system

Abstract

The system according to this embodiment aims to recognize a target animal and drive it away by generating a sound that frightens the animal. [Solution] The system according to the embodiment comprises a recognition unit, a generation unit, and an output unit. The recognition unit recognizes the target animal. The generation unit generates animal sounds based on the animal recognized by the recognition unit. The output unit outputs the sound generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the methods for preventing crop damage are not sufficiently effective and there is room for improvement.

[0005] The system according to the embodiment aims to recognize a target animal and generate a cry that the animal fears to drive it away.

Means for Solving the Problems

[0006] The system according to the embodiment includes a recognition unit, a generation unit, and an output unit. The recognition unit recognizes a target animal. The generation unit generates a cry of the animal based on the animal recognized by the recognition unit. The output unit outputs the sound generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can recognize a target animal and drive it away by generating a sound that the animal will find frightening. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The wildlife damage control system according to an embodiment of the present invention is a novel system using voice and music generation AI. This system aims to drive away animals by recognizing them and generating and outputting animal sounds. Specifically, a camera-mounted object recognition AI recognizes the target animal, and a generation AI generates animal sounds that the target animal will find frightening, and outputs the sound. This system operates day and night and automatically generates intimidating sounds according to the season and surrounding environment. If the initial sound is ineffective, it switches to a different sound and continues the intimidation. Furthermore, it analyzes camera footage, automatically generates a report summarizing daily sighting information, and sends it via email. This allows for collaboration with nearby systems and sharing of sighting information. By supporting collaboration at the municipal level, it is possible to reduce high-priced contracts and maintenance costs. Through this system, the aim is to reduce crop damage, expand agricultural motivation, increase the number of primary industry workers, and revitalize mountain villages as a whole. In this way, the wildlife damage control system can reduce crop damage and expand agricultural motivation.

[0029] The wildlife damage control system according to this embodiment comprises a recognition unit, a generation unit, and an output unit. The recognition unit recognizes target animals. The recognition unit can recognize animals using, for example, a camera. The recognition unit can identify the type of animal using image recognition technology. The recognition unit can recognize animals even at night using, for example, an infrared camera. The generation unit generates animal sounds based on the animals recognized by the recognition unit. The generation unit can generate animal sounds using, for example, a generation AI. The generation unit can generate different sounds depending on the type of animal. The generation unit can generate animal sounds using, for example, a text generation AI (e.g., LLM). The output unit outputs the sound generated by the generation unit. The output unit can output the sound using, for example, a speaker. The output unit can adjust the timing of the sound output. The output unit can output the sound when an animal approaches. Thus, the wildlife damage control system according to this embodiment can drive away animals by recognizing target animals and generating and outputting animal sounds.

[0030] The recognition unit recognizes the target animal. For example, the recognition unit can recognize animals using a camera. Specifically, the recognition unit uses a high-resolution camera to monitor the animal's movements in real time. Multiple cameras may be installed to cover a wide area, enabling animal detection in a large area. The recognition unit can identify the type of animal using image recognition technology. For example, it uses an image recognition algorithm with deep learning to analyze the animal's shape, pattern, and movement patterns. This allows for high-precision identification of different animals such as dogs, cats, deer, and wild boars. The recognition unit can recognize animals even at night using an infrared camera. Infrared cameras can detect the animal's body temperature even in darkness, confirming its presence. This enables 24-hour monitoring of animals, day and night. Furthermore, the recognition unit can record the history of the animal's movements and analyze the animal's behavior patterns based on past data. This allows for prediction of the frequency of animal appearances at specific times and locations, enabling effective countermeasures. The recognition unit can also use AI to predict animal behavior and detect abnormal movements. For example, it can detect movements that deviate from normal behavioral patterns and issue warnings early. This allows the recognition unit to recognize animal species and behavior with high accuracy, improving the overall effectiveness of the system.

[0031] The generation unit generates animal sounds based on the animals recognized by the recognition unit. The generation unit can generate animal sounds using, for example, a generation AI. Specifically, the generation AI refers to a database of animal sounds corresponding to different animal species and generates the appropriate sound in real time. The generation unit can generate different sounds depending on the type of animal. For example, if a deer is recognized, it will generate a deer sound; if a wild boar is recognized, it will generate a wild boar sound. The generation unit can also generate animal sounds using, for example, a text generation AI (e.g., LLM). The text generation AI takes the characteristics of the animal sound as text data as input and generates audio data based on that. This allows the generation unit to reproduce animal sounds with high accuracy. Furthermore, the generation unit can generate not only animal sounds but also intimidation sounds and warning sounds. For example, when a specific animal approaches, it can generate a sound that the animal dislikes, increasing the effectiveness of driving it away. The generation unit can transmit the generated audio data to the output unit in real time and output the sound immediately. This allows the generation unit to work in conjunction with the recognition unit to generate sounds that quickly and effectively drive away animals.

[0032] The output unit outputs the sound generated by the generation unit. The output unit can output sound using, for example, a speaker. Specifically, the output unit can use a high-power speaker to deliver sound over a wide area. The output unit can adjust the timing of sound output. For example, by immediately outputting sound when an animal approaches, it can startle and drive the animal away. The output unit can also adjust the direction and volume of sound output. This allows for effective delivery of sound in a specific direction. Furthermore, the output unit can coordinate multiple speakers to achieve wide-area sound output. For example, speakers can be placed throughout a farmland to allow for immediate response no matter where an animal appears. The output unit can also change the type and pattern of sound. For example, different calls or intimidating sounds can be randomly output to prevent animals from becoming accustomed to them. This allows the output unit to output sound at the appropriate time to effectively drive away animals, maximizing the overall system's effectiveness. In addition, the output unit can record the history of sound output and analyze which sound was most effective. This can be used to improve the system in the future.

[0033] The output unit may include a switching unit that switches to a different sound if the first sound is ineffective. For example, the output unit can switch to a different sound if the animal does not react. The output unit can monitor the animal's reaction in real time and switch the sound if it is ineffective. For example, the output unit can change the intimidation sound if the animal does not run away. This enhances the effectiveness of driving away animals by switching to a different sound when the first sound is ineffective. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal reaction data into a generating AI and cause the generating AI to switch to the optimal sound.

[0034] The report generation unit can analyze camera footage and automatically generate a report summarizing daily animal sightings. For example, the report generation unit can analyze camera footage using image processing technology. The report generation unit can extract animal sighting information using a motion detection algorithm. For example, the report generation unit can identify the date, time, and location of animal sightings and compile them into a report. The report generation unit can send the summarized sighting information via email. This makes it easier to understand animal sightings by automatically generating a report summarizing daily sightings. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input camera footage data into a generation AI and have the generation AI perform the summarization of sighting information.

[0035] The recognition unit operates day and night and can recognize animals according to the seasons and surrounding environment. The recognition unit can recognize animals even at night using, for example, an infrared camera. The recognition unit can use a camera that operates in low-light environments. The recognition unit can recognize animals using, for example, sensors that respond to changes in temperature and humidity. The recognition unit can learn the behavioral patterns of animals for each season and improve its recognition accuracy. As a result, recognition accuracy is improved by operating day and night and recognizing animals according to the seasons and surrounding environment. Some or all of the above processing in the recognition unit may be performed using, for example, AI, or not using AI. For example, the recognition unit can input animal behavioral pattern data into a generating AI and have the generating AI perform optimization of the recognition algorithm.

[0036] The generation unit can automatically generate intimidating sounds according to the seasons and surrounding environment. For example, the generation unit can learn the behavioral patterns of animals in each season and generate intimidating sounds based on that. The generation unit can analyze ambient environmental sounds and generate intimidating sounds appropriate to them. For example, the generation unit can generate an intimidating sound that is effective against a specific animal in the summer and a different intimidating sound in the winter. The generation unit can dynamically adjust the intimidating sounds in response to changes in the environment. As a result, by automatically generating intimidating sounds according to the seasons and surrounding environment, animals can be effectively driven away. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input environmental data into a generation AI and have the generation AI perform the generation of the optimal intimidating sound.

[0037] The report generation unit can collaborate with nearby systems to share sighting information. For example, the report generation unit can collect sighting information from other monitoring systems and incorporate it into reports. The report generation unit can exchange sighting information using data sharing protocols. For example, the report generation unit can share sighting information with nearby systems in real time. The report generation unit can analyze wide-area animal appearance patterns and compile them into reports. This allows for understanding wide-area animal appearance patterns by collaborating with nearby systems and sharing sighting information. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input sighting data into a generation AI and have the generation AI generate the report.

[0038] The recognition unit can analyze animal behavior patterns and optimize the recognition algorithm during recognition. For example, the recognition unit can analyze animal movement patterns and prioritize the recognition of animals that appear during specific time periods. The recognition unit can consider animal feeding times and recognize animals that appear during those times with high accuracy. The recognition unit can learn seasonal animal behavior patterns and improve recognition accuracy. Thus, recognition accuracy can be improved by analyzing animal behavior patterns. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal behavior pattern data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0039] The recognition unit can apply different recognition methods to each type of animal during recognition. For example, the recognition unit can optimize the recognition algorithms for bears and wild boars separately to improve accuracy. The recognition unit can differentiate the recognition methods for birds and mammals to recognize specific animals with high accuracy. The recognition unit can differentiate the recognition methods for small animals and large animals to reduce misrecognition. In this way, recognition accuracy can be improved by applying different recognition methods to each type of animal. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input different recognition methods for each type of animal into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0040] The recognition unit can improve recognition accuracy by taking into account the animal's movement speed during recognition. For example, if the animal is moving fast, the recognition unit can speed up the recognition algorithm to maintain accuracy. If the animal is moving slow, the recognition unit can perform detailed recognition to improve accuracy. The recognition unit can dynamically adjust the recognition algorithm according to the animal's movement speed. This allows for improved recognition accuracy by taking the animal's movement speed into consideration. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal movement speed data into a generating AI and have the generating AI adjust the recognition algorithm.

[0041] The recognition unit can improve recognition accuracy by analyzing animal sounds during recognition. For example, the recognition unit can improve recognition accuracy by analyzing animal sounds in real time. The recognition unit can recognize specific animals with high accuracy by utilizing an animal sound database. The recognition unit can improve recognition accuracy by combining animal sounds and behavioral patterns. In this way, recognition accuracy can be improved by analyzing animal sounds. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal sound data into a generating AI and have the generating AI perform optimization of the recognition algorithm.

[0042] The generation unit can receive real-time feedback of animal reactions during generation and optimize the generation algorithm. For example, if an animal runs away, the generation unit can use that reaction as feedback to adjust the generation algorithm. If an animal does not react, the generation unit can optimize the algorithm to generate a different sound. The generation unit can accumulate animal reaction data and use it for the next generation. This allows for the optimization of the generation algorithm by providing real-time feedback of animal reactions, thereby generating effective sounds. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input animal reaction data into a generation AI and have the generation AI optimize the generation algorithm.

[0043] The generation unit can apply different sound generation techniques to different animal species during generation. For example, the generation unit can generate a specific intimidating sound for bears and a different intimidating sound for wild boars. The generation unit can generate high-frequency sounds for birds and low-frequency sounds for mammals. The generation unit can apply different sound generation techniques to small and large animals. By applying different sound generation techniques to different animal species, effective sounds can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input different sound generation techniques for each animal species into the generation AI and have the generation AI optimize the sound generation algorithm.

[0044] The generation unit can optimize the generated voice by referring to the animal's past response data during generation. For example, the generation unit can prioritize the generation of voices that have been effective in the past. The generation unit can generate effective voices based on past response data. The generation unit can analyze past response data and apply the optimal voice generation algorithm. This allows for the generation of effective voices by referring to the animal's past response data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the animal's past response data into a generation AI and have the generation AI perform the optimization of the generated voice.

[0045] The generation unit can customize the generated voice based on the animal's habitat during generation. For example, the generation unit can generate a voice that is effective for a specific animal in mountainous areas. The generation unit can generate a voice that is effective for a different animal in urban areas. The generation unit can apply different voice generation methods to each habitat. This allows for the generation of effective voices by customizing the generated voice based on the animal's habitat. Some or all of the above-described processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input animal habitat data into a generation AI and have the generation AI perform the customization of the generated voice.

[0046] The output unit can measure the distance to the animal and output sound at the optimal volume. For example, the output unit can set the volume lower when the animal is close, and higher when the animal is far away. The output unit can dynamically adjust the volume according to the distance to the animal. This allows the output unit to output sound at the optimal volume by measuring the distance to the animal. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal distance data to a generating AI and have the generating AI adjust the volume.

[0047] The output unit can apply different output methods to different animal species during output. For example, the output unit can apply a specific sound output method to bears and a different one to wild boars. The output unit can apply a high-frequency sound output method to birds and a low-frequency sound output method to mammals. The output unit can apply different sound output methods to small and large animals. This allows for effective sound output by applying different output methods to different animal species. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input different output methods for each animal species into a generating AI and have the generating AI optimize the output algorithm.

[0048] The output unit can adjust the direction of sound output when outputting, taking into account the animal's direction of movement. For example, if the animal is moving north, the output unit can output sound in the north direction. If the animal is moving south, the output unit can output sound in the south direction. The output unit can dynamically adjust the direction of sound output according to the animal's direction of movement. This allows for effective sound output by taking the animal's direction of movement into consideration. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal movement direction data into a generating AI and have the generating AI perform the adjustment of the sound output direction.

[0049] The output unit can customize the output sound based on the animal's activity time. For example, the output unit can output a sound that is effective at night for nocturnal animals. For example, the output unit can output a sound that is effective during the day for diurnal animals. The output unit can customize the sound according to the animal's activity time. By customizing the output sound based on the animal's activity time, an effective sound can be output. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal activity time data into a generating AI and have the generating AI perform the sound customization.

[0050] The switching unit can analyze animal reaction data in real time during switching and switch to the optimal voice. For example, if an animal runs away, the switching unit can feed back that reaction and switch to the optimal voice. If the animal does not react, the switching unit can switch to a different voice. The switching unit can store animal reaction data and use it for the next switch. This allows the unit to switch to the optimal voice by analyzing animal reaction data in real time. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input animal reaction data into a generating AI and cause the generating AI to execute the switch to the optimal voice.

[0051] The switching unit can apply different switching methods to different animal species during switching. For example, the switching unit can apply a specific voice switching method to bears and a different one to wild boars. The switching unit can apply a high-frequency voice switching method to birds and a low-frequency voice switching method to mammals. The switching unit can apply different voice switching methods to small animals and large animals. This enables effective voice switching by applying different switching methods to different animal species. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input different switching methods for each animal species into a generating AI and have the generating AI optimize the switching algorithm.

[0052] The switching unit can optimize the switching voice by referring to the animal's past response data during the switching process. The switching unit can, for example, switch to a voice that has been effective in the past. The switching unit can switch to an effective voice based on past response data. The switching unit can analyze past response data and apply the optimal voice switching algorithm. This makes effective voice switching possible by referring to the animal's past response data. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input the animal's past response data into a generating AI and have the generating AI perform the optimization of the switching voice.

[0053] The switching unit can customize the switching voice based on the animal's habitat when switching voices. For example, the switching unit can switch to a voice that is effective for a specific animal in mountainous areas. The switching unit can switch to a voice that is effective for a different animal in urban areas. The switching unit can apply different voice switching methods for each habitat. This makes it possible to effectively switch voices by customizing the switching voice based on the animal's habitat. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input animal habitat data into a generating AI and have the generating AI perform the customization of the switching voice.

[0054] The report generation unit can analyze animal appearance patterns during report generation to improve the accuracy of the report. For example, the report generation unit can analyze the time of day when animals appear and generate a detailed report. The report generation unit can identify the locations where animals appear and generate a report with a map. The report generation unit can analyze seasonal appearance patterns and generate a predictive report. In this way, the accuracy of the report can be improved by analyzing animal appearance patterns. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input animal appearance pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of the report.

[0055] The report generation unit can apply different report generation methods to different types of animals when generating reports. For example, the report generation unit can apply a specific report format to bear sighting information and a different report format to wild boar sighting information. The report generation unit can generate reports containing high-frequency audio data for bird sighting information and reports containing low-frequency audio data for mammal sighting information. The report generation unit can apply different report generation methods to small animals and large animals. By applying different report generation methods to different types of animals, effective reports can be generated. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input different report generation methods for each type of animal into the generation AI and have the generation AI optimize the report generation algorithm.

[0056] The report generation unit can optimize the report content by referring to animal movement data during report generation. For example, the report generation unit can generate a detailed report based on animal movement data. The report generation unit can analyze animal movement patterns and generate a predictive report. The report generation unit can utilize animal movement data and apply the optimal report generation algorithm. This allows for the generation of effective reports by referring to animal movement data. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input animal movement data into a generation AI and have the generation AI optimize the report content.

[0057] The report generation unit can customize the content of reports based on the animal's habitat when generating them. For example, the report generation unit can generate a detailed report on a specific animal in a mountainous area. For example, the report generation unit can generate a detailed report on a different animal in an urban area. The report generation unit can apply different report generation methods to each habitat. This allows for the generation of effective reports by customizing the content of reports based on the animal's habitat. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input animal habitat data into a generation AI and have the generation AI perform the customization of the report content.

[0058] The report generation unit can collaborate with nearby systems to share sighting information when generating reports. For example, the report generation unit can collect sighting information from nearby systems and reflect it in the report. The report generation unit can share sighting information and analyze wide-area animal appearance patterns. The report generation unit can collaborate with nearby systems to share sighting information in real time. This allows for understanding wide-area animal appearance patterns by collaborating with nearby systems and sharing sighting information. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input sighting data into a generation AI and have the generation AI execute the report generation.

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

[0060] The recognition unit can analyze animal sounds in real time and identify the type of animal. The recognition unit can, for example, utilize an animal sound database to recognize a specific animal with high accuracy. The recognition unit can improve recognition accuracy by combining animal sounds with behavioral patterns. Thus, recognition accuracy can be improved by analyzing animal sounds. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal sound data into a generating AI and have the generating AI optimize the recognition algorithm.

[0061] The generation unit can receive real-time feedback on animal responses and optimize its generation algorithm. For example, if an animal runs away, the generation unit can use that response as feedback to adjust the generation algorithm. If an animal does not respond, the generation unit can optimize its algorithm to generate a different sound. The generation unit can accumulate animal response data and use it for future generation. This allows for the optimization of the generation algorithm and the generation of effective sounds by providing real-time feedback on animal responses. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input animal response data into a generation AI and have the generation AI optimize the generation algorithm.

[0062] The output unit can adjust the direction of sound output considering the direction of the animal's movement. For example, if the animal is moving north, the output unit can output sound in the north direction. If the animal is moving south, the output unit can output sound in the south direction. The output unit can dynamically adjust the direction of sound output according to the direction of the animal's movement. This allows for effective sound output by considering the direction of the animal's movement. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal movement direction data into a generating AI and have the generating AI perform the adjustment of the sound output direction.

[0063] The report generation unit can analyze animal appearance patterns and improve the accuracy of reports. For example, the report generation unit can analyze the time of day when animals appear and generate a detailed report. The report generation unit can identify the locations where animals appear and generate a report with a map. The report generation unit can analyze seasonal appearance patterns and generate a predictive report. In this way, the accuracy of reports can be improved by analyzing animal appearance patterns. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input animal appearance pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of reports.

[0064] The recognition unit can improve recognition accuracy by taking into account the animal's movement speed. For example, if the animal is moving fast, the recognition unit can speed up the recognition algorithm while maintaining accuracy. If the animal is moving slow, the recognition unit can perform detailed recognition to improve accuracy. The recognition unit can dynamically adjust the recognition algorithm according to the animal's movement speed. This allows for improved recognition accuracy by taking the animal's movement speed into consideration. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal movement speed data into a generating AI and have the generating AI adjust the recognition algorithm.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The recognition unit recognizes the target animal. The recognition unit can recognize animals using, for example, a camera, and can identify the type of animal using image recognition technology. It can also recognize animals at night using an infrared camera. Step 2: The generation unit generates animal sounds based on the animals recognized by the recognition unit. The generation unit can generate animal sounds using, for example, a generation AI, and can generate different sounds depending on the type of animal. Furthermore, it can also generate animal sounds using a text generation AI (e.g., LLM). Step 3: The output unit outputs the sound generated by the generation unit. The output unit can output sound using, for example, a speaker, and the timing of the sound output can be adjusted. Furthermore, it can output sound when an animal approaches.

[0067] (Example of form 2) The wildlife damage control system according to an embodiment of the present invention is a novel system using voice and music generation AI. This system aims to drive away animals by recognizing them and generating and outputting animal sounds. Specifically, a camera-mounted object recognition AI recognizes the target animal, and a generation AI generates animal sounds that the target animal will find frightening, and outputs the sound. This system operates day and night and automatically generates intimidating sounds according to the season and surrounding environment. If the initial sound is ineffective, it switches to a different sound and continues the intimidation. Furthermore, it analyzes camera footage, automatically generates a report summarizing daily sighting information, and sends it via email. This allows for collaboration with nearby systems and sharing of sighting information. By supporting collaboration at the municipal level, it is possible to reduce high-priced contracts and maintenance costs. Through this system, the aim is to reduce crop damage, expand agricultural motivation, increase the number of primary industry workers, and revitalize mountain villages as a whole. In this way, the wildlife damage control system can reduce crop damage and expand agricultural motivation.

[0068] The wildlife damage control system according to this embodiment comprises a recognition unit, a generation unit, and an output unit. The recognition unit recognizes target animals. The recognition unit can recognize animals using, for example, a camera. The recognition unit can identify the type of animal using image recognition technology. The recognition unit can recognize animals even at night using, for example, an infrared camera. The generation unit generates animal sounds based on the animals recognized by the recognition unit. The generation unit can generate animal sounds using, for example, a generation AI. The generation unit can generate different sounds depending on the type of animal. The generation unit can generate animal sounds using, for example, a text generation AI (e.g., LLM). The output unit outputs the sound generated by the generation unit. The output unit can output the sound using, for example, a speaker. The output unit can adjust the timing of the sound output. The output unit can output the sound when an animal approaches. Thus, the wildlife damage control system according to this embodiment can drive away animals by recognizing target animals and generating and outputting animal sounds.

[0069] The recognition unit recognizes the target animal. For example, the recognition unit can recognize animals using a camera. Specifically, the recognition unit uses a high-resolution camera to monitor the animal's movements in real time. Multiple cameras may be installed to cover a wide area, enabling animal detection in a large area. The recognition unit can identify the type of animal using image recognition technology. For example, it uses an image recognition algorithm with deep learning to analyze the animal's shape, pattern, and movement patterns. This allows for high-precision identification of different animals such as dogs, cats, deer, and wild boars. The recognition unit can recognize animals even at night using an infrared camera. Infrared cameras can detect the animal's body temperature even in darkness, confirming its presence. This enables 24-hour monitoring of animals, day and night. Furthermore, the recognition unit can record the history of the animal's movements and analyze the animal's behavior patterns based on past data. This allows for prediction of the frequency of animal appearances at specific times and locations, enabling effective countermeasures. The recognition unit can also use AI to predict animal behavior and detect abnormal movements. For example, it can detect movements that deviate from normal behavioral patterns and issue warnings early. This allows the recognition unit to recognize animal species and behavior with high accuracy, improving the overall effectiveness of the system.

[0070] The generation unit generates animal sounds based on the animals recognized by the recognition unit. The generation unit can generate animal sounds using, for example, a generation AI. Specifically, the generation AI refers to a database of animal sounds corresponding to different animal species and generates the appropriate sound in real time. The generation unit can generate different sounds depending on the type of animal. For example, if a deer is recognized, it will generate a deer sound; if a wild boar is recognized, it will generate a wild boar sound. The generation unit can also generate animal sounds using, for example, a text generation AI (e.g., LLM). The text generation AI takes the characteristics of the animal sound as text data as input and generates audio data based on that. This allows the generation unit to reproduce animal sounds with high accuracy. Furthermore, the generation unit can generate not only animal sounds but also intimidation sounds and warning sounds. For example, when a specific animal approaches, it can generate a sound that the animal dislikes, increasing the effectiveness of driving it away. The generation unit can transmit the generated audio data to the output unit in real time and output the sound immediately. This allows the generation unit to work in conjunction with the recognition unit to generate sounds that quickly and effectively drive away animals.

[0071] The output unit outputs the sound generated by the generation unit. The output unit can output sound using, for example, a speaker. Specifically, the output unit can use a high-power speaker to deliver sound over a wide area. The output unit can adjust the timing of sound output. For example, by immediately outputting sound when an animal approaches, it can startle and drive the animal away. The output unit can also adjust the direction and volume of sound output. This allows for effective delivery of sound in a specific direction. Furthermore, the output unit can coordinate multiple speakers to achieve wide-area sound output. For example, speakers can be placed throughout a farmland to allow for immediate response no matter where an animal appears. The output unit can also change the type and pattern of sound. For example, different calls or intimidating sounds can be randomly output to prevent animals from becoming accustomed to them. This allows the output unit to output sound at the appropriate time to effectively drive away animals, maximizing the overall system's effectiveness. In addition, the output unit can record the history of sound output and analyze which sound was most effective. This can be used to improve the system in the future.

[0072] The output unit may include a switching unit that switches to a different sound if the first sound is ineffective. For example, the output unit can switch to a different sound if the animal does not react. The output unit can monitor the animal's reaction in real time and switch the sound if it is ineffective. For example, the output unit can change the intimidation sound if the animal does not run away. This enhances the effectiveness of driving away animals by switching to a different sound when the first sound is ineffective. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal reaction data into a generating AI and cause the generating AI to switch to the optimal sound.

[0073] The report generation unit can analyze camera footage and automatically generate a report summarizing daily animal sightings. For example, the report generation unit can analyze camera footage using image processing technology. The report generation unit can extract animal sighting information using a motion detection algorithm. For example, the report generation unit can identify the date, time, and location of animal sightings and compile them into a report. The report generation unit can send the summarized sighting information via email. This makes it easier to understand animal sightings by automatically generating a report summarizing daily sightings. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input camera footage data into a generation AI and have the generation AI perform the summarization of sighting information.

[0074] The recognition unit operates day and night and can recognize animals according to the seasons and surrounding environment. The recognition unit can recognize animals even at night using, for example, an infrared camera. The recognition unit can use a camera that operates in low-light environments. The recognition unit can recognize animals using, for example, sensors that respond to changes in temperature and humidity. The recognition unit can learn the behavioral patterns of animals for each season and improve its recognition accuracy. As a result, recognition accuracy is improved by operating day and night and recognizing animals according to the seasons and surrounding environment. Some or all of the above processing in the recognition unit may be performed using, for example, AI, or not using AI. For example, the recognition unit can input animal behavioral pattern data into a generating AI and have the generating AI perform optimization of the recognition algorithm.

[0075] The generation unit can automatically generate intimidating sounds according to the seasons and surrounding environment. For example, the generation unit can learn the behavioral patterns of animals in each season and generate intimidating sounds based on that. The generation unit can analyze ambient environmental sounds and generate intimidating sounds appropriate to them. For example, the generation unit can generate an intimidating sound that is effective against a specific animal in the summer and a different intimidating sound in the winter. The generation unit can dynamically adjust the intimidating sounds in response to changes in the environment. As a result, by automatically generating intimidating sounds according to the seasons and surrounding environment, animals can be effectively driven away. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input environmental data into a generation AI and have the generation AI perform the generation of the optimal intimidating sound.

[0076] The report generation unit can collaborate with nearby systems to share sighting information. For example, the report generation unit can collect sighting information from other monitoring systems and incorporate it into reports. The report generation unit can exchange sighting information using data sharing protocols. For example, the report generation unit can share sighting information with nearby systems in real time. The report generation unit can analyze wide-area animal appearance patterns and compile them into reports. This allows for understanding wide-area animal appearance patterns by collaborating with nearby systems and sharing sighting information. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input sighting data into a generation AI and have the generation AI generate the report.

[0077] The recognition unit can estimate the user's emotions and adjust the recognition accuracy based on the estimated emotions. For example, the recognition unit can estimate the user's emotions using facial recognition technology. The recognition unit can estimate the user's emotions using speech analysis technology. For example, if the user is stressed, the recognition unit can increase the recognition accuracy to reduce misrecognition. If the user is relaxed, the recognition unit can maintain normal recognition accuracy. If the user is in a hurry, the recognition unit can prioritize recognition speed and sacrifice some accuracy. This reduces misrecognition by adjusting the recognition accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of recognition accuracy.

[0078] The recognition unit can analyze animal behavior patterns and optimize the recognition algorithm during recognition. For example, the recognition unit can analyze animal movement patterns and prioritize the recognition of animals that appear during specific time periods. The recognition unit can consider animal feeding times and recognize animals that appear during those times with high accuracy. The recognition unit can learn seasonal animal behavior patterns and improve recognition accuracy. Thus, recognition accuracy can be improved by analyzing animal behavior patterns. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal behavior pattern data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0079] The recognition unit can apply different recognition methods to each type of animal during recognition. For example, the recognition unit can optimize the recognition algorithms for bears and wild boars separately to improve accuracy. The recognition unit can differentiate the recognition methods for birds and mammals to recognize specific animals with high accuracy. The recognition unit can differentiate the recognition methods for small animals and large animals to reduce misrecognition. In this way, recognition accuracy can be improved by applying different recognition methods to each type of animal. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input different recognition methods for each type of animal into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0080] The recognition unit can estimate the user's emotions and adjust the display method of the recognition results based on the estimated user emotions. For example, the recognition unit can estimate the user's emotions using facial recognition technology. The recognition unit can estimate the user's emotions using voice analysis technology. If the user is tense, the recognition unit can provide a simple and highly visible display method. If the user is relaxed, the recognition unit can provide a display method that includes detailed information. If the user is in a hurry, the recognition unit can provide a display method that gets straight to the point. By adjusting the display method of the recognition results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the recognition results.

[0081] The recognition unit can improve recognition accuracy by taking into account the animal's movement speed during recognition. For example, if the animal is moving fast, the recognition unit can speed up the recognition algorithm to maintain accuracy. If the animal is moving slow, the recognition unit can perform detailed recognition to improve accuracy. The recognition unit can dynamically adjust the recognition algorithm according to the animal's movement speed. This allows for improved recognition accuracy by taking the animal's movement speed into consideration. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal movement speed data into a generating AI and have the generating AI adjust the recognition algorithm.

[0082] The recognition unit can improve recognition accuracy by analyzing animal sounds during recognition. For example, the recognition unit can improve recognition accuracy by analyzing animal sounds in real time. The recognition unit can recognize specific animals with high accuracy by utilizing an animal sound database. The recognition unit can improve recognition accuracy by combining animal sounds and behavioral patterns. In this way, recognition accuracy can be improved by analyzing animal sounds. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal sound data into a generating AI and have the generating AI perform optimization of the recognition algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the type of voice it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a calm voice. If the user is tense, the generation unit can generate a threatening voice. If the user is excited, the generation unit can generate a stimulating voice. By adjusting the type of voice generated based on the user's emotions, more effective voices can be produced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the type of voice to be generated.

[0084] The generation unit can receive real-time feedback of animal reactions during generation and optimize the generation algorithm. For example, if an animal runs away, the generation unit can use that reaction as feedback to adjust the generation algorithm. If an animal does not react, the generation unit can optimize the algorithm to generate a different sound. The generation unit can accumulate animal reaction data and use it for the next generation. This allows for the optimization of the generation algorithm by providing real-time feedback of animal reactions, thereby generating effective sounds. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input animal reaction data into a generation AI and have the generation AI optimize the generation algorithm.

[0085] The generation unit can apply different sound generation techniques to different animal species during generation. For example, the generation unit can generate a specific intimidating sound for bears and a different intimidating sound for wild boars. The generation unit can generate high-frequency sounds for birds and low-frequency sounds for mammals. The generation unit can apply different sound generation techniques to small and large animals. By applying different sound generation techniques to different animal species, effective sounds can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input different sound generation techniques for each animal species into the generation AI and have the generation AI optimize the sound generation algorithm.

[0086] The generation unit can estimate the user's emotions and adjust the volume of the generated audio based on the estimated emotions. For example, the generation unit can set the volume lower if the user is relaxed. The generation unit can set the volume higher if the user is tense. The generation unit can dynamically change the volume if the user is excited. This allows for the generation of more effective audio by adjusting the volume of the generated audio based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the volume of the generated audio.

[0087] The generation unit can optimize the generated voice by referring to the animal's past response data during generation. For example, the generation unit can prioritize the generation of voices that have been effective in the past. The generation unit can generate effective voices based on past response data. The generation unit can analyze past response data and apply the optimal voice generation algorithm. This allows for the generation of effective voices by referring to the animal's past response data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the animal's past response data into a generation AI and have the generation AI perform the optimization of the generated voice.

[0088] The generation unit can customize the generated voice based on the animal's habitat during generation. For example, the generation unit can generate a voice that is effective for a specific animal in mountainous areas. The generation unit can generate a voice that is effective for a different animal in urban areas. The generation unit can apply different voice generation methods to each habitat. This allows for the generation of effective voices by customizing the generated voice based on the animal's habitat. Some or all of the above-described processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input animal habitat data into a generation AI and have the generation AI perform the customization of the generated voice.

[0089] The output unit can estimate the user's emotions and adjust the timing of the output audio based on the estimated emotions. For example, if the user is relaxed, the output unit can delay the timing of the audio output. If the user is tense, the output unit can speed up the timing of the audio output. If the user is in a hurry, the output unit can make the audio output immediate. By adjusting the timing of the output audio based on the user's emotions, more effective audio can be produced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, for example, or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI adjust the timing of the output audio.

[0090] The output unit can measure the distance to the animal and output sound at the optimal volume. For example, the output unit can set the volume lower when the animal is close, and higher when the animal is far away. The output unit can dynamically adjust the volume according to the distance to the animal. This allows the output unit to output sound at the optimal volume by measuring the distance to the animal. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal distance data to a generating AI and have the generating AI adjust the volume.

[0091] The output unit can apply different output methods to different animal species during output. For example, the output unit can apply a specific sound output method to bears and a different one to wild boars. The output unit can apply a high-frequency sound output method to birds and a low-frequency sound output method to mammals. The output unit can apply different sound output methods to small and large animals. This allows for effective sound output by applying different output methods to different animal species. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input different output methods for each animal species into a generating AI and have the generating AI optimize the output algorithm.

[0092] The output unit can estimate the user's emotions and adjust the frequency of the output sound based on the estimated user emotions. For example, the output unit can output low-frequency sound when the user is relaxed. The output unit can output high-frequency sound when the user is tense. The output unit can dynamically change the frequency when the user is excited. This allows for the output of more effective sound by adjusting the frequency of the output sound based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of the output sound.

[0093] The output unit can adjust the direction of sound output when outputting, taking into account the animal's direction of movement. For example, if the animal is moving north, the output unit can output sound in the north direction. If the animal is moving south, the output unit can output sound in the south direction. The output unit can dynamically adjust the direction of sound output according to the animal's direction of movement. This allows for effective sound output by taking the animal's direction of movement into consideration. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal movement direction data into a generating AI and have the generating AI perform the adjustment of the sound output direction.

[0094] The output unit can customize the output sound based on the animal's activity time. For example, the output unit can output a sound that is effective at night for nocturnal animals. For example, the output unit can output a sound that is effective during the day for diurnal animals. The output unit can customize the sound according to the animal's activity time. By customizing the output sound based on the animal's activity time, an effective sound can be output. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal activity time data into a generating AI and have the generating AI perform the sound customization.

[0095] The switching unit can estimate the user's emotions and adjust the switching timing based on the estimated emotions. For example, if the user is relaxed, the switching unit can delay the switching timing. If the user is tense, the switching unit can speed up the switching timing. If the user is in a hurry, the switching unit can make the switching timing instantaneous. By adjusting the switching timing based on the user's emotions, more effective voice switching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the switching timing.

[0096] The switching unit can analyze animal reaction data in real time during switching and switch to the optimal voice. For example, if an animal runs away, the switching unit can feed back that reaction and switch to the optimal voice. If the animal does not react, the switching unit can switch to a different voice. The switching unit can store animal reaction data and use it for the next switch. This allows the unit to switch to the optimal voice by analyzing animal reaction data in real time. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input animal reaction data into a generating AI and cause the generating AI to execute the switch to the optimal voice.

[0097] The switching unit can apply different switching methods to different animal species during switching. For example, the switching unit can apply a specific voice switching method to bears and a different one to wild boars. The switching unit can apply a high-frequency voice switching method to birds and a low-frequency voice switching method to mammals. The switching unit can apply different voice switching methods to small animals and large animals. This enables effective voice switching by applying different switching methods to different animal species. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input different switching methods for each animal species into a generating AI and have the generating AI optimize the switching algorithm.

[0098] The switching unit can estimate the user's emotions and adjust the type of voice switching based on the estimated emotions. For example, if the user is relaxed, the switching unit can switch to a calm voice. If the user is tense, the switching unit can switch to a threatening voice. If the user is excited, the switching unit can switch to a stimulating voice. This allows for more effective voice switching by adjusting the type of voice switching based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the switching unit may be performed using AI, for example, or not using AI. For example, the switching unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the type of voice switching.

[0099] The switching unit can optimize the switching voice by referring to the animal's past response data during the switching process. The switching unit can, for example, switch to a voice that has been effective in the past. The switching unit can switch to an effective voice based on past response data. The switching unit can analyze past response data and apply the optimal voice switching algorithm. This makes effective voice switching possible by referring to the animal's past response data. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input the animal's past response data into a generating AI and have the generating AI perform the optimization of the switching voice.

[0100] The switching unit can customize the switching voice based on the animal's habitat when switching voices. For example, the switching unit can switch to a voice that is effective for a specific animal in mountainous areas. The switching unit can switch to a voice that is effective for a different animal in urban areas. The switching unit can apply different voice switching methods for each habitat. This makes it possible to effectively switch voices by customizing the switching voice based on the animal's habitat. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input animal habitat data into a generating AI and have the generating AI perform the customization of the switching voice.

[0101] The report generation unit can estimate the user's emotions and adjust the report content based on the estimated emotions. For example, if the user is relaxed, the report generation unit can generate a detailed report. If the user is stressed, the report generation unit can generate a concise report that gets straight to the point. If the user is in a hurry, the report generation unit can generate a report that contains only the essential information. By adjusting the report content based on the user's emotions, a report that is easy for the user to read is generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can input user emotion data into a generative AI and have the generative AI adjust the report content.

[0102] The report generation unit can analyze animal appearance patterns during report generation to improve the accuracy of the report. For example, the report generation unit can analyze the time of day when animals appear and generate a detailed report. The report generation unit can identify the locations where animals appear and generate a report with a map. The report generation unit can analyze seasonal appearance patterns and generate a predictive report. In this way, the accuracy of the report can be improved by analyzing animal appearance patterns. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input animal appearance pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of the report.

[0103] The report generation unit can apply different report generation methods to different types of animals when generating reports. For example, the report generation unit can apply a specific report format to bear sighting information and a different report format to wild boar sighting information. The report generation unit can generate reports containing high-frequency audio data for bird sighting information and reports containing low-frequency audio data for mammal sighting information. The report generation unit can apply different report generation methods to small animals and large animals. By applying different report generation methods to different types of animals, effective reports can be generated. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input different report generation methods for each type of animal into the generation AI and have the generation AI optimize the report generation algorithm.

[0104] The report generation unit can estimate the user's emotions and adjust the report display method based on the estimated emotions. For example, if the user is relaxed, the report generation unit can provide a display method that includes detailed information. If the user is stressed, the report generation unit can provide a simple and easy-to-read display method. If the user is in a hurry, the report generation unit can provide a display method that gets straight to the point. By adjusting the report display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to read. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can input user emotion data into the generative AI and have the generative AI adjust the report display method.

[0105] The report generation unit can optimize the report content by referring to animal movement data during report generation. For example, the report generation unit can generate a detailed report based on animal movement data. The report generation unit can analyze animal movement patterns and generate a predictive report. The report generation unit can utilize animal movement data and apply the optimal report generation algorithm. This allows for the generation of effective reports by referring to animal movement data. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input animal movement data into a generation AI and have the generation AI optimize the report content.

[0106] The report generation unit can customize the content of reports based on the animal's habitat when generating them. For example, the report generation unit can generate a detailed report on a specific animal in a mountainous area. For example, the report generation unit can generate a detailed report on a different animal in an urban area. The report generation unit can apply different report generation methods to each habitat. This allows for the generation of effective reports by customizing the content of reports based on the animal's habitat. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input animal habitat data into a generation AI and have the generation AI perform the customization of the report content.

[0107] The report generation unit can collaborate with nearby systems to share sighting information when generating reports. For example, the report generation unit can collect sighting information from nearby systems and reflect it in the report. The report generation unit can share sighting information and analyze wide-area animal appearance patterns. The report generation unit can collaborate with nearby systems to share sighting information in real time. This allows for understanding wide-area animal appearance patterns by collaborating with nearby systems and sharing sighting information. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input sighting data into a generation AI and have the generation AI execute the report generation.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The recognition unit can analyze animal sounds in real time and identify the type of animal. The recognition unit can, for example, utilize an animal sound database to recognize a specific animal with high accuracy. The recognition unit can improve recognition accuracy by combining animal sounds with behavioral patterns. Thus, recognition accuracy can be improved by analyzing animal sounds. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal sound data into a generating AI and have the generating AI optimize the recognition algorithm.

[0110] The generation unit can receive real-time feedback on animal responses and optimize its generation algorithm. For example, if an animal runs away, the generation unit can use that response as feedback to adjust the generation algorithm. If an animal does not respond, the generation unit can optimize its algorithm to generate a different sound. The generation unit can accumulate animal response data and use it for future generation. This allows for the optimization of the generation algorithm and the generation of effective sounds by providing real-time feedback on animal responses. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input animal response data into a generation AI and have the generation AI optimize the generation algorithm.

[0111] The output unit can adjust the direction of sound output considering the direction of the animal's movement. For example, if the animal is moving north, the output unit can output sound in the north direction. If the animal is moving south, the output unit can output sound in the south direction. The output unit can dynamically adjust the direction of sound output according to the direction of the animal's movement. This allows for effective sound output by considering the direction of the animal's movement. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal movement direction data into a generating AI and have the generating AI perform the adjustment of the sound output direction.

[0112] The report generation unit can analyze animal appearance patterns and improve the accuracy of reports. For example, the report generation unit can analyze the time of day when animals appear and generate a detailed report. The report generation unit can identify the locations where animals appear and generate a report with a map. The report generation unit can analyze seasonal appearance patterns and generate a predictive report. In this way, the accuracy of reports can be improved by analyzing animal appearance patterns. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input animal appearance pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of reports.

[0113] The recognition unit can improve recognition accuracy by taking into account the animal's movement speed. For example, if the animal is moving fast, the recognition unit can speed up the recognition algorithm while maintaining accuracy. If the animal is moving slow, the recognition unit can perform detailed recognition to improve accuracy. The recognition unit can dynamically adjust the recognition algorithm according to the animal's movement speed. This allows for improved recognition accuracy by taking the animal's movement speed into consideration. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input animal movement speed data into a generating AI and have the generating AI adjust the recognition algorithm.

[0114] The recognition unit can estimate the user's emotions and adjust the recognition accuracy based on the estimated emotions. For example, the recognition unit can estimate the user's emotions using facial recognition technology. The recognition unit can estimate the user's emotions using speech analysis technology. For example, if the user is stressed, the recognition unit can increase the recognition accuracy to reduce misrecognition. If the user is relaxed, the recognition unit can maintain normal recognition accuracy. If the user is in a hurry, the recognition unit can prioritize recognition speed and sacrifice some accuracy. This reduces misrecognition by adjusting the recognition accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of recognition accuracy.

[0115] The generation unit can estimate the user's emotions and adjust the type of voice it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a calm voice. If the user is tense, the generation unit can generate a threatening voice. If the user is excited, the generation unit can generate a stimulating voice. By adjusting the type of voice generated based on the user's emotions, more effective voices can be produced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the type of voice to be generated.

[0116] The output unit can estimate the user's emotions and adjust the timing of the output audio based on the estimated emotions. For example, if the user is relaxed, the output unit can delay the timing of the audio output. If the user is tense, the output unit can speed up the timing of the audio output. If the user is in a hurry, the output unit can make the audio output immediate. By adjusting the timing of the output audio based on the user's emotions, more effective audio can be produced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, for example, or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI adjust the timing of the output audio.

[0117] The report generation unit can estimate the user's emotions and adjust the report content based on the estimated emotions. For example, if the user is relaxed, the report generation unit can generate a detailed report. If the user is stressed, the report generation unit can generate a concise report that gets straight to the point. If the user is in a hurry, the report generation unit can generate a report that contains only the essential information. By adjusting the report content based on the user's emotions, a report that is easy for the user to read is generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can input user emotion data into a generative AI and have the generative AI adjust the report content.

[0118] The switching unit can estimate the user's emotions and adjust the switching timing based on the estimated emotions. For example, if the user is relaxed, the switching unit can delay the switching timing. If the user is tense, the switching unit can speed up the switching timing. If the user is in a hurry, the switching unit can make the switching timing instantaneous. By adjusting the switching timing based on the user's emotions, more effective voice switching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the switching timing.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The recognition unit recognizes the target animal. The recognition unit can recognize animals using, for example, a camera, and can identify the type of animal using image recognition technology. It can also recognize animals at night using an infrared camera. Step 2: The generation unit generates animal sounds based on the animals recognized by the recognition unit. The generation unit can generate animal sounds using, for example, a generation AI, and can generate different sounds depending on the type of animal. Furthermore, it can also generate animal sounds using a text generation AI (e.g., LLM). Step 3: The output unit outputs the sound generated by the generation unit. The output unit can output sound using, for example, a speaker, and the timing of the sound output can be adjusted. Furthermore, it can output sound when an animal approaches.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements, including the recognition unit, generation unit, and output unit described above, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit recognizes an animal using the camera 42 of the smart device 14 and identifies the type of animal using the control unit 46A. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates animal sounds using generation AI. The output unit outputs the generated sound using the speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements, including the recognition unit, generation unit, and output unit described above, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit recognizes an animal using the camera 42 of the smart glasses 214 and identifies the type of animal using the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates animal sounds using generation AI. The output unit outputs the generated sound using, for example, the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements, including the recognition unit, generation unit, and output unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit recognizes an animal using the camera 42 of the headset terminal 314 and identifies the type of animal using the control unit 46A. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates animal sounds using a generation AI. The output unit outputs the generated sound using the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements, including the recognition unit, generation unit, and output unit described above, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the recognition unit recognizes animals using the camera 42 of the robot 414 and identifies the type of animal using the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates animal sounds using generation AI. The output unit outputs the generated sound using, for example, the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A recognition unit that recognizes the target animal, A generation unit that generates animal sounds based on the animal recognized by the recognition unit, The system comprises an output unit that outputs the sound generated by the generation unit. A system characterized by the following features. (Note 2) The output unit is, It includes a switching unit that switches to a different audio if the first one is ineffective. The system described in Appendix 1, characterized by the features described herein. (Note 3) Analyze the camera footage, It features a report generation unit that automatically generates a report summarizing daily sighting information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The recognition unit, It operates day and night and recognizes animals according to the seasons and surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Automatically generates intimidating sounds according to the seasons and surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The report generation unit, It collaborates with nearby systems to share sighting information. The system described in Appendix 3, characterized by the features described herein. (Note 7) The recognition unit, It estimates the user's emotions and adjusts the recognition accuracy based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The recognition unit, During recognition, the recognition algorithm is optimized by analyzing the animal's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recognition unit, When recognizing an animal, different recognition methods are applied depending on the type of animal. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recognition unit, When recognizing an animal, the recognition accuracy is improved by taking into account the animal's movement speed. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recognition unit, During recognition, the system analyzes animal sounds to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the type of voice generated based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the generation algorithm is optimized by receiving real-time feedback on the animal's reactions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different voice generation techniques are applied for each type of animal. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the volume of the generated audio based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the generated voice is optimized by referencing past animal response data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the generated voice is customized based on the animal's habitat. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, It estimates the user's emotions and adjusts the timing of the output audio based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, When outputting, the system measures the distance to the animal and outputs sound at the optimal volume. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, When outputting, different output methods are applied for each type of animal. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, It estimates the user's emotions and adjusts the frequency of the output audio based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, When outputting sound, the direction of the output is adjusted to take into account the animal's movement. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, When outputting, the output sound is customized based on the animal's activity time. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned switching unit is It estimates the user's emotions and adjusts the switching timing based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned switching unit is During the switching process, the system analyzes animal response data in real time and switches to the optimal voice. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned switching unit is When switching, different switching methods are applied depending on the type of animal. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned switching unit is It estimates the user's emotions and adjusts the type of voice prompt based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned switching unit is When switching, the system optimizes the switching voice by referencing past animal response data. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned switching unit is When switching between animals, the voice prompts are customized based on the animal's habitat. The system described in Appendix 2, characterized by the features described herein. (Note 31) The report generation unit, It estimates the user's emotions and adjusts the report content based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The report generation unit, When generating reports, we analyze animal appearance patterns to improve the accuracy of the reports. The system described in Appendix 3, characterized by the features described herein. (Note 33) The report generation unit, When generating reports, different report generation methods are applied for each type of animal. The system described in Appendix 3, characterized by the features described herein. (Note 34) The report generation unit, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 35) The report generation unit, When generating reports, the report content is optimized by referencing animal movement data. The system described in Appendix 3, characterized by the features described herein. (Note 36) The report generation unit, When generating a report, customize the report content based on the animal's habitat. The system described in Appendix 3, characterized by the features described herein. (Note 37) The report generation unit, When generating reports, the system collaborates with nearby systems to share sighting information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit that recognizes the target animal, A generation unit that generates animal sounds based on the animal recognized by the recognition unit, The system comprises an output unit that outputs the sound generated by the generation unit. A system characterized by the following features.

2. The output unit is, It includes a switching unit that switches to a different audio if the first one is ineffective. The system according to feature 1.

3. Analyze the camera footage, It features a report generation unit that automatically generates a report summarizing daily sighting information. The system according to feature 1.

4. The recognition unit, It operates day and night and recognizes animals according to the seasons and surrounding environment. The system according to feature 1.

5. The generating unit is Automatically generates intimidating sounds according to the seasons and surrounding environment. The system according to feature 1.

6. The report generation unit, It collaborates with nearby systems to share sighting information. The system according to claim 3.

7. The recognition unit, It estimates the user's emotions and adjusts the recognition accuracy based on the estimated emotions. The system according to feature 1.

8. The recognition unit, During recognition, the recognition algorithm is optimized by analyzing the animal's behavioral patterns. The system according to feature 1.

9. The recognition unit, When recognizing an animal, different recognition methods are applied depending on the type of animal. The system according to feature 1.

10. The recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system according to feature 1.

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