unit can analyze images of footprints taken by a camera and identify the type of vermin. The recognition unit can also accumulate footprint shape data and identify vermin with a specific shape pattern. For example, the recognition unit accumulates footprint shape data and identifies vermin with a specific shape pattern. Furthermore, the recognition unit can estimate the weight and size of the vermin based on the size and depth of the footprints. For example, the recognition unit estimates the weight and size of the vermin based on the size and depth of the footprints. This allows for more accurate vermin control measures to be taken by identifying vermin based on the shape of the footprints.

[0038] The recognition unit can detect the coat color of a vermin and identify the vermin from the coat color pattern. For example, the recognition unit can analyze the coat color of a vermin photographed by a camera and identify the type. For example, the recognition unit can analyze the coat color of a vermin photographed by a camera and identify the type. The recognition unit can also accumulate coat color pattern data and identify vermin with specific patterns. For example, the recognition unit can accumulate coat color pattern data and identify vermin with specific patterns. Furthermore, the recognition unit can predict the behavior of vermin in response to changes in seasons and environment based on changes in coat color. For example, the recognition unit can predict the behavior of vermin in response to changes in seasons and environment based on changes in coat color. This allows for more accurate vermin control measures to be taken by identifying vermin based on coat color patterns.

[0039] The recognition unit can learn the time periods during which vermin are active and improve recognition accuracy during specific time periods. The recognition unit, for example, collects data for each time period and learns the time periods during which vermin are active. For example, the recognition unit learns the time periods during which vermin appear and improves the recognition accuracy of the camera during those time periods. The recognition unit can also accumulate data on the time periods during which vermin are active and analyze patterns in which vermin appear during specific time periods. For example, the recognition unit accumulates data on the time periods during which vermin are active and analyzes patterns in which vermin appear during specific time periods. Furthermore, the recognition unit can also adjust the camera monitoring schedule to match the time periods during which vermin are active. For example, the recognition unit adjusts the camera monitoring schedule to match the time periods during which vermin are active. In this way, by learning the time periods during which vermin are active and improving recognition accuracy, more effective measures against vermin are possible.

[0040] The analysis unit can analyze the behavioral history of vermin and identify behavioral patterns. The analysis unit, for example, identifies the appearance pattern of vermin based on past behavioral history. For example, the analysis unit identifies the appearance pattern of vermin based on past behavioral history. The analysis unit can also accumulate behavioral history data and identify vermin with specific behavioral patterns. For example, the analysis unit accumulates behavioral history data and identify vermin with specific behavioral patterns. Furthermore, the analysis unit can analyze the behavioral history and predict locations where vermin are likely to appear. For example, the analysis unit analyzes the behavioral history and predicts locations where vermin are likely to appear. In this way, by analyzing the behavioral history and identifying behavioral patterns, more effective measures against vermin are possible.

[0041] The analysis unit can identify areas where pests live and propose countermeasures for each area. The analysis unit can identify areas where pests live using, for example, GPS data or a geographic information system (GIS). For example, the analysis unit can identify areas where pests live using GPS data. The analysis unit can also accumulate habitat data and identify pests that appear in specific areas. For example, the analysis unit can accumulate habitat data and identify pests that appear in specific areas. The analysis unit can also customize countermeasures for each area based on the habitat. For example, the analysis unit customizes countermeasures for each area based on the habitat. This allows for more effective pest control by identifying habitats and proposing countermeasures for each area.

[0042] The analysis unit can analyze the feeding habits of pests and propose countermeasures based on the feeding habits. The analysis unit, for example, analyzes the type of food and feeding behavior of pests. For example, the analysis unit analyzes the type of food of pests and proposes countermeasures based on the feeding habits. The analysis unit can also accumulate feeding habits data and identify pests with specific feeding habits. For example, the analysis unit accumulates feeding habits data and identify pests with specific feeding habits. Furthermore, the analysis unit can also propose countermeasures to avoid crops that pests prefer based on the feeding habits. For example, the analysis unit proposes countermeasures to avoid crops that pests prefer based on the feeding habits. In this way, by analyzing feeding habits and proposing countermeasures, more effective pest control becomes possible.

[0043] The analysis unit can identify the breeding season of pests and propose countermeasures according to the breeding season. The analysis unit can identify the breeding season of pests, for example, based on seasonal data and behavioral observations. For example, the analysis unit can identify the breeding season of pests based on seasonal data. The analysis unit can also accumulate breeding season data and identify pests with specific breeding seasons. For example, the analysis unit can accumulate breeding season data and identify pests with specific breeding seasons. The analysis unit can also customize countermeasures according to the breeding season based on the breeding season. For example, the analysis unit customizes countermeasures according to the breeding season based on the breeding season. This makes it possible to identify breeding seasons and propose countermeasures, thereby enabling more effective pest control measures.

[0044] The analysis unit can identify the movement path of the vermin and propose countermeasures based on the movement path. The analysis unit can identify the movement path of the vermin using, for example, a tracking device or movement pattern analysis. For example, the analysis unit can identify the movement path of the vermin using a tracking device. The analysis unit can also accumulate movement path data and identify vermin with specific movement paths. For example, the analysis unit accumulates movement path data and identifies vermin with specific movement paths. Furthermore, the analysis unit can customize countermeasures according to the movement path based on the movement path. For example, the analysis unit customizes countermeasures according to the movement path based on the movement path. In this way, more effective countermeasures against vermin can be implemented by identifying the movement path and proposing countermeasures.

[0045] The analysis unit can identify the size of a vermin herd and propose countermeasures according to the size of the herd. The analysis unit can identify the size of a vermin herd, for example, by using an individual counting method or observing the behavior of the herd. For example, the analysis unit can analyze video captured by a camera to identify the size of a vermin herd. The analysis unit can also accumulate herd size data and identify vermin of a specific size. For example, the analysis unit can accumulate herd size data and identify vermin of a specific size. Furthermore, the analysis unit can customize countermeasures according to the herd size based on the herd size. For example, the analysis unit customizes countermeasures according to the herd size based on the herd size. In this way, more effective countermeasures against vermin can be implemented by identifying the size of the herd and proposing countermeasures.

[0046] The output unit can output a combination of different sounds and lights depending on the type of pest. For example, the output unit outputs a combination of loud sounds and strong lights for wild boars. For example, the output unit outputs a loud sound using a speaker and flashes a strong light using an LED light. The output unit can also output a combination of sounds of a specific frequency and flashing lights for deer. For example, the output unit outputs sounds of a specific frequency and flashes lights using an LED light. Furthermore, the output unit can output a combination of the calls and shadows of birds of prey for crows. For example, the output unit outputs the calls of birds of prey using a speaker and displays the shadows of birds of prey using a display. This allows for more effective pest control by outputting a combination of different sounds and lights depending on the type of pest.

[0047] The output unit can adjust the intensity of the output according to the approaching distance of the vermin. The output unit, for example, measures the approaching distance of the vermin using a distance sensor. For example, the output unit measures the approaching distance of the vermin using a distance sensor and adjusts the intensity of the output according to that distance. The output unit can also measure the approaching distance of the vermin using laser measurement. For example, the output unit measures the approaching distance of the vermin using laser measurement and adjusts the intensity of the output according to that distance. Furthermore, the output unit can increase the intensity of the sound as the vermin approaches. For example, the output unit increases the intensity of the sound as the vermin approaches. The output unit can also decrease the intensity of the light as the vermin moves away. For example, the output unit decreases the intensity of the light as the vermin moves away. This allows for more effective vermin control by adjusting the intensity of the output according to the approaching distance.

[0048] The output unit can monitor the reaction of the vermin in real time and change the output content in accordance with the reaction. The output unit monitors the reaction of the vermin in real time using, for example, a camera or a sensor. For example, the output unit can monitor the movement of the vermin using a camera and change the output content in accordance with the reaction. The output unit can also monitor the behavior of the vermin using a sensor and change the output content in accordance with the reaction. For example, the output unit detects movement and analyzes behavior, and changes the output content in accordance with the reaction of the vermin. Furthermore, the output unit can increase the output of light if the vermin does not react to sound. For example, the output unit can increase the output of light if the vermin does not react to sound. The output unit can also increase the output of sound if the vermin does not react to light. For example, the output unit can increase the output of sound if the vermin does not react to light. In this way, by changing the output content in accordance with the reaction of the vermin, more effective vermin control becomes possible.

[0049] The output unit can project different images depending on the type of pest. For example, the output unit projects an image of a bird of prey onto a wild boar. For example, the output unit projects an image of a bird of prey using a display. The output unit can also project an image of a predator onto a deer. For example, the output unit projects an image of a predator using a display. Furthermore, the output unit can also project an image of a human figure onto a crow. For example, the output unit projects an image of a human figure using a display. This allows for more effective pest control measures by projecting different images depending on the type of pest.

[0050] The output unit can adjust the frequency of the output according to the approaching speed of the vermin. The output unit, for example, measures the approaching speed of the vermin using a speed sensor. For example, the output unit measures the approaching speed of the vermin using a speed sensor and adjusts the frequency of the output according to that speed. The output unit can also measure the approaching speed of the vermin using motion detection. For example, the output unit measures the approaching speed of the vermin using motion detection and adjusts the frequency of the output according to that speed. Furthermore, the output unit can increase the frequency of the output when the vermin approaches at high speed. For example, the output unit increases the frequency of the output when the vermin approaches at high speed. The output unit can also decrease the frequency of the output when the vermin approaches slowly. For example, the output unit decreases the frequency of the output when the vermin approaches slowly. In this way, by adjusting the frequency of the output according to the approaching speed, more effective measures against vermin are possible.

[0051] The output unit can record the pest's response and optimize the next output content. The output unit records the pest's response using, for example, a camera or a sensor. For example, the output unit can record the pest's movement using a camera and optimize the next output content based on the response. The output unit can also record the pest's behavior using a sensor and optimize the next output content based on the response. For example, the output unit detects movement and analyzes behavior, and optimizes the next output content based on the pest's response. Furthermore, if the pest responds to a specific sound, the output unit can use that sound again next time. For example, if the pest responds to a specific sound, the output unit can use that sound again next time. Furthermore, if the pest responds to a specific light, the output unit can use that light again next time. For example, if the pest responds to a specific light, the output unit can use that light again next time. In this way, by recording the pest's response and optimizing the next output content, more effective pest control measures are possible.

[0052] The operation unit can analyze the frequency of appearance of vermin and propose countermeasures according to the frequency. The operation unit can analyze the frequency of appearance of vermin using, for example, statistical analysis or data mining technology. For example, the operation unit can propose strong countermeasures when the frequency of appearance of vermin is high. The operation unit can also propose mild countermeasures when the frequency of appearance of vermin is low. For example, the operation unit can propose mild countermeasures when the frequency of appearance of vermin is low. Furthermore, the operation unit can accumulate appearance frequency data and identify vermin with specific frequency patterns. For example, the operation unit accumulates appearance frequency data and identifies vermin with specific frequency patterns. This allows for more effective vermin control by analyzing the frequency of appearance and proposing countermeasures.

[0053] The operation unit can analyze the time periods when vermin appear and propose countermeasures according to the time periods. The operation unit, for example, analyzes the time periods when vermin appear by collecting data for each time period or by using time series analysis. For example, if vermin appear at night, the operation unit proposes countermeasures suitable for nighttime. Furthermore, if vermin appear during the day, the operation unit can also propose countermeasures suitable for daytime. For example, if vermin appear during the day, the operation unit proposes countermeasures suitable for daytime. Furthermore, the operation unit can accumulate data on the time periods when vermin appear and identify vermin that appear during specific time periods. For example, the operation unit accumulates data on the time periods when vermin appear and identify vermin that appear during specific time periods. This allows for more effective vermin control by analyzing the time periods when vermin appear and proposing countermeasures.

[0054] The operation unit can be configured to set different countermeasures for each type of pest. For example, the operation unit can set a combination of sound and light for wild boars. For example, the operation unit can output a loud sound using a speaker and flash a strong light using an LED light. The operation unit can also set a specific frequency of sound and flashing light for deer. For example, the operation unit can output a specific frequency of sound and flash a light using an LED light. Furthermore, the operation unit can set the sound of a raptor and a shadow for crows. For example, the operation unit can output the sound of a raptor using a speaker and project a shadow of a raptor using a display. This allows more effective pest control by setting different countermeasures for each type of pest.

[0055] The operation unit can display the locations where vermin appear on a map and suggest countermeasures according to the location. The operation unit identifies the locations where vermin appear using, for example, GPS data or a geographic information system (GIS). For example, the operation unit displays the locations where vermin appear on a map using GPS data. The operation unit can also accumulate appearance location data and identify vermin that appear in specific locations. For example, the operation unit accumulates appearance location data and identifies vermin that appear in specific locations. Furthermore, the operation unit can customize countermeasures for each location based on the appearance locations. For example, the operation unit customizes countermeasures for each location based on the appearance locations. This enables more effective vermin control by displaying appearance locations on a map and suggesting countermeasures.

[0056] The operation unit can analyze the appearance patterns of pests and propose countermeasures according to the patterns. The operation unit can analyze the appearance patterns of pests using, for example, data mining technology or time series analysis. For example, the operation unit can analyze the appearance patterns of pests and propose countermeasures appropriate for the patterns. The operation unit can also accumulate appearance pattern data and identify pests with specific patterns. For example, the operation unit accumulates appearance pattern data and identifies pests with specific patterns. Furthermore, the operation unit can customize countermeasures for each pattern based on the appearance patterns. For example, the operation unit customizes countermeasures for each pattern based on the appearance patterns. In this way, by analyzing appearance patterns and proposing countermeasures, more effective pest control becomes possible.

[0057] The operation unit can store pest appearance data in the cloud so that it can be shared with other devices. The operation unit stores pest appearance data in the cloud, for example, using a cloud storage service or data synchronization technology. For example, the operation unit stores pest appearance data in the cloud so that it can be accessed from other devices. The operation unit can also upload the appearance data to the cloud in real time so that it can be shared immediately. For example, the operation unit uploads the appearance data to the cloud in real time so that it can be shared immediately. Furthermore, the operation unit can take measures in cooperation with other devices based on the data in the cloud. For example, the operation unit takes measures in cooperation with other devices based on the data in the cloud. In this way, by storing appearance data in the cloud and sharing it with other devices, more effective pest control measures are possible.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The recognition unit can learn and predict the behavioral patterns of pests. For example, the recognition unit can predict the time and location of pest appearances based on past data, and optimize the camera's monitoring range. The recognition unit can also install cameras in locations where pests are likely to follow a specific route. Furthermore, the recognition unit can predict the speed of pest movement and adjust the camera's frame rate to make it easier to capture their movements. This allows for more effective pest control measures by learning and predicting pest behavioral patterns.

[0060] The analysis unit can analyze the feeding habits of pests and propose countermeasures based on the feeding habits. For example, the analysis unit can analyze the types of food and feeding behavior of pests and propose countermeasures based on the feeding habits. The analysis unit can also accumulate feeding habits data and identify pests with specific feeding habits. Furthermore, the analysis unit can also propose countermeasures to avoid crops that pests prefer based on the feeding habits. In this way, by analyzing feeding habits and proposing countermeasures, more effective pest control becomes possible.

[0061] The output unit can adjust the intensity of the output according to the approaching distance of the vermin. For example, the output unit measures the approaching distance of the vermin using a distance sensor and adjusts the intensity of the output according to that distance. The output unit can also increase the intensity of the sound as the vermin approaches. Furthermore, the output unit can also decrease the intensity of the light as the vermin moves away. This allows for more effective vermin control by adjusting the intensity of the output according to the approaching distance.

[0062] The operation unit can analyze the frequency of pest appearances and propose countermeasures according to the frequency. For example, the operation unit can analyze the frequency of pest appearances using statistical analysis and data mining technology, and propose strong countermeasures if the frequency of appearance is high. It can also propose light countermeasures if the frequency of appearance is low. Furthermore, the operation unit can accumulate appearance frequency data and identify pests with specific frequency patterns. This allows for more effective pest control by analyzing appearance frequency and proposing countermeasures.

[0063] The operating unit can display the locations of pests on a map and suggest countermeasures appropriate to the location. For example, the operating unit can use GPS data or a geographic information system (GIS) to identify the locations of pests and display them on a map. It can also accumulate data on the locations of pests and identify pests that appear in specific locations. Furthermore, it can customize countermeasures for each location based on the locations of their appearance. This allows for more effective pest control by displaying the locations of pests on a map and suggesting countermeasures.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The recognition unit uses a camera to recognize pests. The recognition unit uses a high-resolution camera or an infrared camera, allowing it to recognize pests day or night. For example, an infrared camera can be used to detect pests even in the dark. Step 2: The analysis unit uses AI to analyze the type of pest recognized by the recognition unit. Based on pre-trained data, the analysis unit identifies pests such as wild boars, deer, and crows. For example, an image of a pest can be input into the AI ​​model to identify the type of pest. Step 3: The output unit outputs the images and sounds identified by the analysis unit that are disliked by the pests. For example, it makes a loud noise for wild boars, flashes a specific light for deer, and displays the image of a bird of prey for crows. The output unit outputs these images using speakers and LED lights. Step 4: The control unit enables remote control. The control unit allows you to check the status in real time and change settings from a smartphone or computer. For example, you can access the digital scarecrow system via the Internet and change the settings.

[0066] (Example 2) A digital scarecrow system according to an embodiment of the present invention is a system for reducing damage caused by pests on farms. This digital scarecrow system effectively repels pests by identifying them with a camera and outputting images and sounds that repel the pests. For example, the digital scarecrow system uses a camera to monitor the surrounding environment. The high-resolution camera can recognize pests day or night. Next, AI analyzes the type of pest detected by the camera. The AI ​​identifies the type of pest based on pre-trained data. Once the type of pest is identified, it outputs images and sounds that repel the pest. For example, wild boars are frightened by loud noises, and deer are scared by flashing specific lights. Furthermore, crows are frightened by projecting the image of a bird of prey. This encourages pests to avoid approaching fields and rice paddies. Furthermore, this digital scarecrow system can be remotely controlled, allowing farmers to check the situation in real time from their smartphones or computers and change settings as needed. This allows farmers to take effective measures against a wide variety of pests, potentially significantly reducing crop damage. This allows the digital scarecrow system to effectively reduce damage caused by pests on farms. For example, it is more cost-effective than traditional scarecrows and can be used for a long period of time. In addition, farmers can check the situation in real time and change settings as needed, allowing for quick and flexible response.

[0067] A digital scarecrow system according to an embodiment includes a recognition unit, an analysis unit, an output unit, and an operation unit. The recognition unit recognizes vermin using a camera. For example, the recognition unit recognizes vermin day or night using a high-resolution camera. The recognition unit can also recognize vermin at night using an infrared camera. For example, the recognition unit detects vermin even in the dark using an infrared camera. The analysis unit analyzes the type of vermin recognized by the recognition unit using AI. For example, the analysis unit identifies vermin such as wild boars, deer, and crows based on pre-trained data. For example, the analysis unit inputs images of vermin into an AI model to identify the type of vermin. The output unit outputs images and sounds that vermin identify by the analysis unit dislike. For example, the output unit scares wild boars by making loud noises and scares deer by flashing specific lights. The output unit also frightens crows by displaying the image of a bird of prey. For example, the output unit outputs loud sounds using a speaker and flashes specific lights using an LED light. The operation unit enables remote operation. The operation unit can check the situation in real time and change settings from, for example, a smartphone or a PC. For example, the operation unit accesses the digital scarecrow system via the Internet and changes settings. As a result, the digital scarecrow system according to the embodiment can effectively repel harmful animals and reduce damage to agricultural crops.

[0068] The recognition unit can recognize vermin even at night using an infrared camera. The infrared camera has, for example, high resolution and high sensitivity. The recognition unit can detect vermin even in the dark using an infrared camera. For example, the recognition unit can detect vermin that are active at night using an infrared camera. The recognition unit can also detect the body temperature of vermin using an infrared camera. For example, the recognition unit can detect the body temperature of vermin using an infrared camera and identify the vermin based on the body temperature. This allows vermin to be recognized even at night, enabling vermin control measures to be implemented 24 hours a day.

[0069] The output unit can flash a specific light. Examples of the specific light include an LED light and a strobe light. The output unit flashes the specific light using, for example, an LED light. For example, the output unit flashes the light at a specific frequency to frighten pests. The output unit can also flash a strong light using a strobe light. For example, the output unit flashes a strong light using a strobe light to repel pests. In this way, flashing the specific light can effectively repel pests.

[0070] The output unit can display the image of a bird of prey. The image of a bird of prey includes, for example, a hawk or an eagle. The output unit displays the image of the bird of prey using, for example, a display. For example, the output unit displays the image of a hawk to scare away crows. The output unit can also display the image of an eagle to frighten harmful animals. For example, the output unit displays the image of an eagle to scare away harmful animals. In this way, displaying the image of a bird of prey can frighten and drive away harmful animals.

[0071] The operation unit can check the status and change settings in real time from a smartphone or PC. The operation unit accesses the digital scarecrow system using, for example, a smartphone app or a PC web browser. For example, the operation unit can check the status and change settings in real time using a smartphone app. The operation unit can also access the digital scarecrow system using a PC web browser and change settings. For example, the operation unit can access the digital scarecrow system via the Internet and change settings. This enables remote operation, allowing the status to be checked in real time and settings to be changed as necessary.

[0072] The recognition unit can estimate the user's emotions and adjust the camera's recognition accuracy based on the estimated user's emotions. The recognition unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the recognition unit analyzes the user's facial expressions captured by the camera to estimate the user's emotions. The recognition unit can also estimate the user's emotions using voice analysis technology. For example, the recognition unit analyzes the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the recognition unit adjusts the camera's recognition accuracy based on the estimated user's emotions. For example, if the user is feeling stressed, the recognition unit increases the camera's recognition accuracy to quickly detect pests. Furthermore, if the user is relaxed, the recognition unit sets the camera's recognition accuracy to normal mode to reduce battery consumption. This allows for more appropriate pest control measures by adjusting the camera's recognition accuracy according to the user's emotions.

[0073] The recognition unit can learn patterns of pest movement and predict its movements. The recognition unit learns patterns of pest movement using, for example, a machine learning algorithm. For example, the recognition unit predicts times when pests will appear based on past data and strengthens camera monitoring. The recognition unit can also install cameras in locations where pests are likely to take a specific route. For example, the recognition unit learns patterns of pest movement and installs cameras in locations where pests are likely to take a specific route. Furthermore, the recognition unit can predict the speed of pest movement and adjust the frame rate of the camera to make it easier to capture its movements. For example, the recognition unit predicts the speed of pest movement and adjusts the frame rate of the camera. In this way, more effective pest control measures can be implemented by learning and predicting patterns of pest movement.

[0074] The recognition unit can detect the body temperature of a vermin and identify the vermin based on changes in body temperature. The recognition unit can detect the body temperature of a vermin using, for example, thermography. For example, the recognition unit can detect the body temperature of a vermin in real time using an infrared camera. The recognition unit can also identify the type of vermin based on changes in body temperature and take appropriate measures. For example, the recognition unit can identify the type of vermin based on changes in body temperature and take appropriate measures. Furthermore, the recognition unit can accumulate body temperature data and identify vermin with specific body temperature patterns. For example, the recognition unit accumulates body temperature data and identifies vermin with specific body temperature patterns. This allows for more accurate vermin control measures by identifying vermin based on changes in body temperature.

[0075] The recognition unit can identify vermin by detecting their cries and performing audio analysis. The recognition unit, for example, uses a microphone to detect vermin cries in real time. For example, the recognition unit, for example, uses a microphone to detect vermin cries in real time. The recognition unit can also use audio analysis technology to analyze the cries pattern and identify the type of vermin. For example, the recognition unit can use audio analysis technology to analyze the cries pattern and identify the type of vermin. Furthermore, the recognition unit can accumulate cries data and identify vermin with specific cries patterns. For example, the recognition unit accumulates cries data and identifies vermin with specific cries patterns. This allows for more effective vermin control measures by identifying vermin based on their cries.

[0076] The recognition unit can estimate the user's emotion and change the operation mode of the recognition unit based on the estimated user's emotion. The recognition unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the recognition unit analyzes the user's facial expression captured by a camera to estimate the user's emotion. The recognition unit can also estimate the user's emotion using voice analysis technology. For example, the recognition unit analyzes the tone and speed of the user's voice to estimate the user's emotion. Furthermore, the recognition unit changes the operation mode of the recognition unit based on the estimated user's emotion. For example, if the user is feeling stressed, the recognition unit switches the operation mode to high-precision mode. Also, if the user is relaxed, the recognition unit sets the operation mode to energy-saving mode. In this way, by changing the operation mode of the recognition unit according to the user's emotion, more appropriate pest control measures are possible.

[0077] The recognition unit can detect vermin footprints and identify the vermin from the shape of the footprints. The recognition unit, for example, analyzes images of footprints taken by a camera and identifies the type of vermin. For example, the recognition unit can analyze images of footprints taken by a camera and identify the type of vermin. The recognition unit can also accumulate footprint shape data and identify vermin with a specific shape pattern. For example, the recognition unit accumulates footprint shape data and identifies vermin with a specific shape pattern. Furthermore, the recognition unit can estimate the weight and size of the vermin based on the size and depth of the footprints. For example, the recognition unit estimates the weight and size of the vermin based on the size and depth of the footprints. This allows for more accurate vermin control measures to be taken by identifying vermin based on the shape of the footprints.

[0078] The recognition unit can detect the coat color of a vermin and identify the vermin from the coat color pattern. For example, the recognition unit can analyze the coat color of a vermin photographed by a camera and identify the type. For example, the recognition unit can analyze the coat color of a vermin photographed by a camera and identify the type. The recognition unit can also accumulate coat color pattern data and identify vermin with specific patterns. For example, the recognition unit can accumulate coat color pattern data and identify vermin with specific patterns. Furthermore, the recognition unit can predict the behavior of vermin in response to changes in seasons and environment based on changes in coat color. For example, the recognition unit can predict the behavior of vermin in response to changes in seasons and environment based on changes in coat color. This allows for more accurate vermin control measures to be taken by identifying vermin based on coat color patterns.

[0079] The recognition unit can learn the time periods during which vermin are active and improve recognition accuracy during specific time periods. The recognition unit, for example, collects data for each time period and learns the time periods during which vermin are active. For example, the recognition unit learns the time periods during which vermin appear and improves the recognition accuracy of the camera during those time periods. The recognition unit can also accumulate data on the time periods during which vermin are active and analyze patterns in which vermin appear during specific time periods. For example, the recognition unit accumulates data on the time periods during which vermin are active and analyzes patterns in which vermin appear during specific time periods. Furthermore, the recognition unit can also adjust the camera monitoring schedule to match the time periods during which vermin are active. For example, the recognition unit adjusts the camera monitoring schedule to match the time periods during which vermin are active. In this way, by learning the time periods during which vermin are active and improving recognition accuracy, more effective measures against vermin are possible.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the analysis unit analyzes the user's facial expressions captured by a camera to estimate the user's emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the analysis unit adjusts the display method of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. On the other hand, if the user is relaxed, the analysis unit provides a display method that includes detailed information. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions.

[0081] The analysis unit can analyze the behavioral history of vermin and identify behavioral patterns. The analysis unit, for example, identifies the appearance pattern of vermin based on past behavioral history. For example, the analysis unit identifies the appearance pattern of vermin based on past behavioral history. The analysis unit can also accumulate behavioral history data and identify vermin with specific behavioral patterns. For example, the analysis unit accumulates behavioral history data and identify vermin with specific behavioral patterns. Furthermore, the analysis unit can analyze the behavioral history and predict locations where vermin are likely to appear. For example, the analysis unit analyzes the behavioral history and predicts locations where vermin are likely to appear. In this way, by analyzing the behavioral history and identifying behavioral patterns, more effective measures against vermin are possible.

[0082] The analysis unit can identify areas where pests live and propose countermeasures for each area. The analysis unit can identify areas where pests live using, for example, GPS data or a geographic information system (GIS). For example, the analysis unit can identify areas where pests live using GPS data. The analysis unit can also accumulate habitat data and identify pests that appear in specific areas. For example, the analysis unit can accumulate habitat data and identify pests that appear in specific areas. The analysis unit can also customize countermeasures for each area based on the habitat. For example, the analysis unit customizes countermeasures for each area based on the habitat. This allows for more effective pest control by identifying habitats and proposing countermeasures for each area.

[0083] The analysis unit can analyze the feeding habits of pests and propose countermeasures based on the feeding habits. The analysis unit, for example, analyzes the type of food and feeding behavior of pests. For example, the analysis unit analyzes the type of food of pests and proposes countermeasures based on the feeding habits. The analysis unit can also accumulate feeding habits data and identify pests with specific feeding habits. For example, the analysis unit accumulates feeding habits data and identify pests with specific feeding habits. Furthermore, the analysis unit can also propose countermeasures to avoid crops that pests prefer based on the feeding habits. For example, the analysis unit proposes countermeasures to avoid crops that pests prefer based on the feeding habits. In this way, by analyzing feeding habits and proposing countermeasures, more effective pest control becomes possible.

[0084] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the analysis unit analyzes the user's facial expressions captured by a camera to estimate the user's emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the analysis unit determines the priority of analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit will prioritize displaying important analysis results. Also, if the user is relaxed, the analysis unit will display detailed analysis results. This allows for more appropriate information to be provided by determining the priority of analysis results according to the user's emotions.

[0085] The analysis unit can identify the breeding season of pests and propose countermeasures according to the breeding season. The analysis unit can identify the breeding season of pests, for example, based on seasonal data and behavioral observations. For example, the analysis unit can identify the breeding season of pests based on seasonal data. The analysis unit can also accumulate breeding season data and identify pests with specific breeding seasons. For example, the analysis unit can accumulate breeding season data and identify pests with specific breeding seasons. The analysis unit can also customize countermeasures according to the breeding season based on the breeding season. For example, the analysis unit customizes countermeasures according to the breeding season based on the breeding season. This makes it possible to identify breeding seasons and propose countermeasures, thereby enabling more effective pest control measures.

[0086] The analysis unit can identify the movement path of the vermin and propose countermeasures based on the movement path. The analysis unit can identify the movement path of the vermin using, for example, a tracking device or movement pattern analysis. For example, the analysis unit can identify the movement path of the vermin using a tracking device. The analysis unit can also accumulate movement path data and identify vermin with specific movement paths. For example, the analysis unit accumulates movement path data and identifies vermin with specific movement paths. Furthermore, the analysis unit can customize countermeasures according to the movement path based on the movement path. For example, the analysis unit customizes countermeasures according to the movement path based on the movement path. In this way, more effective countermeasures against vermin can be implemented by identifying the movement path and proposing countermeasures.

[0087] The analysis unit can identify the size of a vermin herd and propose countermeasures according to the size of the herd. The analysis unit can identify the size of a vermin herd, for example, by using an individual counting method or observing the behavior of the herd. For example, the analysis unit can analyze video captured by a camera to identify the size of a vermin herd. The analysis unit can also accumulate herd size data and identify vermin of a specific size. For example, the analysis unit can accumulate herd size data and identify vermin of a specific size. Furthermore, the analysis unit can customize countermeasures according to the herd size based on the herd size. For example, the analysis unit customizes countermeasures according to the herd size based on the herd size. In this way, more effective countermeasures against vermin can be implemented by identifying the size of the herd and proposing countermeasures.

[0088] The output unit can estimate the user's emotions and adjust the type of sound to be output based on the estimated user's emotions. The output unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the output unit analyzes the user's facial expressions captured by a camera to estimate the user's emotions. The output unit can also estimate the user's emotions using voice analysis technology. For example, the output unit analyzes the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the output unit adjusts the type of sound to be output based on the estimated user's emotions. For example, if the user is stressed, the output unit outputs a loud sound to pests. On the other hand, if the user is relaxed, the output unit outputs a gentle sound to pests. This allows for more effective pest control by adjusting the type of sound to be output according to the user's emotions.

[0089] The output unit can output a combination of different sounds and lights depending on the type of pest. For example, the output unit outputs a combination of loud sounds and strong lights for wild boars. For example, the output unit outputs a loud sound using a speaker and flashes a strong light using an LED light. The output unit can also output a combination of sounds of a specific frequency and flashing lights for deer. For example, the output unit outputs sounds of a specific frequency and flashes lights using an LED light. Furthermore, the output unit can output a combination of the calls and shadows of birds of prey for crows. For example, the output unit outputs the calls of birds of prey using a speaker and displays the shadows of birds of prey using a display. This allows for more effective pest control by outputting a combination of different sounds and lights depending on the type of pest.

[0090] The output unit can adjust the intensity of the output according to the approaching distance of the vermin. The output unit, for example, measures the approaching distance of the vermin using a distance sensor. For example, the output unit measures the approaching distance of the vermin using a distance sensor and adjusts the intensity of the output according to that distance. The output unit can also measure the approaching distance of the vermin using laser measurement. For example, the output unit measures the approaching distance of the vermin using laser measurement and adjusts the intensity of the output according to that distance. Furthermore, the output unit can increase the intensity of the sound as the vermin approaches. For example, the output unit increases the intensity of the sound as the vermin approaches. The output unit can also decrease the intensity of the light as the vermin moves away. For example, the output unit decreases the intensity of the light as the vermin moves away. This allows for more effective vermin control by adjusting the intensity of the output according to the approaching distance.

[0091] The output unit can monitor the reaction of the vermin in real time and change the output content in accordance with the reaction. The output unit monitors the reaction of the vermin in real time using, for example, a camera or a sensor. For example, the output unit can monitor the movement of the vermin using a camera and change the output content in accordance with the reaction. The output unit can also monitor the behavior of the vermin using a sensor and change the output content in accordance with the reaction. For example, the output unit detects movement and analyzes behavior, and changes the output content in accordance with the reaction of the vermin. Furthermore, the output unit can increase the output of light if the vermin does not react to sound. For example, the output unit can increase the output of light if the vermin does not react to sound. The output unit can also increase the output of sound if the vermin does not react to light. For example, the output unit can increase the output of sound if the vermin does not react to light. In this way, by changing the output content in accordance with the reaction of the vermin, more effective vermin control becomes possible.

[0092] The output unit can estimate the user's emotions and adjust the output light pattern based on the estimated user's emotions. The output unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the output unit analyzes the user's facial expressions captured by a camera to estimate the user's emotions. The output unit can also estimate the user's emotions using voice analysis technology. For example, the output unit analyzes the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the output unit adjusts the output light pattern based on the estimated user's emotions. For example, if the user is stressed, the output unit outputs a strong flashing light. On the other hand, if the user is relaxed, the output unit outputs a gentle light pattern. This allows for more effective pest control by adjusting the output light pattern according to the user's emotions.

[0093] The output unit can project different images depending on the type of pest. For example, the output unit projects an image of a bird of prey onto a wild boar. For example, the output unit projects an image of a bird of prey using a display. The output unit can also project an image of a predator onto a deer. For example, the output unit projects an image of a predator using a display. Furthermore, the output unit can also project an image of a human figure onto a crow. For example, the output unit projects an image of a human figure using a display. This allows for more effective pest control measures by projecting different images depending on the type of pest.

[0094] The output unit can adjust the frequency of the output according to the approaching speed of the vermin. The output unit, for example, measures the approaching speed of the vermin using a speed sensor. For example, the output unit measures the approaching speed of the vermin using a speed sensor and adjusts the frequency of the output according to that speed. The output unit can also measure the approaching speed of the vermin using motion detection. For example, the output unit measures the approaching speed of the vermin using motion detection and adjusts the frequency of the output according to that speed. Furthermore, the output unit can increase the frequency of the output when the vermin approaches at high speed. For example, the output unit increases the frequency of the output when the vermin approaches at high speed. The output unit can also decrease the frequency of the output when the vermin approaches slowly. For example, the output unit decreases the frequency of the output when the vermin approaches slowly. In this way, by adjusting the frequency of the output according to the approaching speed, more effective measures against vermin are possible.

[0095] The output unit can record the pest's response and optimize the next output content. The output unit records the pest's response using, for example, a camera or a sensor. For example, the output unit can record the pest's movement using a camera and optimize the next output content based on the response. The output unit can also record the pest's behavior using a sensor and optimize the next output content based on the response. For example, the output unit detects movement and analyzes behavior, and optimizes the next output content based on the pest's response. Furthermore, if the pest responds to a specific sound, the output unit can use that sound again next time. For example, if the pest responds to a specific sound, the output unit can use that sound again next time. Furthermore, if the pest responds to a specific light, the output unit can use that light again next time. For example, if the pest responds to a specific light, the output unit can use that light again next time. In this way, by recording the pest's response and optimizing the next output content, more effective pest control measures are possible.

[0096] The operation unit can estimate the user's emotion and adjust the layout of the operation screen based on the estimated user's emotion. The operation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the operation unit analyzes the user's facial expression captured by a camera to estimate the user's emotion. The operation unit can also estimate the user's emotion using voice analysis technology. For example, the operation unit analyzes the tone and speed of the user's voice to estimate the user's emotion. Furthermore, the operation unit adjusts the layout of the operation screen based on the estimated user's emotion. For example, if the user is feeling stressed, the operation unit provides a simple, highly visible layout. On the other hand, if the user is relaxed, the operation unit provides a layout including detailed information. In this way, by adjusting the layout of the operation screen according to the user's emotion, a more user-friendly interface can be provided.

[0097] The operation unit can analyze the frequency of appearance of vermin and propose countermeasures according to the frequency. The operation unit can analyze the frequency of appearance of vermin using, for example, statistical analysis or data mining technology. For example, the operation unit can propose strong countermeasures when the frequency of appearance of vermin is high. The operation unit can also propose mild countermeasures when the frequency of appearance of vermin is low. For example, the operation unit can propose mild countermeasures when the frequency of appearance of vermin is low. Furthermore, the operation unit can accumulate appearance frequency data and identify vermin with specific frequency patterns. For example, the operation unit accumulates appearance frequency data and identifies vermin with specific frequency patterns. This allows for more effective vermin control by analyzing the frequency of appearance and proposing countermeasures.

[0098] The operation unit can analyze the time periods when vermin appear and propose countermeasures according to the time periods. The operation unit, for example, analyzes the time periods when vermin appear by collecting data for each time period or by using time series analysis. For example, if vermin appear at night, the operation unit proposes countermeasures suitable for nighttime. Furthermore, if vermin appear during the day, the operation unit can also propose countermeasures suitable for daytime. For example, if vermin appear during the day, the operation unit proposes countermeasures suitable for daytime. Furthermore, the operation unit can accumulate data on the time periods when vermin appear and identify vermin that appear during specific time periods. For example, the operation unit accumulates data on the time periods when vermin appear and identify vermin that appear during specific time periods. This allows for more effective vermin control by analyzing the time periods when vermin appear and proposing countermeasures.

[0099] The operation unit can be configured to set different countermeasures for each type of pest. For example, the operation unit can set a combination of sound and light for wild boars. For example, the operation unit can output a loud sound using a speaker and flash a strong light using an LED light. The operation unit can also set a specific frequency of sound and flashing light for deer. For example, the operation unit can output a specific frequency of sound and flash a light using an LED light. Furthermore, the operation unit can set the sound of a raptor and a shadow for crows. For example, the operation unit can output the sound of a raptor using a speaker and project a shadow of a raptor using a display. This allows more effective pest control by setting different countermeasures for each type of pest.

[0100] The operation unit can estimate the user's emotions and simplify the operation procedure based on the estimated user's emotions. The operation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the operation unit analyzes the user's facial expressions captured by a camera to estimate the user's emotions. The operation unit can also estimate the user's emotions using voice analysis technology. For example, the operation unit analyzes the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the operation unit simplifies the operation procedure based on the estimated user's emotions. For example, if the user is feeling stressed, the operation unit minimizes the operation procedure. On the other hand, if the user is relaxed, the operation unit provides detailed operation procedures. This makes it possible to provide a more user-friendly interface by simplifying the operation procedure according to the user's emotions.

[0101] The operation unit can display the locations where vermin appear on a map and suggest countermeasures according to the location. The operation unit identifies the locations where vermin appear using, for example, GPS data or a geographic information system (GIS). For example, the operation unit displays the locations where vermin appear on a map using GPS data. The operation unit can also accumulate appearance location data and identify vermin that appear in specific locations. For example, the operation unit accumulates appearance location data and identifies vermin that appear in specific locations. Furthermore, the operation unit can customize countermeasures for each location based on the appearance locations. For example, the operation unit customizes countermeasures for each location based on the appearance locations. This enables more effective vermin control by displaying appearance locations on a map and suggesting countermeasures.

[0102] The operation unit can analyze the appearance patterns of pests and propose countermeasures according to the patterns. The operation unit can analyze the appearance patterns of pests using, for example, data mining technology or time series analysis. For example, the operation unit can analyze the appearance patterns of pests and propose countermeasures appropriate for the patterns. The operation unit can also accumulate appearance pattern data and identify pests with specific patterns. For example, the operation unit accumulates appearance pattern data and identifies pests with specific patterns. Furthermore, the operation unit can customize countermeasures for each pattern based on the appearance patterns. For example, the operation unit customizes countermeasures for each pattern based on the appearance patterns. In this way, by analyzing appearance patterns and proposing countermeasures, more effective pest control becomes possible.

[0103] The operation unit can store pest appearance data in the cloud so that it can be shared with other devices. The operation unit stores pest appearance data in the cloud, for example, using a cloud storage service or data synchronization technology. For example, the operation unit stores pest appearance data in the cloud so that it can be accessed from other devices. The operation unit can also upload the appearance data to the cloud in real time so that it can be shared immediately. For example, the operation unit uploads the appearance data to the cloud in real time so that it can be shared immediately. Furthermore, the operation unit can take measures in cooperation with other devices based on the data in the cloud. For example, the operation unit takes measures in cooperation with other devices based on the data in the cloud. In this way, by storing appearance data in the cloud and sharing it with other devices, more effective pest control measures are possible. === Hard Collateral 1-1 === Each of the multiple elements, including the recognition unit, analysis unit, output unit, and operation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit recognizes vermin using the camera 42 of the smart device 14 and detects the vermin day or night using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the type of vermin using an AI model. The output unit outputs images and sounds that vermin dislike, for example, using the speaker 40B or LED light of the smart device 14. The operation unit enables remote operation from a smartphone or PC via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the recognition unit, analysis unit, output unit, and operation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit recognizes vermin using the camera 42 of the smart glasses 214 and detects vermin day or night using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the type of vermin using an AI model. The output unit outputs images and sounds that vermin dislike, for example, using the speaker 240 or LED light of the smart glasses 214. The operation unit enables remote operation from a smartphone or PC via the control unit 46A of the smart glasses 214, for example. === Hard Collateral 1-3 === Each of the multiple elements including the recognition unit, analysis unit, output unit, and operation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the recognition unit recognizes vermin using the camera 42 of the headset terminal 314 and detects the vermin day or night using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the type of vermin using an AI model. The output unit outputs images and sounds that vermin dislike, for example, using the speaker 240 or LED light of the headset terminal 314. The operation unit enables remote operation from a smartphone or PC via the control unit 46A of the headset terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the recognition unit, analysis unit, output unit, and operation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit recognizes vermin using the camera 42 of the robot 414 and detects the vermin day or night using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the type of vermin using an AI model. The output unit outputs images and sounds that vermin dislike, for example, using the speaker 240 or LED light of the robot 414. The operation unit enables remote control from a smartphone or PC via the control unit 46A of the robot 414, for example.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The recognition unit can learn and predict the behavioral patterns of pests. For example, the recognition unit can predict the time and location of pest appearances based on past data, and optimize the camera's monitoring range. The recognition unit can also install cameras in locations where pests are likely to follow a specific route. Furthermore, the recognition unit can predict the speed of pest movement and adjust the camera's frame rate to make it easier to capture their movements. This allows for more effective pest control measures by learning and predicting pest behavioral patterns.

[0106] The analysis unit can analyze the feeding habits of pests and propose countermeasures based on the feeding habits. For example, the analysis unit can analyze the types of food and feeding behavior of pests and propose countermeasures based on the feeding habits. The analysis unit can also accumulate feeding habits data and identify pests with specific feeding habits. Furthermore, the analysis unit can also propose countermeasures to avoid crops that pests prefer based on the feeding habits. In this way, by analyzing feeding habits and proposing countermeasures, more effective pest control becomes possible.

[0107] The output unit can adjust the intensity of the output according to the approaching distance of the vermin. For example, the output unit measures the approaching distance of the vermin using a distance sensor and adjusts the intensity of the output according to that distance. The output unit can also increase the intensity of the sound as the vermin approaches. Furthermore, the output unit can also decrease the intensity of the light as the vermin moves away. This allows for more effective vermin control by adjusting the intensity of the output according to the approaching distance.

[0108] The operation unit can analyze the frequency of pest appearances and propose countermeasures according to the frequency. For example, the operation unit can analyze the frequency of pest appearances using statistical analysis and data mining technology, and propose strong countermeasures if the frequency of appearance is high. It can also propose light countermeasures if the frequency of appearance is low. Furthermore, the operation unit can accumulate appearance frequency data and identify pests with specific frequency patterns. This allows for more effective pest control by analyzing appearance frequency and proposing countermeasures.

[0109] The operating unit can display the locations of pests on a map and suggest countermeasures appropriate to the location. For example, the operating unit can use GPS data or a geographic information system (GIS) to identify the locations of pests and display them on a map. It can also accumulate data on the locations of pests and identify pests that appear in specific locations. Furthermore, it can customize countermeasures for each location based on the locations of their appearance. This allows for more effective pest control by displaying the locations of pests on a map and suggesting countermeasures.

[0110] The recognition unit can estimate the user's emotions and adjust the camera's recognition accuracy based on the estimated user's emotions. For example, the recognition unit can estimate the user's emotions using facial expression recognition technology and increase the camera's recognition accuracy if the user is feeling stressed. Furthermore, if the user is relaxed, the recognition unit can set the camera's recognition accuracy to normal mode to reduce battery consumption. This allows for more appropriate pest control measures by adjusting the camera's recognition accuracy according to the user's emotions.

[0111] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology and provide a simple, highly visible display method if the user is feeling stressed. Alternatively, it can provide a display method that includes detailed information if the user is relaxed. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions.

[0112] The output unit can estimate the user's emotions and adjust the type of sound to be output based on the estimated user's emotions. For example, the output unit can estimate the user's emotions using facial expression recognition technology, and output a loud sound to pests if the user is feeling stressed. On the other hand, the output unit can output a gentle sound to pests if the user is relaxed. This allows for more effective pest control by adjusting the type of sound to be output according to the user's emotions.

[0113] The operation unit can estimate the user's emotions and adjust the layout of the operation screen based on the estimated user emotions. For example, the operation unit can estimate the user's emotions using facial expression recognition technology and provide a simple, highly visible layout when the user is feeling stressed. On the other hand, it can provide a layout including detailed information when the user is relaxed. In this way, by adjusting the layout of the operation screen according to the user's emotions, it is possible to provide a more user-friendly interface.

[0114] The operation unit can estimate the user's emotions and simplify the operation procedures based on the estimated user emotions. For example, the operation unit can estimate the user's emotions using facial expression recognition technology and minimize the operation procedures if the user is feeling stressed. On the other hand, the operation unit can provide detailed operation procedures if the user is relaxed. This simplifies the operation procedures according to the user's emotions, making it possible to provide a more user-friendly interface.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The recognition unit uses a camera to recognize pests. The recognition unit uses a high-resolution camera or an infrared camera, allowing it to recognize pests day or night. For example, an infrared camera can be used to detect pests even in the dark. Step 2: The analysis unit uses AI to analyze the type of pest recognized by the recognition unit. Based on pre-trained data, the analysis unit identifies pests such as wild boars, deer, and crows. For example, an image of a pest can be input into the AI ​​model to identify the type of pest. Step 3: The output unit outputs the images and sounds identified by the analysis unit that are disliked by the pests. For example, it makes a loud noise for wild boars, flashes a specific light for deer, and displays the image of a bird of prey for crows. The output unit outputs these images using speakers and LED lights. Step 4: The control unit enables remote control. The control unit allows you to check the status in real time and change settings from a smartphone or computer. For example, you can access the digital scarecrow system via the Internet and change the settings.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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, to avoid confusion and 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recognition unit that recognizes pests using a camera; an analysis unit that analyzes the type of pest recognized by the recognition unit; an output unit that outputs a specific appearance or sound for the pest identified by the analysis unit; and an operation unit that enables remote operation. A system characterized by:

2. The recognition unit Use infrared cameras to identify pests at night 2. The system of claim 1.

3. The output unit Flashing a specific light 2. The system of claim 1.

4. The output unit Reflecting the appearance of birds of prey 2. The system of claim 1.

5. The operation unit includes: Check the status and change settings in real time from your smartphone or computer 2. The system of claim 1.

6. The recognition unit Estimate the user's emotions and adjust the camera's recognition accuracy based on the estimated user emotions.

2. The system of claim 1.

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

8. The recognition unit Detecting the body temperature of pests and identifying them based on changes in body temperature 2. The system of claim 1.

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