system

Abstract

The system according to this embodiment aims to analyze animal behavior patterns and propose and implement optimal countermeasures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an analysis unit, a proposal unit, and a processing unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit and identifies the animal species. The analysis unit analyzes the animal's behavior pattern based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal countermeasures based on the analysis results obtained by the analysis unit. The processing unit performs post-capture processing based on the countermeasures proposed by the proposal 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that information collection in pest control is insufficient, it is difficult to understand the behavior patterns of animals, and there is a lack of efficient capture and treatment plans.

[0005] The system according to the embodiment aims to analyze the behavior patterns of beasts and propose and implement optimal countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an analysis unit, a proposal unit, and a processing unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit and identifies the animal species. The analysis unit analyzes the animal's behavioral patterns based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal countermeasures based on the analysis results obtained by the analysis unit. The processing unit performs post-capture processing based on the countermeasures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the behavioral patterns of animals and propose and implement optimal countermeasures. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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), etc.

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also 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 behavior analysis system according to an embodiment of the present invention is a system that uses AI technology to analyze the behavior of wild animals and proposes efficient capture and processing plans. To address conventional challenges such as delays in information gathering, difficulty in understanding animal behavior patterns, and a lack of efficient capture and processing plans, this wildlife behavior analysis system has the following configuration. First, in the information gathering and analysis step, a portal system is constructed, providing a platform where residents and farmers can post sighting information. Furthermore, generative AI is used to analyze images (footprints, droppings, animal trails) and identify animal species. Generative AI is also used to analyze comments and deepen understanding. Next, in the animal behavior and countermeasures formulation step, the AI ​​analyzes animal appearance patterns and proposes optimal countermeasures. Specific countermeasures include providing support for concrete decision-making, such as optimizing traps and ambush locations. Finally, in the post-capture processing and coordination step, the system collaborates with buyers and conducts preliminary consultations with meat processing companies depending on the likelihood of capture, thereby achieving rapid processing and reducing waste. For example, the wildlife behavior analysis system provides a platform where residents and farmers can post sighting information. For example, residents and farmers can post images of animal footprints, droppings, and animal trails they have seen. Next, the wildlife behavior analysis system uses generative AI to analyze the posted images and identify the animal species. For example, the generative AI analyzes the shape of footprints and the characteristics of droppings to identify a specific animal species. Furthermore, the wildlife behavior analysis system uses generative AI to analyze the posted comments and understand the background and details of the sightings. For example, the generative AI analyzes the content of the comments to identify the behavioral patterns and locations of the sighted animals. Next, the wildlife behavior analysis system uses AI to analyze the animal's appearance patterns and propose optimal countermeasures. For example, based on past sightings and capture data, the AI ​​analyzes the frequency of animal appearances and movement routes and proposes optimal locations for traps and ambushes. Furthermore, the wildlife behavior analysis system makes preliminary inquiries to meat processors depending on the likelihood of capture. For example, if capture is expected, it collaborates with meat processors in advance to ensure rapid processing. In this way, the wildlife behavior analysis system enables the scientific optimization of local wildlife damage control measures and allows for rapid responses.Furthermore, understanding animal behavior can improve capture efficiency, reduce losses through efficient processing, and contribute to the local economy. This means that wildlife behavior analysis systems can scientifically optimize local wildlife damage control measures and enable rapid responses.

[0029] The wildlife behavior analysis system according to this embodiment comprises a collection unit, an analysis unit, an analysis unit, a proposal unit, and a processing unit. The collection unit collects information. The collection unit provides, for example, a platform where residents and farmers can post sighting information. The collection unit can collect sighting information, for example, through a website or a mobile app. The collection unit can collect, for example, image information, text information, sensor data, etc., as sighting information. The analysis unit analyzes the information collected by the collection unit and identifies the animal species. The analysis unit identifies the animal species by, for example, analyzing images using generative AI. The generative AI can use, for example, technologies such as deep learning or GAN (Generative Opposite Network). The analysis unit identifies specific animal species by, for example, analyzing images such as footprints, droppings, and animal trails. The analysis unit analyzes comments using, for example, generative AI to understand the background and details of the sighting information. The generative AI can use, for example, technologies such as text analysis and sentiment analysis. The analysis unit analyzes the animal's behavior pattern based on the analysis results obtained by the analysis unit. The analysis unit analyzes animal appearance patterns using AI, for example. The AI ​​can utilize technologies such as machine learning and deep learning. The analysis unit analyzes behavioral patterns such as frequency of appearance, time of day, and location. The proposal unit proposes optimal countermeasures based on the analysis results obtained by the analysis unit. The proposal unit proposes optimal countermeasures using AI, for example. The AI ​​can utilize technologies such as machine learning and deep learning. The proposal unit provides concrete decision support, including the optimization of traps and ambush locations. The proposal unit proposes criteria for selecting installation locations and effective placement methods, for example. The processing unit carries out post-capture processing based on the countermeasures proposed by the proposal unit. The processing unit makes preliminary inquiries to meat processors depending on the likelihood of capture, for example. The processing unit proposes post-capture processing methods such as meat processing and disposal methods, for example. Thus, the wildlife behavior analysis system according to this embodiment enables efficient wildlife damage control by consistently performing everything from information gathering and analysis to behavioral pattern analysis, countermeasure proposals, and post-capture processing.

[0030] The collection unit collects information. For example, the collection unit provides a platform where residents and farmers can post sighting information. Specifically, the collection unit can collect sighting information through websites and mobile apps. The website provides an easily accessible interface for users and includes a form for posting sighting information. The form includes fields for entering the date and time of the sighting, location, and detailed information that helps identify the animal species (e.g., animal characteristics, behavior, surrounding environment, etc.). The mobile app uses GPS functionality to automatically record the sighting location and makes it easy for users to post information. The app also uses a camera function to allow users to directly upload images and videos of sighted animals. The collection unit can collect information such as image information, text information, and sensor data as sighting information. Image information includes evidence such as the animal's appearance, footprints, and droppings. Text information includes comments and detailed descriptions from witnesses. Sensor data is obtained from, for example, motion sensors to detect animal movement and temperature and humidity sensors to collect environmental data. This allows the collection unit to collect a wide range of data from diverse sources and gain a detailed understanding of wildlife behavior. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and data processing units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit to identify animal species. For example, the analysis unit uses generative AI to analyze images and identify animal species. Generative AI can utilize technologies such as deep learning and GAN (Generative Opposite Network). Specifically, it trains an image recognition model using deep learning to extract animal features from collected images. This allows for highly accurate identification of animal species and individuals. For example, it can analyze images of footprints or droppings to identify specific animal species. Generative AI can also utilize technologies such as text analysis and sentiment analysis. Text analysis analyzes comments from sighting reports to understand the details of animal behavior and sighting circumstances. Sentiment analysis evaluates the emotions and urgency of witnesses to determine priority for response. This allows the analysis unit to quickly and accurately analyze collected data and understand the behavior and appearances of wild animals. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past sighting data, it can predict animal appearance patterns in specific areas and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Analysis Department analyzes animal behavior patterns based on the analysis results obtained by the Analysis Department. The Analysis Department analyzes animal appearance patterns using, for example, AI. The AI ​​can utilize technologies such as machine learning and deep learning. Specifically, it uses machine learning algorithms to learn animal behavior patterns from collected data and analyze behavior patterns such as frequency of appearance, time of day, and location. For example, it can grasp the tendency of animals to appear in specific seasons and time periods and build predictive models. This allows the Analysis Department to understand animal behavior patterns in detail and provide basic data for formulating effective countermeasures. Furthermore, the Analysis Department can also detect and predict abnormal behavior. For example, it can detect unusual behavior patterns or abnormal appearance frequencies and issue warnings early. In addition, the Analysis Department can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. This allows the Analysis Department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0033] The proposal department proposes optimal countermeasures based on the analysis results obtained by the analysis department. The proposal department proposes optimal countermeasures using, for example, AI. The AI ​​can utilize technologies such as machine learning and deep learning. Specifically, it uses AI to propose optimal countermeasures based on collected data and analysis results. For example, it provides support for specific decisions, including optimizing traps and ambush locations. The AI ​​can learn from past data and successful cases and propose optimal installation locations and effective placement methods. This allows the proposal department to devise efficient and effective countermeasures and minimize damage from wild animals. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it evaluates the results of implementing proposed countermeasures and reflects them in future proposals. In addition, the proposal department can provide multiple countermeasure options and allow users to choose, enabling flexible responses. This allows the proposal department to provide users with optimal countermeasures and minimize damage from wild animals.

[0034] The processing unit carries out post-capture processing based on the measures proposed by the proposal unit. For example, the processing unit will conduct preliminary consultations with meat processors depending on the likelihood of capture. Specifically, if capture is anticipated, it will coordinate with meat processors in advance to prepare for smooth post-capture processing. It will also propose post-capture processing methods, such as meat processing or disposal methods. For example, if the captured animal is usable as meat, it will propose an appropriate processing method and handle the procedures for handing it over to a meat processor. If disposal is necessary, it will propose and implement an appropriate disposal method that is environmentally friendly. In this way, the processing unit can efficiently and appropriately handle post-capture processing, minimizing damage to wild animals. Furthermore, the processing unit can collect post-capture data and use it for future countermeasures. For example, it can record data such as the type and number of captured animals and the location of capture to help plan future countermeasures. The processing unit can also collect feedback on post-capture processing to improve and streamline processing methods. In this way, the processing unit can continuously improve post-capture processing and enhance the reliability and effectiveness of the entire system.

[0035] The data collection unit provides a platform where residents and farmers can post sighting information. The data collection unit can collect sighting information, for example, through a website or mobile app. The data collection unit can collect, for example, image information, text information, sensor data, etc., as sighting information. This improves the accuracy and quantity of information by collecting sighting information from residents and farmers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input a platform for collecting sighting information into a generating AI and have the generating AI design the platform and optimize its functions.

[0036] The analysis unit analyzes images using generative AI to identify animal species. For example, the analysis unit uses generative AI to analyze images of footprints, droppings, animal trails, etc., and identify specific animal species. The generative AI can use technologies such as deep learning or GAN (Generative Opposite Network). This allows for rapid and accurate identification of animal species through image analysis. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis unit can input image data into the generative AI and have the generative AI perform animal species identification.

[0037] The analysis unit analyzes comments using a generative AI to deepen its understanding. For example, the analysis unit uses a generative AI to understand the background and details of the eyewitness information. The generative AI can use techniques such as text analysis and sentiment analysis. This allows for an understanding of the background and details of the eyewitness information through comment analysis. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input comment data into a generative AI and have the generative AI perform the analysis of the eyewitness information.

[0038] The proposal department uses AI to analyze animal appearance patterns and proposes optimal countermeasures. For example, the proposal department uses AI to analyze behavioral patterns such as frequency of appearance, time of day, and location. The AI ​​can utilize technologies such as machine learning and deep learning. This enables effective animal damage control through countermeasure proposals based on the analysis of appearance patterns. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input behavioral pattern data into a generating AI and have the generating AI generate optimal countermeasure proposals.

[0039] The proposal unit provides concrete decision-making support, including the optimization of traps and ambush locations. For example, the proposal unit proposes criteria for selecting installation locations and effective placement methods. This concrete decision-making support improves capture efficiency. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input installation location data into a generating AI and have the generating AI propose optimal installation locations.

[0040] The processing unit conducts preliminary consultations with meat processors depending on the likelihood of capture. For example, if capture is anticipated, the processing unit coordinates with meat processors in advance to ensure rapid processing. This allows for quick processing after capture and reduces waste. Some or all of the above-described processes in the processing unit may be performed using AI, or not. For example, the processing unit can input capture data into a generating AI and have the generating AI perform preliminary consultations with meat processors.

[0041] The data collection unit analyzes the user's past posting history when eyewitness information is submitted and selects the optimal posting method. For example, if the user has previously submitted many images, the data collection unit provides an interface that prioritizes image submissions. For example, if the user has previously submitted many text posts, the data collection unit provides an interface that prioritizes text input. For example, if the user has previously submitted voice posts, the data collection unit provides an interface that prioritizes voice input. This streamlines information collection by selecting the optimal posting method based on the user's past posting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past posting history data into a generating AI and have the generating AI select the optimal posting method.

[0042] The data collection unit filters sighting information based on the user's current lifestyle and areas of interest when the user posts it. For example, if the user is engaged in agriculture, the data collection unit prioritizes collecting sighting information related to crops. For example, if the user owns a pet, the data collection unit prioritizes collecting sighting information related to pets. For example, if the user is interested in nature conservation, the data collection unit prioritizes collecting sighting information related to protected animals. By filtering information based on the user's lifestyle and areas of interest, the data collection unit can collect highly relevant information. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest data into a generating AI and have the generating AI perform the information filtering.

[0043] The data collection unit prioritizes collecting highly relevant information when a user posts a sighting report, taking into account the user's geographical location. For example, if the user is in a specific area, the data collection unit prioritizes collecting sighting reports related to that area. For example, if the user is on the move, the data collection unit prioritizes collecting sighting reports close to the user's current location. For example, if the user has been staying in a specific location for an extended period, the data collection unit prioritizes collecting sighting reports related to that location. This improves the accuracy of the information by collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0044] The data collection unit analyzes the user's social media activity when a sighting report is posted and collects relevant information. For example, if a user frequently posts about a particular animal on social media, the data collection unit prioritizes collecting sighting reports related to that animal. For example, if a user frequently posts about a particular region on social media, the data collection unit prioritizes collecting sighting reports related to that region. For example, if a user frequently posts about a particular activity on social media, the data collection unit prioritizes collecting sighting reports related to that activity. This improves the accuracy of the information by collecting relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant information.

[0045] The analysis unit dynamically selects the features necessary for identifying animal species during image analysis. For example, the analysis unit analyzes the shape of footprints in an image and selects the features necessary for identifying animal species. For example, the analysis unit analyzes the shape and color of feces in an image and selects the features necessary for identifying animal species. For example, the analysis unit analyzes the shape and width of animal trails in an image and selects the features necessary for identifying animal species. By dynamically selecting the features necessary for identifying animal species, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input image data into a generative AI and have the generative AI perform the feature selection.

[0046] The analysis unit improves analysis accuracy by combining different analysis algorithms during image analysis. For example, to analyze footprints in an image, the analysis unit combines a shape analysis algorithm and a pattern recognition algorithm. For example, to analyze feces in an image, the analysis unit combines a color analysis algorithm and a texture analysis algorithm. For example, to analyze animal trails in an image, the analysis unit combines a width analysis algorithm and a shape analysis algorithm. By combining different analysis algorithms, the analysis accuracy is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input image data into a generative AI and have the generative AI execute combinations of different analysis algorithms.

[0047] The analysis unit determines the priority of image analysis based on the time the submitted images were taken. For example, the analysis unit prioritizes the analysis of recently taken images to provide the latest information. For example, the analysis unit prioritizes the analysis of images taken in a specific season to understand seasonal behavioral patterns. For example, the analysis unit prioritizes the analysis of images taken during a specific time period to understand time-based behavioral patterns. By determining the priority of analysis based on the time the submitted images were taken, the latest information can be prioritized. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input image shooting time data into a generative AI and have the generative AI determine the priority of analysis.

[0048] The analysis unit improves the accuracy of image analysis by referring to relevant literature and databases. For example, when analyzing footprints in an image, the analysis unit improves the accuracy by referring to relevant literature. For example, when analyzing feces in an image, the analysis unit improves the accuracy by referring to relevant databases. For example, when analyzing animal trails in an image, the analysis unit improves the accuracy by referring to relevant research results. Thus, the accuracy of the analysis is improved by referring to relevant literature and databases. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input information from literature and databases into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0049] The analysis unit predicts current behavioral patterns by referring to past data when analyzing behavioral patterns. For example, the analysis unit predicts current appearance patterns by referring to past appearance data. For example, the analysis unit predicts current movement patterns by referring to past movement data. For example, the analysis unit predicts current behavioral patterns by referring to past behavioral data. In this way, current behavioral patterns can be accurately predicted by referring to past data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past data into a generative AI and have the generative AI perform the prediction of current behavioral patterns.

[0050] The analysis unit applies different analysis methods to each animal species when analyzing behavioral patterns. For example, the analysis unit applies an analysis method for appearance patterns to a specific animal species. For example, the analysis unit applies an analysis method for movement patterns to a specific animal species. For example, the analysis unit applies an analysis method for behavioral patterns to a specific animal species. By applying the appropriate analysis method to each animal species, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data for each animal species into a generative AI and have the generative AI execute the application of different analysis methods.

[0051] The analysis unit analyzes changes in behavioral patterns based on the timing of sighting reports. For example, the analysis unit analyzes changes in behavioral patterns based on recent sighting reports. For example, the analysis unit analyzes changes in behavioral patterns based on sighting reports posted during a specific season. For example, the analysis unit analyzes changes in behavioral patterns based on sighting reports posted during a specific time period. This allows for an accurate understanding of changes in behavioral patterns by analyzing them based on the timing of sighting reports. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input sighting report posting timing data into a generative AI and have the generative AI perform the analysis of changes in behavioral patterns.

[0052] The analysis unit improves the accuracy of its analysis by referring to relevant environmental data when analyzing behavioral patterns. For example, the analysis unit refers to environmental data to analyze changes in behavioral patterns. For example, the analysis unit refers to weather data to analyze changes in behavioral patterns. For example, the analysis unit refers to geographic data to analyze changes in behavioral patterns. This improves the accuracy of behavioral pattern analysis by referring to relevant environmental data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input environmental data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0053] The proposal unit adjusts the level of detail of countermeasures based on the animal's behavior patterns when proposing countermeasures. For example, if the animal appears frequently, the proposal unit proposes detailed countermeasures. For example, if the animal travels over a wide area, the proposal unit proposes wide-ranging countermeasures. For example, if the animal's behavior is predictable, the proposal unit proposes specific countermeasures. By adjusting the level of detail of countermeasures based on the animal's behavior patterns, it becomes possible to propose effective countermeasures. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can input behavior pattern data into a generative AI and have the generative AI perform the adjustment of the level of detail of the countermeasures.

[0054] The proposal unit improves the accuracy of its proposals by combining different countermeasure algorithms when proposing countermeasures. For example, when proposing trap placement locations, the proposal unit combines an appearance pattern analysis algorithm and a geographic information analysis algorithm. For example, when proposing ambush locations, the proposal unit combines a behavior pattern analysis algorithm and an environmental data analysis algorithm. For example, when proposing capture methods, the proposal unit combines a species identification algorithm and a behavior prediction algorithm. By combining different countermeasure algorithms in this way, the accuracy of the proposals is improved. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input countermeasure algorithm data into a generative AI and have the generative AI perform the improvement of proposal accuracy.

[0055] The proposal unit determines the priority of countermeasures based on the timing of the posting of sighting information when proposing countermeasures. For example, the proposal unit may prioritize countermeasures that are most urgent based on recent sighting information. For example, the proposal unit may prioritize countermeasures for each season based on sighting information posted during a specific season. For example, the proposal unit may prioritize countermeasures for each time of day based on sighting information posted during a specific time of day. By determining the priority of countermeasures based on the timing of the posting of sighting information, it is possible to prioritize the proposal of countermeasures that are most urgent. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal unit can input the data on the posting timing of sighting information into a generation AI and have the generation AI perform the determination of the priority of countermeasures.

[0056] The proposal unit improves the accuracy of its proposals by referring to relevant literature and databases when proposing countermeasures. For example, when proposing trap placement locations, the proposal unit improves the accuracy of its proposals by referring to relevant literature. For example, when proposing ambush locations, the proposal unit improves the accuracy of its proposals by referring to relevant databases. For example, when proposing capture methods, the proposal unit improves the accuracy of its proposals by referring to relevant research results. In this way, the accuracy of proposals is improved by referring to relevant literature and databases. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input information from literature and databases into a generative AI and have the generative AI perform the improvement of proposal accuracy.

[0057] The processing unit selects the optimal processing method by referring to past processing data during post-capture processing. For example, the processing unit selects the optimal processing method by referring to past capture data. For example, the processing unit selects an efficient processing method by referring to past processing data. For example, the processing unit selects a waste-free processing method by referring to past processing results. In this way, the optimal processing method can be selected by referring to past processing data. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the processing unit can input past processing data into a generative AI and have the generative AI select the optimal processing method.

[0058] The processing unit improves processing accuracy by combining different processing methods during post-capture processing. For example, the processing unit may combine a rapid processing method with a detailed processing method as a post-capture processing method. For example, the processing unit may combine an efficient processing method with a waste-free processing method as a post-capture processing method. For example, the processing unit may combine a reliable processing method with a flexible processing method as a post-capture processing method. By combining different processing methods in this way, processing accuracy is improved. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit may input processing method data into a generative AI and have the generative AI perform the improvement of processing accuracy.

[0059] The processing unit adjusts the processing method based on the type of animal captured during post-capture processing. For example, if the captured animal is large, the processing unit proposes a quick and efficient processing method. For example, if the captured animal is small, the processing unit proposes a detailed processing method. For example, if the captured animal is of a specific type, the processing unit proposes a processing method appropriate to that type. This allows for appropriate processing by adjusting the processing method based on the type of animal captured. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the processing unit can input data on the type of animal captured into a generative AI and have the generative AI perform the adjustment of the processing method.

[0060] The processing unit improves processing accuracy by referring to information on relevant businesses and facilities during post-capture processing. For example, the processing unit improves processing accuracy by referring to information on relevant businesses as a post-capture processing method. For example, the processing unit improves processing accuracy by referring to information on relevant facilities as a post-capture processing method. For example, the processing unit improves processing accuracy by referring to relevant databases as a post-capture processing method. This improves processing accuracy by referring to information on relevant businesses and facilities. Some or all of the above processing in the processing unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the processing unit can input information on businesses and facilities into a generating AI and have the generating AI perform the improvement of processing accuracy.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The analysis unit can improve analysis accuracy by combining different analysis algorithms during image analysis. For example, a shape analysis algorithm and a pattern recognition algorithm can be combined to analyze footprints in an image. A color analysis algorithm and a texture analysis algorithm can be combined to analyze feces in an image. A width analysis algorithm and a shape analysis algorithm can be combined to analyze animal trails in an image. In this way, combining different analysis algorithms improves analysis accuracy.

[0063] The proposal department can adjust the level of detail in proposed countermeasures based on the animal's behavior patterns. For example, if the animal appears frequently, detailed countermeasures can be proposed. If the animal travels over a wide area, comprehensive countermeasures can be proposed. If the animal's behavior is predictable, specific countermeasures can be proposed. By adjusting the level of detail in countermeasures based on the animal's behavior patterns, effective countermeasure proposals become possible.

[0064] The processing unit can select the optimal processing method by referring to past processing data during post-capture processing. For example, it can select the optimal processing method by referring to past capture data. It can select an efficient processing method by referring to past processing data. It can select a waste-free processing method by referring to past processing results. In this way, the optimal processing method can be selected by referring to past processing data.

[0065] The data collection unit can prioritize collecting highly relevant information when a user submits a sighting report, taking into account the user's geographical location. For example, if a user is in a specific area, it can prioritize collecting sighting reports related to that area. If a user is on the move, it can prioritize collecting sighting reports close to their current location. If a user has been staying in a specific location for an extended period, it can prioritize collecting sighting reports related to that location. This improves the accuracy of the information by collecting highly relevant information based on the user's geographical location.

[0066] The proposal department can prioritize countermeasures based on when the sighting information was posted. For example, based on recent sightings, it can prioritize proposing countermeasures that are most urgent. Based on sightings posted during a specific season, it can prioritize proposing countermeasures specific to a particular time of day. Based on sightings posted during a specific time of day, it can prioritize proposing countermeasures specific to a particular time of day. By prioritizing countermeasures based on when the sighting information was posted, it is possible to prioritize proposing countermeasures that are most urgent.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects information. The collection unit provides, for example, a platform where residents and farmers can post sighting information. The collection unit can collect sighting information, for example, through websites or mobile apps. The collection unit can collect, for example, image information, text information, sensor data, etc., as sighting information. Step 2: The analysis unit analyzes the information collected by the collection unit to identify the animal species. The analysis unit identifies the animal species by analyzing images, for example, using generative AI. Generative AI can use technologies such as deep learning or GAN (Generative Opposite Network). The analysis unit identifies specific animal species by analyzing images such as footprints, droppings, and animal trails. The analysis unit analyzes comments, for example, using generative AI to understand the background and details of the sighting information. Generative AI can use technologies such as text analysis and sentiment analysis. Step 3: The analysis unit analyzes the animal's behavior patterns based on the analysis results obtained by the analysis unit. The analysis unit analyzes the animal's appearance patterns using, for example, AI. The AI ​​can use technologies such as machine learning and deep learning. The analysis unit analyzes behavior patterns such as frequency of appearance, time of day, and location. Step 4: The proposal department proposes the optimal countermeasures based on the analysis results obtained by the analysis department. The proposal department may, for example, use AI to propose the optimal countermeasures. The AI ​​may use technologies such as machine learning and deep learning. The proposal department provides support for specific decisions, such as optimizing traps and ambush locations. The proposal department may propose criteria for selecting installation locations and effective placement methods. Step 5: The processing unit carries out post-capture processing based on the measures proposed by the proposal unit. For example, the processing unit may contact meat processors in advance depending on the likelihood of capture. For example, the processing unit may propose post-capture processing methods such as meat processing or disposal methods.

[0069] (Example of form 2) The wildlife behavior analysis system according to an embodiment of the present invention is a system that uses AI technology to analyze the behavior of wild animals and proposes efficient capture and processing plans. To address conventional challenges such as delays in information gathering, difficulty in understanding animal behavior patterns, and a lack of efficient capture and processing plans, this wildlife behavior analysis system has the following configuration. First, in the information gathering and analysis step, a portal system is constructed, providing a platform where residents and farmers can post sighting information. Furthermore, generative AI is used to analyze images (footprints, droppings, animal trails) and identify animal species. Generative AI is also used to analyze comments and deepen understanding. Next, in the animal behavior and countermeasures formulation step, the AI ​​analyzes animal appearance patterns and proposes optimal countermeasures. Specific countermeasures include providing support for concrete decision-making, such as optimizing traps and ambush locations. Finally, in the post-capture processing and coordination step, the system collaborates with buyers and conducts preliminary consultations with meat processing companies depending on the likelihood of capture, thereby achieving rapid processing and reducing waste. For example, the wildlife behavior analysis system provides a platform where residents and farmers can post sighting information. For example, residents and farmers can post images of animal footprints, droppings, and animal trails they have seen. Next, the wildlife behavior analysis system uses generative AI to analyze the posted images and identify the animal species. For example, the generative AI analyzes the shape of footprints and the characteristics of droppings to identify a specific animal species. Furthermore, the wildlife behavior analysis system uses generative AI to analyze the posted comments and understand the background and details of the sightings. For example, the generative AI analyzes the content of the comments to identify the behavioral patterns and locations of the sighted animals. Next, the wildlife behavior analysis system uses AI to analyze the animal's appearance patterns and propose optimal countermeasures. For example, based on past sightings and capture data, the AI ​​analyzes the frequency of animal appearances and movement routes and proposes optimal locations for traps and ambushes. Furthermore, the wildlife behavior analysis system makes preliminary inquiries to meat processors depending on the likelihood of capture. For example, if capture is expected, it collaborates with meat processors in advance to ensure rapid processing. In this way, the wildlife behavior analysis system enables the scientific optimization of local wildlife damage control measures and allows for rapid responses.Furthermore, understanding animal behavior can improve capture efficiency, reduce losses through efficient processing, and contribute to the local economy. This means that wildlife behavior analysis systems can scientifically optimize local wildlife damage control measures and enable rapid responses.

[0070] The wildlife behavior analysis system according to this embodiment comprises a collection unit, an analysis unit, an analysis unit, a proposal unit, and a processing unit. The collection unit collects information. The collection unit provides, for example, a platform where residents and farmers can post sighting information. The collection unit can collect sighting information, for example, through a website or a mobile app. The collection unit can collect, for example, image information, text information, sensor data, etc., as sighting information. The analysis unit analyzes the information collected by the collection unit and identifies the animal species. The analysis unit identifies the animal species by, for example, analyzing images using generative AI. The generative AI can use, for example, technologies such as deep learning or GAN (Generative Opposite Network). The analysis unit identifies specific animal species by, for example, analyzing images such as footprints, droppings, and animal trails. The analysis unit analyzes comments using, for example, generative AI to understand the background and details of the sighting information. The generative AI can use, for example, technologies such as text analysis and sentiment analysis. The analysis unit analyzes the animal's behavior pattern based on the analysis results obtained by the analysis unit. The analysis unit analyzes animal appearance patterns using AI, for example. The AI ​​can utilize technologies such as machine learning and deep learning. The analysis unit analyzes behavioral patterns such as frequency of appearance, time of day, and location. The proposal unit proposes optimal countermeasures based on the analysis results obtained by the analysis unit. The proposal unit proposes optimal countermeasures using AI, for example. The AI ​​can utilize technologies such as machine learning and deep learning. The proposal unit provides concrete decision support, including the optimization of traps and ambush locations. The proposal unit proposes criteria for selecting installation locations and effective placement methods, for example. The processing unit carries out post-capture processing based on the countermeasures proposed by the proposal unit. The processing unit makes preliminary inquiries to meat processors depending on the likelihood of capture, for example. The processing unit proposes post-capture processing methods such as meat processing and disposal methods, for example. Thus, the wildlife behavior analysis system according to this embodiment enables efficient wildlife damage control by consistently performing everything from information gathering and analysis to behavioral pattern analysis, countermeasure proposals, and post-capture processing.

[0071] The collection unit collects information. For example, the collection unit provides a platform where residents and farmers can post sighting information. Specifically, the collection unit can collect sighting information through websites and mobile apps. The website provides an easily accessible interface for users and includes a form for posting sighting information. The form includes fields for entering the date and time of the sighting, location, and detailed information that helps identify the animal species (e.g., animal characteristics, behavior, surrounding environment, etc.). The mobile app uses GPS functionality to automatically record the sighting location and makes it easy for users to post information. The app also uses a camera function to allow users to directly upload images and videos of sighted animals. The collection unit can collect information such as image information, text information, and sensor data as sighting information. Image information includes evidence such as the animal's appearance, footprints, and droppings. Text information includes comments and detailed descriptions from witnesses. Sensor data is obtained from, for example, motion sensors to detect animal movement and temperature and humidity sensors to collect environmental data. This allows the collection unit to collect a wide range of data from diverse sources and gain a detailed understanding of wildlife behavior. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and data processing units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis unit analyzes the information collected by the collection unit to identify animal species. For example, the analysis unit uses generative AI to analyze images and identify animal species. Generative AI can utilize technologies such as deep learning and GAN (Generative Opposite Network). Specifically, it trains an image recognition model using deep learning to extract animal features from collected images. This allows for highly accurate identification of animal species and individuals. For example, it can analyze images of footprints or droppings to identify specific animal species. Generative AI can also utilize technologies such as text analysis and sentiment analysis. Text analysis analyzes comments from sighting reports to understand the details of animal behavior and sighting circumstances. Sentiment analysis evaluates the emotions and urgency of witnesses to determine priority for response. This allows the analysis unit to quickly and accurately analyze collected data and understand the behavior and appearances of wild animals. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past sighting data, it can predict animal appearance patterns in specific areas and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0073] The Analysis Department analyzes animal behavior patterns based on the analysis results obtained by the Analysis Department. The Analysis Department analyzes animal appearance patterns using, for example, AI. The AI ​​can utilize technologies such as machine learning and deep learning. Specifically, it uses machine learning algorithms to learn animal behavior patterns from collected data and analyze behavior patterns such as frequency of appearance, time of day, and location. For example, it can grasp the tendency of animals to appear in specific seasons and time periods and build predictive models. This allows the Analysis Department to understand animal behavior patterns in detail and provide basic data for formulating effective countermeasures. Furthermore, the Analysis Department can also detect and predict abnormal behavior. For example, it can detect unusual behavior patterns or abnormal appearance frequencies and issue warnings early. In addition, the Analysis Department can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. This allows the Analysis Department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0074] The proposal department proposes optimal countermeasures based on the analysis results obtained by the analysis department. The proposal department proposes optimal countermeasures using, for example, AI. The AI ​​can utilize technologies such as machine learning and deep learning. Specifically, it uses AI to propose optimal countermeasures based on collected data and analysis results. For example, it provides support for specific decisions, including optimizing traps and ambush locations. The AI ​​can learn from past data and successful cases and propose optimal installation locations and effective placement methods. This allows the proposal department to devise efficient and effective countermeasures and minimize damage from wild animals. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it evaluates the results of implementing proposed countermeasures and reflects them in future proposals. In addition, the proposal department can provide multiple countermeasure options and allow users to choose, enabling flexible responses. This allows the proposal department to provide users with optimal countermeasures and minimize damage from wild animals.

[0075] The processing unit carries out post-capture processing based on the measures proposed by the proposal unit. For example, the processing unit will conduct preliminary consultations with meat processors depending on the likelihood of capture. Specifically, if capture is anticipated, it will coordinate with meat processors in advance to prepare for smooth post-capture processing. It will also propose post-capture processing methods, such as meat processing or disposal methods. For example, if the captured animal is usable as meat, it will propose an appropriate processing method and handle the procedures for handing it over to a meat processor. If disposal is necessary, it will propose and implement an appropriate disposal method that is environmentally friendly. In this way, the processing unit can efficiently and appropriately handle post-capture processing, minimizing damage to wild animals. Furthermore, the processing unit can collect post-capture data and use it for future countermeasures. For example, it can record data such as the type and number of captured animals and the location of capture to help plan future countermeasures. The processing unit can also collect feedback on post-capture processing to improve and streamline processing methods. In this way, the processing unit can continuously improve post-capture processing and enhance the reliability and effectiveness of the entire system.

[0076] The data collection unit provides a platform where residents and farmers can post sighting information. The data collection unit can collect sighting information, for example, through a website or mobile app. The data collection unit can collect, for example, image information, text information, sensor data, etc., as sighting information. This improves the accuracy and quantity of information by collecting sighting information from residents and farmers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input a platform for collecting sighting information into a generating AI and have the generating AI design the platform and optimize its functions.

[0077] The analysis unit analyzes images using generative AI to identify animal species. For example, the analysis unit uses generative AI to analyze images of footprints, droppings, animal trails, etc., and identify specific animal species. The generative AI can use technologies such as deep learning or GAN (Generative Opposite Network). This allows for rapid and accurate identification of animal species through image analysis. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis unit can input image data into the generative AI and have the generative AI perform animal species identification.

[0078] The analysis unit analyzes comments using a generative AI to deepen its understanding. For example, the analysis unit uses a generative AI to understand the background and details of the eyewitness information. The generative AI can use techniques such as text analysis and sentiment analysis. This allows for an understanding of the background and details of the eyewitness information through comment analysis. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input comment data into a generative AI and have the generative AI perform the analysis of the eyewitness information.

[0079] The proposal department uses AI to analyze animal appearance patterns and proposes optimal countermeasures. For example, the proposal department uses AI to analyze behavioral patterns such as frequency of appearance, time of day, and location. The AI ​​can utilize technologies such as machine learning and deep learning. This enables effective animal damage control through countermeasure proposals based on the analysis of appearance patterns. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input behavioral pattern data into a generating AI and have the generating AI generate optimal countermeasure proposals.

[0080] The proposal unit provides concrete decision-making support, including the optimization of traps and ambush locations. For example, the proposal unit proposes criteria for selecting installation locations and effective placement methods. This concrete decision-making support improves capture efficiency. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input installation location data into a generating AI and have the generating AI propose optimal installation locations.

[0081] The processing unit conducts preliminary consultations with meat processors depending on the likelihood of capture. For example, if capture is anticipated, the processing unit coordinates with meat processors in advance to ensure rapid processing. This allows for quick processing after capture and reduces waste. Some or all of the above-described processes in the processing unit may be performed using AI, or not. For example, the processing unit can input capture data into a generating AI and have the generating AI perform preliminary consultations with meat processors.

[0082] The data collection unit estimates the user's emotions and adjusts the timing of sighting reports based on the estimated emotions. For example, if the user is excited, the data collection unit sends a notification to prompt them to post the sighting immediately. For example, if the user is relaxed, the data collection unit sends a reminder to encourage them to post the sighting. For example, if the user is busy, the data collection unit provides a temporary save function so they can post later. This makes information collection more efficient because sighting reports can be posted at a time that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of sighting reports.

[0083] The data collection unit analyzes the user's past posting history when eyewitness information is submitted and selects the optimal posting method. For example, if the user has previously submitted many images, the data collection unit provides an interface that prioritizes image submissions. For example, if the user has previously submitted many text posts, the data collection unit provides an interface that prioritizes text input. For example, if the user has previously submitted voice posts, the data collection unit provides an interface that prioritizes voice input. This streamlines information collection by selecting the optimal posting method based on the user's past posting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past posting history data into a generating AI and have the generating AI select the optimal posting method.

[0084] The data collection unit filters sighting information based on the user's current lifestyle and areas of interest when the user posts it. For example, if the user is engaged in agriculture, the data collection unit prioritizes collecting sighting information related to crops. For example, if the user owns a pet, the data collection unit prioritizes collecting sighting information related to pets. For example, if the user is interested in nature conservation, the data collection unit prioritizes collecting sighting information related to protected animals. By filtering information based on the user's lifestyle and areas of interest, the data collection unit can collect highly relevant information. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest data into a generating AI and have the generating AI perform the information filtering.

[0085] The data collection unit estimates the user's emotions and prioritizes the submitted sighting information based on the estimated emotions. For example, if the user is tense, the data collection unit prioritizes urgent sighting information. If the user is relaxed, the data collection unit prioritizes detailed sighting information. If the user is excited, the data collection unit prioritizes sighting information requiring immediate attention. This allows for the priority processing of important information by prioritizing sighting information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of sighting information.

[0086] The data collection unit prioritizes collecting highly relevant information when a user posts a sighting report, taking into account the user's geographical location. For example, if the user is in a specific area, the data collection unit prioritizes collecting sighting reports related to that area. For example, if the user is on the move, the data collection unit prioritizes collecting sighting reports close to the user's current location. For example, if the user has been staying in a specific location for an extended period, the data collection unit prioritizes collecting sighting reports related to that location. This improves the accuracy of the information by collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0087] The data collection unit analyzes the user's social media activity when a sighting report is posted and collects relevant information. For example, if a user frequently posts about a particular animal on social media, the data collection unit prioritizes collecting sighting reports related to that animal. For example, if a user frequently posts about a particular region on social media, the data collection unit prioritizes collecting sighting reports related to that region. For example, if a user frequently posts about a particular activity on social media, the data collection unit prioritizes collecting sighting reports related to that activity. This improves the accuracy of the information by collecting relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant information.

[0088] The analysis unit estimates the user's emotions and adjusts the accuracy of the image analysis based on the estimated emotions. For example, if the user is tense, the analysis unit performs highly accurate image analysis to provide reliable results. For example, if the user is relaxed, the analysis unit performs rapid image analysis to provide results quickly. For example, if the user is excited, the analysis unit performs detailed image analysis to provide rich information. This improves the reliability of the analysis results by adjusting the accuracy of the image analysis according to 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the accuracy of the image analysis.

[0089] The analysis unit dynamically selects the features necessary for identifying animal species during image analysis. For example, the analysis unit analyzes the shape of footprints in an image and selects the features necessary for identifying animal species. For example, the analysis unit analyzes the shape and color of feces in an image and selects the features necessary for identifying animal species. For example, the analysis unit analyzes the shape and width of animal trails in an image and selects the features necessary for identifying animal species. By dynamically selecting the features necessary for identifying animal species, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input image data into a generative AI and have the generative AI perform the feature selection.

[0090] The analysis unit improves analysis accuracy by combining different analysis algorithms during image analysis. For example, to analyze footprints in an image, the analysis unit combines a shape analysis algorithm and a pattern recognition algorithm. For example, to analyze feces in an image, the analysis unit combines a color analysis algorithm and a texture analysis algorithm. For example, to analyze animal trails in an image, the analysis unit combines a width analysis algorithm and a shape analysis algorithm. By combining different analysis algorithms, the analysis accuracy is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input image data into a generative AI and have the generative AI execute combinations of different analysis algorithms.

[0091] The analysis unit estimates the user's emotions and determines the priority of image analysis based on the estimated emotions. For example, if the user is tense, the analysis unit prioritizes analyzing images of high urgency. For example, if the user is relaxed, the analysis unit prioritizes analyzing images containing detailed information. For example, if the user is excited, the analysis unit prioritizes analyzing images that require immediate attention. This allows for the priority of analyzing important information by determining the priority of image analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of image analysis.

[0092] The analysis unit determines the priority of image analysis based on the time the submitted images were taken. For example, the analysis unit prioritizes the analysis of recently taken images to provide the latest information. For example, the analysis unit prioritizes the analysis of images taken in a specific season to understand seasonal behavioral patterns. For example, the analysis unit prioritizes the analysis of images taken during a specific time period to understand time-based behavioral patterns. By determining the priority of analysis based on the time the submitted images were taken, the latest information can be prioritized. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input image shooting time data into a generative AI and have the generative AI determine the priority of analysis.

[0093] The analysis unit improves the accuracy of image analysis by referring to relevant literature and databases. For example, when analyzing footprints in an image, the analysis unit improves the accuracy by referring to relevant literature. For example, when analyzing feces in an image, the analysis unit improves the accuracy by referring to relevant databases. For example, when analyzing animal trails in an image, the analysis unit improves the accuracy by referring to relevant research results. Thus, the accuracy of the analysis is improved by referring to relevant literature and databases. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input information from literature and databases into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0094] The analysis unit estimates the user's emotions and adjusts the behavioral pattern analysis method based on the estimated user emotions. For example, if the user is tense, the analysis unit performs a detailed behavioral pattern analysis and provides reliable results. For example, if the user is relaxed, the analysis unit performs a rapid behavioral pattern analysis and provides results quickly. For example, if the user is excited, the analysis unit performs a detailed behavioral pattern analysis and provides rich information. This improves the reliability of the analysis results by adjusting the behavioral pattern analysis method according to 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the behavioral pattern analysis method.

[0095] The analysis unit predicts current behavioral patterns by referring to past data when analyzing behavioral patterns. For example, the analysis unit predicts current appearance patterns by referring to past appearance data. For example, the analysis unit predicts current movement patterns by referring to past movement data. For example, the analysis unit predicts current behavioral patterns by referring to past behavioral data. In this way, current behavioral patterns can be accurately predicted by referring to past data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past data into a generative AI and have the generative AI perform the prediction of current behavioral patterns.

[0096] The analysis unit applies different analysis methods to each animal species when analyzing behavioral patterns. For example, the analysis unit applies an analysis method for appearance patterns to a specific animal species. For example, the analysis unit applies an analysis method for movement patterns to a specific animal species. For example, the analysis unit applies an analysis method for behavioral patterns to a specific animal species. By applying the appropriate analysis method to each animal species, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data for each animal species into a generative AI and have the generative AI execute the application of different analysis methods.

[0097] The analysis unit estimates the user's emotions and adjusts the importance of behavioral patterns based on the estimated emotions. For example, if the user is tense, the analysis unit prioritizes analyzing urgent behavioral patterns. If the user is relaxed, the analysis unit prioritizes analyzing detailed behavioral patterns. If the user is excited, the analysis unit prioritizes analyzing behavioral patterns that require immediate attention. This allows for the prioritization of important behavioral patterns by adjusting their importance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the importance of behavioral patterns.

[0098] The analysis unit analyzes changes in behavioral patterns based on the timing of sighting reports. For example, the analysis unit analyzes changes in behavioral patterns based on recent sighting reports. For example, the analysis unit analyzes changes in behavioral patterns based on sighting reports posted during a specific season. For example, the analysis unit analyzes changes in behavioral patterns based on sighting reports posted during a specific time period. This allows for an accurate understanding of changes in behavioral patterns by analyzing them based on the timing of sighting reports. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input sighting report posting timing data into a generative AI and have the generative AI perform the analysis of changes in behavioral patterns.

[0099] The analysis unit improves the accuracy of its analysis by referring to relevant environmental data when analyzing behavioral patterns. For example, the analysis unit refers to environmental data to analyze changes in behavioral patterns. For example, the analysis unit refers to weather data to analyze changes in behavioral patterns. For example, the analysis unit refers to geographic data to analyze changes in behavioral patterns. This improves the accuracy of behavioral pattern analysis by referring to relevant environmental data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input environmental data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0100] The suggestion unit estimates the user's emotions and adjusts the presentation of the suggested solutions based on the estimated emotions. For example, if the user is tense, the suggestion unit provides simple and highly visual solutions. If the user is relaxed, the suggestion unit provides solutions that include detailed information. If the user is excited, the suggestion unit provides solutions that are visually stimulating. This improves the acceptability of the suggestions by presenting them in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the suggested solutions.

[0101] The proposal unit adjusts the level of detail of countermeasures based on the animal's behavior patterns when proposing countermeasures. For example, if the animal appears frequently, the proposal unit proposes detailed countermeasures. For example, if the animal travels over a wide area, the proposal unit proposes wide-ranging countermeasures. For example, if the animal's behavior is predictable, the proposal unit proposes specific countermeasures. By adjusting the level of detail of countermeasures based on the animal's behavior patterns, it becomes possible to propose effective countermeasures. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can input behavior pattern data into a generative AI and have the generative AI perform the adjustment of the level of detail of the countermeasures.

[0102] The proposal unit improves the accuracy of its proposals by combining different countermeasure algorithms when proposing countermeasures. For example, when proposing trap placement locations, the proposal unit combines an appearance pattern analysis algorithm and a geographic information analysis algorithm. For example, when proposing ambush locations, the proposal unit combines a behavior pattern analysis algorithm and an environmental data analysis algorithm. For example, when proposing capture methods, the proposal unit combines a species identification algorithm and a behavior prediction algorithm. By combining different countermeasure algorithms in this way, the accuracy of the proposals is improved. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input countermeasure algorithm data into a generative AI and have the generative AI perform the improvement of proposal accuracy.

[0103] The suggestion unit estimates the user's emotions and determines the priority of suggested solutions based on the estimated emotions. For example, if the user is tense, the suggestion unit will prioritize suggesting urgent solutions. If the user is relaxed, the suggestion unit will prioritize suggesting detailed solutions. If the user is excited, the suggestion unit will prioritize suggesting solutions that require immediate attention. This allows for the prioritization of important solutions by determining the priority of suggested solutions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggested solutions.

[0104] The proposal unit determines the priority of countermeasures based on the timing of the posting of sighting information when proposing countermeasures. For example, the proposal unit may prioritize countermeasures that are most urgent based on recent sighting information. For example, the proposal unit may prioritize countermeasures for each season based on sighting information posted during a specific season. For example, the proposal unit may prioritize countermeasures for each time of day based on sighting information posted during a specific time of day. By determining the priority of countermeasures based on the timing of the posting of sighting information, it is possible to prioritize the proposal of countermeasures that are most urgent. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal unit can input the data on the posting timing of sighting information into a generation AI and have the generation AI perform the determination of the priority of countermeasures.

[0105] The proposal unit improves the accuracy of its proposals by referring to relevant literature and databases when proposing countermeasures. For example, when proposing trap placement locations, the proposal unit improves the accuracy of its proposals by referring to relevant literature. For example, when proposing ambush locations, the proposal unit improves the accuracy of its proposals by referring to relevant databases. For example, when proposing capture methods, the proposal unit improves the accuracy of its proposals by referring to relevant research results. In this way, the accuracy of proposals is improved by referring to relevant literature and databases. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input information from literature and databases into a generative AI and have the generative AI perform the improvement of proposal accuracy.

[0106] The processing unit estimates the user's emotions and adjusts the post-capture processing method based on the estimated emotions. For example, if the user is tense, the processing unit suggests a quick and reliable processing method. For example, if the user is relaxed, the processing unit suggests a detailed processing method. For example, if the user is excited, the processing unit suggests a processing method that requires immediate attention. This allows for appropriate processing by adjusting the post-capture processing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing described above in the processing unit may be performed using AI or not using AI. For example, the processing unit can input user emotion data into a generative AI and have the generative AI adjust the post-capture processing method.

[0107] The processing unit selects the optimal processing method by referring to past processing data during post-capture processing. For example, the processing unit selects the optimal processing method by referring to past capture data. For example, the processing unit selects an efficient processing method by referring to past processing data. For example, the processing unit selects a waste-free processing method by referring to past processing results. In this way, the optimal processing method can be selected by referring to past processing data. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the processing unit can input past processing data into a generative AI and have the generative AI select the optimal processing method.

[0108] The processing unit improves processing accuracy by combining different processing methods during post-capture processing. For example, the processing unit may combine a rapid processing method with a detailed processing method as a post-capture processing method. For example, the processing unit may combine an efficient processing method with a waste-free processing method as a post-capture processing method. For example, the processing unit may combine a reliable processing method with a flexible processing method as a post-capture processing method. By combining different processing methods in this way, processing accuracy is improved. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit may input processing method data into a generative AI and have the generative AI perform the improvement of processing accuracy.

[0109] The processing unit estimates the user's emotions and determines the priority of post-capture processing based on the estimated emotions. For example, if the user is tense, the processing unit prioritizes urgent processing. For example, if the user is relaxed, the processing unit prioritizes detailed processing. For example, if the user is excited, the processing unit prioritizes processing that requires immediate attention. This allows important processing to be prioritized by determining the priority of post-capture processing according to 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 processing described above in the processing unit may be performed using AI or not using AI. For example, the processing unit can input user emotion data into a generative AI and have the generative AI determine the priority of post-capture processing.

[0110] The processing unit adjusts the processing method based on the type of animal captured during post-capture processing. For example, if the captured animal is large, the processing unit proposes a quick and efficient processing method. For example, if the captured animal is small, the processing unit proposes a detailed processing method. For example, if the captured animal is of a specific type, the processing unit proposes a processing method appropriate to that type. This allows for appropriate processing by adjusting the processing method based on the type of animal captured. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the processing unit can input data on the type of animal captured into a generative AI and have the generative AI perform the adjustment of the processing method.

[0111] The processing unit improves processing accuracy by referring to information on relevant businesses and facilities during post-capture processing. For example, the processing unit improves processing accuracy by referring to information on relevant businesses as a post-capture processing method. For example, the processing unit improves processing accuracy by referring to information on relevant facilities as a post-capture processing method. For example, the processing unit improves processing accuracy by referring to relevant databases as a post-capture processing method. This improves processing accuracy by referring to information on relevant businesses and facilities. Some or all of the above processing in the processing unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the processing unit can input information on businesses and facilities into a generating AI and have the generating AI perform the improvement of processing accuracy.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The data collection unit can estimate the user's emotions and customize the eyewitness account posting interface based on those emotions. For example, if the user is excited, a simple and intuitive interface can be provided to encourage quick posting. If the user is relaxed, an interface that allows for detailed information input can be provided to encourage more accurate information gathering. If the user is nervous, a guided interface can be provided to allow them to post information with confidence. By providing an interface that responds to the user's emotions, the efficiency of information gathering is improved.

[0114] The analysis unit can improve analysis accuracy by combining different analysis algorithms during image analysis. For example, a shape analysis algorithm and a pattern recognition algorithm can be combined to analyze footprints in an image. A color analysis algorithm and a texture analysis algorithm can be combined to analyze feces in an image. A width analysis algorithm and a shape analysis algorithm can be combined to analyze animal trails in an image. In this way, combining different analysis algorithms improves analysis accuracy.

[0115] The analysis unit can estimate the user's emotions and adjust the behavioral pattern analysis method based on the estimated emotions. For example, if the user is nervous, a detailed behavioral pattern analysis can be performed to provide reliable results. If the user is relaxed, a rapid behavioral pattern analysis can be performed to provide results quickly. If the user is excited, a detailed behavioral pattern analysis can be performed to provide rich information. In this way, the reliability of the analysis results is improved by adjusting the behavioral pattern analysis method according to the user's emotions.

[0116] The proposal department can adjust the level of detail in proposed countermeasures based on the animal's behavior patterns. For example, if the animal appears frequently, detailed countermeasures can be proposed. If the animal travels over a wide area, comprehensive countermeasures can be proposed. If the animal's behavior is predictable, specific countermeasures can be proposed. By adjusting the level of detail in countermeasures based on the animal's behavior patterns, effective countermeasure proposals become possible.

[0117] The proposal function can estimate the user's emotions and adjust the presentation of suggested solutions based on those emotions. For example, if the user is tense, it can present simple and highly visible solutions. If the user is relaxed, it can present solutions that include detailed information. If the user is excited, it can present solutions that are visually stimulating. By presenting solutions in a way that matches the user's emotions, the likelihood of the suggestions being accepted is increased.

[0118] The processing unit can select the optimal processing method by referring to past processing data during post-capture processing. For example, it can select the optimal processing method by referring to past capture data. It can select an efficient processing method by referring to past processing data. It can select a waste-free processing method by referring to past processing results. In this way, the optimal processing method can be selected by referring to past processing data.

[0119] The processing unit can estimate the user's emotions and adjust the post-capture processing method based on those emotions. For example, if the user is tense, it can suggest a quick and reliable processing method. If the user is relaxed, it can suggest a detailed processing method. If the user is excited, it can suggest a processing method that requires immediate attention. This allows for appropriate processing by adjusting the post-capture processing method according to the user's emotions.

[0120] The data collection unit can prioritize collecting highly relevant information when a user submits a sighting report, taking into account the user's geographical location. For example, if a user is in a specific area, it can prioritize collecting sighting reports related to that area. If a user is on the move, it can prioritize collecting sighting reports close to their current location. If a user has been staying in a specific location for an extended period, it can prioritize collecting sighting reports related to that location. This improves the accuracy of the information by collecting highly relevant information based on the user's geographical location.

[0121] The analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated emotions. For example, if the user is nervous, it can perform highly accurate image analysis and provide reliable results. If the user is relaxed, it can perform rapid image analysis and provide results quickly. If the user is excited, it can perform detailed image analysis and provide rich information. In this way, the reliability of the analysis results is improved by adjusting the accuracy of the image analysis according to the user's emotions.

[0122] The proposal department can prioritize countermeasures based on when the sighting information was posted. For example, based on recent sightings, it can prioritize proposing countermeasures that are most urgent. Based on sightings posted during a specific season, it can prioritize proposing countermeasures specific to a particular time of day. Based on sightings posted during a specific time of day, it can prioritize proposing countermeasures specific to a particular time of day. By prioritizing countermeasures based on when the sighting information was posted, it is possible to prioritize proposing countermeasures that are most urgent.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The collection unit collects information. The collection unit provides, for example, a platform where residents and farmers can post sighting information. The collection unit can collect sighting information, for example, through websites or mobile apps. The collection unit can collect, for example, image information, text information, sensor data, etc., as sighting information. Step 2: The analysis unit analyzes the information collected by the collection unit to identify the animal species. The analysis unit identifies the animal species by analyzing images, for example, using generative AI. Generative AI can use technologies such as deep learning or GAN (Generative Opposite Network). The analysis unit identifies specific animal species by analyzing images such as footprints, droppings, and animal trails. The analysis unit analyzes comments, for example, using generative AI to understand the background and details of the sighting information. Generative AI can use technologies such as text analysis and sentiment analysis. Step 3: The analysis unit analyzes the animal's behavior patterns based on the analysis results obtained by the analysis unit. The analysis unit analyzes the animal's appearance patterns using, for example, AI. The AI ​​can use technologies such as machine learning and deep learning. The analysis unit analyzes behavior patterns such as frequency of appearance, time of day, and location. Step 4: The proposal department proposes the optimal countermeasures based on the analysis results obtained by the analysis department. The proposal department may, for example, use AI to propose the optimal countermeasures. The AI ​​may use technologies such as machine learning and deep learning. The proposal department provides support for specific decisions, such as optimizing traps and ambush locations. The proposal department may propose criteria for selecting installation locations and effective placement methods. Step 5: The processing unit carries out post-capture processing based on the measures proposed by the proposal unit. For example, the processing unit may contact meat processors in advance depending on the likelihood of capture. For example, the processing unit may propose post-capture processing methods such as meat processing or disposal methods.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, analysis unit, proposal unit, and processing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and provides a platform where residents and farmers can post sighting information. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generating AI to analyze images and comments and identify animal species. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the behavior patterns of animals. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal countermeasures. The processing unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs post-capture processing. 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.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, analysis unit, proposal unit, and processing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and provides a platform where residents and farmers can post sighting information. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generating AI to analyze images and comments and identify animal species. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the behavior patterns of animals. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal countermeasures. The processing unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs post-capture processing. 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.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, interpretation unit, proposal unit, and processing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and provides a platform where residents and farmers can post sighting information. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generating AI to analyze images and comments and identify the animal species. The interpretation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the animal's behavior patterns. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal countermeasures. The processing unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs post-capture processing. 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.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, analysis unit, proposal unit, and processing unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and provides a platform where residents and farmers can post sighting information. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generating AI to analyze images and comments and identify the animal species. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the animal's behavior patterns. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal countermeasures. The processing unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs post-capture processing. 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit to identify the animal species, An analysis unit analyzes the behavioral patterns of animals based on the analysis results obtained by the aforementioned analysis unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal countermeasures, The system includes a processing unit that performs post-capture processing based on the countermeasures proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The platform allows residents and farmers to post eyewitness accounts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We use generative AI to analyze images and identify animal species. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We use generative AI to analyze comments and deepen our understanding. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Using AI, we analyze animal sighting patterns and propose optimal countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We provide support for specific decisions, including optimizing traps and ambush locations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned processing unit, Depending on the likelihood of capture, we will consult with meat processing companies in advance. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of eyewitness reports based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When a user posts a sighting report, the system analyzes their past posting history to select the most suitable posting method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When users submit eyewitness accounts, the system filters them based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates user sentiment and prioritizes submitted sighting reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When users submit eyewitness accounts, the system prioritizes collecting highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When a user posts a sighting report, we analyze their social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of image analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During image analysis, the necessary features for identifying animal species are dynamically selected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, By combining different analysis algorithms during image analysis, we can improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and determines the priority of image analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During image analysis, the priority of analysis is determined based on when the submitted images were taken. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When performing image analysis, we improve the accuracy of the analysis by referring to relevant literature and databases. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of behavioral patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When analyzing behavioral patterns, we refer to past data to predict current behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is When analyzing behavioral patterns, different analytical methods are applied to each animal species. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is It estimates the user's emotions and adjusts the importance of behavioral patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is When analyzing behavioral patterns, we analyze changes in behavioral patterns based on the timing of the posting of sighting reports. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is When analyzing behavioral patterns, referencing relevant environmental data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way suggested solutions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When proposing countermeasures, adjust the level of detail based on the animal's behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When proposing countermeasures, combine different countermeasure algorithms to improve the accuracy of the proposals. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, The system estimates user sentiment and prioritizes suggested actions based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When proposing countermeasures, prioritize those measures based on when the eyewitness reports were posted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When proposing countermeasures, we improve the accuracy of the proposals by referring to relevant literature and databases. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned processing unit, The system estimates the user's emotions and adjusts the post-capture processing method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned processing unit, During post-capture processing, the optimal processing method is selected by referring to past processing data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned processing unit, By combining different processing methods during post-capture processing, we can improve processing accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned processing unit, The system estimates the user's emotions and determines the priority of post-capture processing based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned processing unit, During post-capture processing, the processing method is adjusted based on the type of animal captured. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned processing unit, To improve processing accuracy after capture, we refer to information from relevant companies and facilities. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 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. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit to identify the animal species, An analysis unit analyzes the behavioral patterns of animals based on the analysis results obtained by the aforementioned analysis unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal countermeasures, The system includes a processing unit that performs post-capture processing based on the countermeasures proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is The platform allows residents and farmers to post eyewitness accounts. The system according to feature 1.

3. The aforementioned analysis unit, We use generative AI to analyze images and identify animal species. The system according to feature 1.

4. The aforementioned analysis unit, We use generative AI to analyze comments and deepen our understanding. The system according to feature 1.

5. The aforementioned proposal section is, Using AI, we analyze animal sighting patterns and propose optimal countermeasures. The system according to feature 1.

6. The aforementioned proposal section is, We provide support for specific decisions, including optimizing traps and ambush locations. The system according to feature 1.

7. The aforementioned processing unit, Depending on the likelihood of capture, we will consult with meat processing companies in advance. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of eyewitness reports based on those emotions. The system according to feature 1.

9. The aforementioned collection unit is When a user posts a sighting report, the system analyzes their past posting history to select the most suitable posting method. The system according to feature 1.

10. The aforementioned collection unit is When users submit eyewitness accounts, the system filters them based on their current lifestyle and areas of interest. The system according to feature 1.

Images